WO2022134596A1 - Active power distribution network vulnerable node identification method which considers new energy impact - Google Patents

Active power distribution network vulnerable node identification method which considers new energy impact Download PDF

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WO2022134596A1
WO2022134596A1 PCT/CN2021/109973 CN2021109973W WO2022134596A1 WO 2022134596 A1 WO2022134596 A1 WO 2022134596A1 CN 2021109973 W CN2021109973 W CN 2021109973W WO 2022134596 A1 WO2022134596 A1 WO 2022134596A1
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node
network
matrix
vulnerability index
nodes
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French (fr)
Chinese (zh)
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窦春霞
胡亮
岳东
张智俊
丁孝华
李延满
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南京邮电大学
国网电力科学研究院有限公司
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Publication of WO2022134596A1 publication Critical patent/WO2022134596A1/en

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

Definitions

  • the invention relates to an active distribution network vulnerable node identification method considering the impact of new energy, and belongs to the technical field of active distribution network vulnerable node identification.
  • the penetration rate of new energy sources such as wind power and solar energy in the distribution network is increasing day by day.
  • the unique randomness and volatility of new energy power sources when the new energy power generation output fluctuates, the power flow of the distribution network will also change, and the identification of vulnerable nodes in the power grid often depends on the power flow results. In the case of entering the distribution network, it is particularly urgent and important to accurately and quickly identify the vulnerable nodes in the power grid.
  • the technical problem to be solved by the present invention is to overcome the defects of the prior art, and to provide a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy, so as to achieve accurate and fast detection when the new energy is connected to the distribution network. Identify vulnerable nodes in the power grid to ensure the safe and stable operation of the active distribution network.
  • the present invention provides a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy
  • the data in the arrangement matrix is input into the pre-built node vulnerability index evaluation system model, and the node comprehensive vulnerability index is obtained;
  • the pre-built node vulnerability index evaluation system model is a comprehensive consideration of the inherent topology structure of the active distribution network and the DG The uncertainty of the node, taking into account the probability of node failure and the calculation model of the impact of the network topology and power flow changes on the system after it is out of operation;
  • Vulnerable nodes are identified by the three-parameter interval number sorting method based on Boolean matrix combined with node comprehensive vulnerability index.
  • the pre-built node vulnerability index evaluation system model includes: a structural vulnerability index calculation model, a state vulnerability index calculation model, and an index weight calculation model;
  • the structural vulnerability index calculation model is used to calculate network cohesion, network efficiency change rate and node electrical betweenness
  • the state vulnerability index calculation model is used to calculate the improved power flow shock entropy and the improved minimum singular value change rate
  • the index weight calculation model is used to calculate the weights of network cohesion, network efficiency change rate, node electrical betweenness, improved power flow impulse entropy and improved minimum singular value change rate.
  • a(k) is the average shortest electrical distance in the network after the node k is contracted by the node contraction method
  • n′ is the number of nodes in the network after the contraction
  • d ij is the shortest electrical distance between any two nodes i and j in the contracted network, and V represents the set of all nodes in the network;
  • the network performance change rate is obtained by the following formula:
  • c(k) is the change rate of the network efficiency before and after the failure of node k
  • C(k) is the network efficiency after the failure of node k
  • C is the network energy efficiency
  • G and D are the generator and load node sets, respectively, N G and N D are the number of generator and load nodes, respectively, min(P Gi′ , P Dj′ ) is the generator load node pair (i′ ) ,j′) the smaller value of the active power, P Gi′ is the active power of the generator node i′, P Di′ is the active power of the load node j′;
  • e(k) is the electrical betweenness of node k
  • the load flow capacity on branch l E(k) is the unit power injected into the generator load node pair (i', j') is the power flow variation of node k at time
  • ⁇ k is the set of lines directly connected to node k.
  • l(k) is the improved power flow shock entropy
  • f(k) is the node failure risk factor
  • g(k) is the power flow shock entropy of node k
  • ⁇ is the risk weighting coefficient
  • F(k) is the probability that the node k voltage exceeds the limit
  • N (k) is the number of times the node k voltage exceeds the lower limit
  • N is the total number of sampling times
  • G j (k) is the impact rate of power flow to line j after node k is out of operation
  • M is the total number of branches in the system
  • ⁇ P j (k) is the power flow impact on line j after node k is out of operation due to a fault
  • o(k) is the improved minimum singular value change rate
  • h(k) is the minimum singular value change rate
  • the network cohesion of each node, the rate of change of network efficiency, the electrical betweenness of the node, and the improved power flow shock entropy are input into the stacked autoencoder neural network to determine the weight of each index.
  • the process of identifying vulnerable nodes using the three-parameter interval number sorting method based on Boolean matrix combined with the comprehensive vulnerability index of each node includes:
  • the specific distribution of the comprehensive vulnerability index of each node is obtained according to the value of the comprehensive vulnerability index of each node, and the upper limit, expected value and lower limit of the comprehensive vulnerability index of each node are calculated according to the equal probability criterion.
  • the lower limit value and the expected value together constitute the value interval of the comprehensive vulnerability index of each node, and the comprehensive vulnerability index expressed in the form of interval is obtained;
  • the three-parameter interval number sorting method based on Boolean matrix is used to sort the comprehensive vulnerability indicators expressed in the form of intervals, and identify vulnerable nodes.
  • the process of identifying vulnerable nodes includes:
  • interval number of the comprehensive vulnerability index of node i be The number of intervals of the comprehensive vulnerability index of node j is the lower limit of the comprehensive vulnerability index of node i, is the expected value, is the upper limit value; is the lower limit of the comprehensive vulnerability index of node j, is the expected value, is the upper limit value;
  • the number of intervals is sorted according to the size of ⁇ i , the vulnerability level of each node is determined, and the vulnerable node is identified.
  • the invention can accurately and quickly identify the vulnerable nodes in the power grid under the condition that the new energy is connected to the power distribution network, and effectively evaluate the vulnerability of the power distribution network, which is beneficial to the power grid operator to comprehensively and deeply grasp the power distribution. the security status of the network, eliminating or mitigating risks arising from vulnerabilities.
  • Fig. 1 is the overall flow schematic diagram of the present invention
  • FIG. 2 is a general flow chart of a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy provided in an embodiment
  • Fig. 3 is the comprehensive vulnerability assessment index system of the active distribution network node provided in the embodiment.
  • Fig. 4 is the wiring diagram of the IEEE39 node system with photovoltaic access provided in the embodiment
  • Fig. 5 shows the upper limit value, the expected value and the lower limit value of the comprehensive vulnerability index value of the distribution network calculated in the embodiment.
  • an active distribution network vulnerable node identification method considering the impact of new energy includes:
  • Step 1 Build DG (distributed power supply) random output model and load random model.
  • Step 2 Comprehensively consider the inherent topology of the active distribution network and the uncertainty of the DG, and take into account the probability of node failure and the impact on the system caused by network topology and power flow changes after exiting operation. Build node vulnerability index evaluation system.
  • Step 3 Use the stacked autoencoder neural network to obtain the index weight, and establish the node comprehensive vulnerability index.
  • Step 4 Calculate the comprehensive vulnerability index of nodes based on the Latin hypercube sampling method, and identify vulnerable nodes using the three-parameter interval number sorting method based on Boolean matrix.
  • the step 1 is achieved in the following ways:
  • Beta distribution Light intensity is generally considered to belong to the Beta distribution, and its probability density function is as follows:
  • E represents the light intensity
  • E max is the maximum light intensity in a certain time
  • k′ and c′ are the shape parameters of the Beta distribution.
  • A is the area of the photovoltaic square
  • ⁇ is the photoelectric conversion efficiency
  • the probability density functions of the active and reactive power of the load are:
  • ⁇ P and ⁇ Q are the mean values of active and reactive power of the load, respectively, and ⁇ P and ⁇ Q are their corresponding variances, respectively.
  • step 2 is achieved by:
  • System structure vulnerability refers to the ability to maintain the integrity of the system after its own structural changes, taking into account the randomness and volatility of new energy sources. Therefore, the structural vulnerability index mainly considers the topology structure and electrical characteristics of the power network. Based on the complex network analysis method, the important nodes at key positions in the power grid are screened. The more important and influential nodes are to the structure of the distribution network, the greater the vulnerability. high.
  • the node network cohesion index is based on measuring the degree of damage to the overall connectivity of the network after the node is deleted, and judges the importance of the node by judging the damage to the system topology after the node is disconnected.
  • a(k) is the average shortest electrical distance in the network after the node k is contracted by the node contraction method
  • d ij is the shortest electrical distance between any two nodes i and j in the contracted network, that is, the nodes i and j.
  • the sum of the impedance values of the transmission lines on the power transmission path between nodes j is the smallest
  • n' is the number of nodes in the network after the contraction.
  • the degree of network cohesion can be obtained by the following formula:
  • b(k) is the network cohesion degree of node k.
  • the degree of network cohesion describes the degree to which the node resides in the center of the network and the ability to maintain network connectivity. The greater the network cohesion of a node in the distribution network, the more important the node is.
  • the network efficiency of the distribution network should not only reflect the topology of the network, but also take into account the electrical characteristics of the power grid, so it is defined as:
  • C represents the network energy efficiency
  • G and D are the generator and load node sets, respectively.
  • N G and N D are the number of generators and load nodes, respectively, min(P Gi′ , P Dj′ ) is the smaller value of active power in the generator load node pair (i′, j′), which represents the node pair (i′,j′) The maximum power that can be transferred between.
  • P Gi' is the active power of the generator node i'
  • P Di' is the active power of the load node j'.
  • the connectivity of the power network is affected by each node. If key nodes are lost, the connectivity and network performance will change. Therefore, the change rate of network performance before and after the removal of node k can be defined to measure the importance of the node, namely:
  • c(k) is the change rate of the network performance before and after the failure of node k
  • C(k) is the network performance after the failure of node k.
  • the electrical betweenness of the node organically combines the topology of the power grid with the electrical parameters, which makes up for the defect that the traditional betweenness ignores the electrical characteristics of the system, and is more in line with the operating conditions of the power grid.
  • e(k) is the electrical betweenness of node k
  • the power transmission distribution coefficient that is, the unit power injected into the generator load node pair (i', j'), the load flow capacity on branch l.
  • E(k) is the power flow variation of node k when unit power is injected into the generator load node pair (i′, j′), and ⁇ k is the set of lines directly connected to node k. The greater the electrical betweenness of a node, the higher the importance of the node.
  • the node state vulnerability index starts from the operating state of the distribution network, examines the degree of deviation of electrical quantities after a fault occurs, and represents the ability of the power grid to withstand disturbances or faults.
  • F(k) is the probability that the node k voltage exceeds the limit, is the number of times the node k voltage exceeds the upper limit, N (k) is the number of times the node k voltage exceeds the lower limit, and N is the total number of sampling times.
  • the node failure risk factor is defined as:
  • P'j is the current power flow of line j after node k fails, is the initial flow of line j.
  • M is the total number of branches in the system.
  • the power flow shock entropy of node k can be obtained as:
  • Pi and Q i are the active and reactive power injected by node i
  • U i and U j are the voltage amplitudes of node i
  • G ij and B ij are the elements of the admittance matrix
  • V 1 and U 1 are orthogonal matrices of c ⁇ c
  • vi and ui are the column vectors corresponding to ⁇ i in V 1 and U 1 , respectively.
  • the minimum singular value ⁇ i,min of the power flow Jacobian matrix J can represent the relative degree of system voltage stability. The smaller the minimum singular value is, the more unstable the system voltage is; otherwise, the more stable the system voltage is. Therefore, the rate of change of the smallest singular value before and after the removal of node k is defined as:
  • the improved power flow shock entropy index is defined as:
  • the improved minimum singular value change rate indicator is defined as:
  • step (3) is realized in the following manner:
  • the singular value change rate indicators are combined to define the comprehensive vulnerability index of node k as:
  • ⁇ represents the weight of each indicator.
  • step 4 is realized in the following ways:
  • the node comprehensive vulnerability index established in step 3 can only be calculated with a certain method to obtain a deterministic index calculation value, which cannot reflect the uncertainty of the random fluctuation of the new energy output and load under the normal operation of the power grid. Therefore, in order to take into account the influence of new energy output and load uncertainty, the probabilistic power flow method is used to calculate the node comprehensive vulnerability index.
  • Latin hypercube sampling method has greatly improved computational efficiency compared with Monte Carlo sampling method, and can provide rich information of output random variables, so that the calculated comprehensive vulnerability index can be calculated from statistical to better meet the requirements.
  • the main idea of the Latin hypercube sampling method is based on the inverse function transformation method.
  • the method is divided into the following two steps: 1 Sampling: Sampling each input random variable to ensure that the random distribution area can be completely covered by the sampling points. 2 Arrangement: Change the arrangement order of the sampling values of each random variable to minimize the correlation of the sampling values of each random variable.
  • n 1,2,...,N.
  • N is the total number of sampling times.
  • ⁇ L is a K ⁇ K order matrix, which can be expressed as:
  • ⁇ ij is the correlation coefficient between the i-th row and the j-th row of the arrangement matrix L KN .
  • ⁇ ij can be calculated by the following formula:
  • L ik is the value of the element in the i-th row and the k-th column of the arrangement matrix L KN , is the mean of the elements in the ith row of the permutation matrix L KN .
  • the arrangement order of the elements of the arrangement matrix G KN from large to small is used to indicate the arrangement position of the elements in the sampling matrix X KN , and the elements in the sampling matrix X KN are rearranged.
  • step (1) random sampling is carried out using the Latin hypercube sampling method.
  • the sampling results are the active power of random loads and renewable energy, and the sampling results are brought into formula (17)
  • the node voltage and branch power flow can be obtained by the power flow calculation of the power flow equation.
  • the comprehensive vulnerability index value of each node can be calculated according to the sampling results, node voltage and branch power flow.
  • the comprehensive vulnerability index value of nodes within the set sampling scale can be obtained by the Latin hypercube sampling method in step 4.2, and then the specific distribution of the comprehensive vulnerability index value of each node can be obtained, and the comprehensive vulnerability index value of each node can be calculated according to the equal probability criterion.
  • Upper limit, expected value and lower limit of vulnerability index value The upper and lower limit values and the expected value together constitute the value range of the comprehensive vulnerability index value of each node, so the vulnerability level of each node is reflected by the interval form instead of the definite value form.
  • the present invention adopts the standard IEEE39 node system for calculation example verification, and the serial numbers 1-39 represent the 1-39th nodes, and the photovoltaic with a rated capacity of 100MW is connected to the node 21 .
  • the load obeys the normal distribution, the mean value takes the rated power of the system load, and the variance takes 20% of the mean value.
  • the power of 2000 groups of photovoltaic power generation and the active and reactive power of the load are sampled by Latin hypercube sampling, and the sampling results are respectively brought into the distribution network data for power flow calculation.
  • the five indexes of node degree, network efficiency change rate, network cohesion, improved power flow shock entropy and improved minimum singular value change rate are calculated, and the data is brought into the autoencoder neural network to calculate the weight value of each index. Further, the comprehensive vulnerability index value of the distribution network is calculated.
  • the upper and lower limit values and the expected value together constitute the value range of the comprehensive vulnerability index value of each node.
  • the three-parameter interval number sorting method based on Boolean matrix is used to identify vulnerable nodes. The identification results are shown in the following table:
  • this patented method takes into account the impact of new energy on vulnerability after the connection of new energy, reflecting the principle that the uncertainty brought about by the connection of new energy to the grid will have a greater impact on the nearest neighbor. For example, after node 21 is connected to new energy, Nodes 16, 22 and their subsequent affected nodes will become very vulnerable.
  • the method of the invention constructs a scientific and comprehensive vulnerability evaluation index based on the structure and state of the distribution network, and can accurately and quickly identify the vulnerable nodes in the power grid under the condition that the new energy is connected to the distribution network.
  • the vulnerability of the distribution network can be effectively assessed, thereby reducing the probability of accident safety risks.

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Abstract

Disclosed is an active power distribution network vulnerable node identification method which considers new energy impact, comprising: sampling a pre-constructed DG random output model and load random model to acquire random output data of new energy and loads, determining a random output matrix according to the random output data, and arranging the random output matrix to obtain an arrangement matrix which tends to minimize the correlation of each random variable sampling value; inputting the data in the arrangement matrix into a pre-constructed node vulnerability index evaluation system model to obtain a node comprehensive vulnerability index; and identifying a vulnerable node by using a Boolean matrix-based three-parameter interval number sorting method in combination with the node comprehensive vulnerability index. Advantages: in the present invention, when considering that new energy accesses a power distribution network, vulnerable nodes in the power distribution network can be accurately and quickly identified, and the vulnerability of the power distribution network can be effectively evaluated, facilitating a power grid operator to comprehensively and deeply master the safety situation of the power distribution network, and eliminate or alleviate risks caused by the vulnerability.

Description

一种计及新能源影响的主动配电网脆弱节点辨识方法A method for identifying vulnerable nodes in active distribution network considering the impact of new energy 技术领域technical field
本发明涉及一种计及新能源影响的主动配电网脆弱节点辨识方法，属于主动配电网脆弱节点辨识技术领域。The invention relates to an active distribution network vulnerable node identification method considering the impact of new energy, and belongs to the technical field of active distribution network vulnerable node identification.
背景技术Background technique
近年来，国内外电力***发生了多次大停电事故，造成了大量的经济损失。2003年发生的美加大停电和2008年中国南方冰灾，都造成了大范围、长时间的停电；2018年3月21日，巴西电网因断路器过载保护引发连锁故障，导致电网北部和东北部14个州大面积停电。这些停电事故的发生引起了电力工作人员的高度重视，如何辨识电力***中的关键设备风险，成为减少大停电发生的重要研究内容。节点是电网能量传输的出发点和重要汇聚点，大量事实证明某些脆弱节点的故障停运在电网事故传播中起着推波助澜的作用。辨识电网中的脆弱节点，既有助于评估***当前安全水平，又能把握安全水平的变化趋势，对预防连锁故障具有重要意义。In recent years, there have been many blackouts in the domestic and foreign power systems, resulting in a lot of economic losses. The power outage in the U.S. and Canada in 2003 and the ice disaster in southern China in 2008 caused large-scale and long-term power outages; on March 21, 2018, the Brazilian power grid caused a cascading failure due to the overload protection of circuit breakers, causing the northern and northeastern parts of the grid to fail. 14 states experienced widespread power outages. The occurrence of these power outages has attracted the attention of electric power workers. How to identify the risks of key equipment in the power system has become an important research content to reduce the occurrence of blackouts. Nodes are the starting point and important convergence point of power grid energy transmission. A large number of facts have proved that the failure and outage of some vulnerable nodes play a role in the propagation of power grid accidents. Identifying vulnerable nodes in the power grid not only helps to evaluate the current security level of the system, but also grasps the changing trend of the security level, which is of great significance for preventing cascading failures.
另一方面，风电、太阳能等新能源在配电网中的渗透率日益增高。但是由于新能源电源特有的随机性和波动性，当新能源发电出力波动时，配电网潮流也将随之变化，而电网脆弱节点辨识往往需要依赖于潮流结果，因此，如何在新能源接入配电网的情况下，准确和快速地辨识电网中的脆弱节点就尤为迫切和重要。On the other hand, the penetration rate of new energy sources such as wind power and solar energy in the distribution network is increasing day by day. However, due to the unique randomness and volatility of new energy power sources, when the new energy power generation output fluctuates, the power flow of the distribution network will also change, and the identification of vulnerable nodes in the power grid often depends on the power flow results. In the case of entering the distribution network, it is particularly urgent and important to accurately and quickly identify the vulnerable nodes in the power grid.
发明内容SUMMARY OF THE INVENTION
本发明所要解决的技术问题是克服现有技术的缺陷，提供一种计及新能源影响的主动配电网脆弱节点辨识方法，以实现在新能源接入配电网的情况下，准确和快速地辨识电网中的脆弱节点，确保主动配电网的安全稳定运行。The technical problem to be solved by the present invention is to overcome the defects of the prior art, and to provide a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy, so as to achieve accurate and fast detection when the new energy is connected to the distribution network. Identify vulnerable nodes in the power grid to ensure the safe and stable operation of the active distribution network.
为解决上述技术问题，本发明提供一种计及新能源影响的主动配电网脆弱节点辨识方法，In order to solve the above technical problems, the present invention provides a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy,
对预先构建的DG随机出力模型和负荷随机模型进行抽样，获取新能源和负荷的随机出力数据，根据随机出力数据确定随机出力矩阵，对随机出力矩阵 进行排列，得到使每个随机变量采样值的相关性趋于最小的排列矩阵；Sampling the pre-built DG random output model and load random model, obtain the random output data of new energy and load, determine the random output matrix according to the random output data, and arrange the random output matrix to obtain the sampling value of each random variable. The permutation matrix whose correlation tends to be the smallest;
将排列矩阵中的数据输入到预先构建的节点脆弱性指标评估体系模型，得到节点综合脆弱性指标；所述预先构建的节点脆弱性指标评估体系模型为综合考虑主动配电网固有拓扑结构与DG的不确定性，并兼顾节点故障的概率及其退出运行后由于网络拓扑结构和电力潮流变化对***造成的影响的计算模型；The data in the arrangement matrix is input into the pre-built node vulnerability index evaluation system model, and the node comprehensive vulnerability index is obtained; the pre-built node vulnerability index evaluation system model is a comprehensive consideration of the inherent topology structure of the active distribution network and the DG The uncertainty of the node, taking into account the probability of node failure and the calculation model of the impact of the network topology and power flow changes on the system after it is out of operation;
利用基于布尔矩阵的三参数区间数排序方法结合节点综合脆弱性指标辨识出脆弱节点。Vulnerable nodes are identified by the three-parameter interval number sorting method based on Boolean matrix combined with node comprehensive vulnerability index.
进一步的，所述预先构建的节点脆弱性指标评估体系模型包括：结构脆弱性指标计算模型、状态脆弱性指标计算模型以及指标权重计算模型；Further, the pre-built node vulnerability index evaluation system model includes: a structural vulnerability index calculation model, a state vulnerability index calculation model, and an index weight calculation model;
所述结构脆弱性指标计算模型，用于计算网络凝聚度、网络效能变化率和节点电气介数；The structural vulnerability index calculation model is used to calculate network cohesion, network efficiency change rate and node electrical betweenness;
所述状态脆弱性指标计算模型，用于计算改进的潮流冲击熵和改进的最小奇异值变化率；The state vulnerability index calculation model is used to calculate the improved power flow shock entropy and the improved minimum singular value change rate;
所述指标权重计算模型，用于计算网络凝聚度、网络效能变化率、节点电气介数、改进的潮流冲击熵和改进的最小奇异值变化率的权重。The index weight calculation model is used to calculate the weights of network cohesion, network efficiency change rate, node electrical betweenness, improved power flow impulse entropy and improved minimum singular value change rate.
进一步的，所述网络凝聚度通过下式得到：Further, the network cohesion is obtained by the following formula:
Figure PCTCN2021109973-appb-000001
Figure PCTCN2021109973-appb-000001
式中，a(k)为采用节点收缩法对节点k进行收缩后网络中的平均最短电气距离，n′为收缩后网络中节点的个数；In the formula, a(k) is the average shortest electrical distance in the network after the node k is contracted by the node contraction method, and n′ is the number of nodes in the network after the contraction;
Figure PCTCN2021109973-appb-000002
Figure PCTCN2021109973-appb-000002
式中，d ij为收缩后网络中任意俩节点i和节点j之间的最短电气距离，V表示网络中所有节点的集合； In the formula, d ij is the shortest electrical distance between any two nodes i and j in the contracted network, and V represents the set of all nodes in the network;
所述网络效能变化率通过下式得到：The network performance change rate is obtained by the following formula:
Figure PCTCN2021109973-appb-000003
Figure PCTCN2021109973-appb-000003
式中，c(k)为节点k失效前后网络效能的变化率，C(k)为节点k失效后的网 络效能，C为网络能效；In the formula, c(k) is the change rate of the network efficiency before and after the failure of node k, C(k) is the network efficiency after the failure of node k, and C is the network energy efficiency;
Figure PCTCN2021109973-appb-000004
Figure PCTCN2021109973-appb-000004
式中，G和D分别为发电机和负荷节点集合，N G和N D分别为发电机和负荷节点的个数，min(P Gi′,P Dj′)为发电机负荷节点对(i′,j′)中有功功率较小值，P Gi′为发电机节点i′的有功功率，P Di′为负荷节点j′的有功功率； In the formula, G and D are the generator and load node sets, respectively, N G and N D are the number of generator and load nodes, respectively, min(P Gi′ , P Dj′ ) is the generator load node pair (i′ ) ,j′) the smaller value of the active power, P Gi′ is the active power of the generator node i′, P Di′ is the active power of the load node j′;
所述节点电气介数通过下式得到：The electrical betweenness of the node is obtained by the following formula:
Figure PCTCN2021109973-appb-000005
Figure PCTCN2021109973-appb-000005
式中，e(k)为节点k的电气介数，
Figure PCTCN2021109973-appb-000006
为在发电机负荷节点对(i′,j′)中注入单位功率，支路l上的潮流承载量，E(k)为在发电机负荷节点对(i′,j′)中注入单位功率时节点k的潮流变化量，Ω k为与节点k直接相连的线路集合。 where e(k) is the electrical betweenness of node k,
Figure PCTCN2021109973-appb-000006
In order to inject unit power into the generator load node pair (i', j'), the load flow capacity on branch l, E(k) is the unit power injected into the generator load node pair (i', j') is the power flow variation of node k at time, and Ω k is the set of lines directly connected to node k.
进一步的，所述改进的潮流冲击熵通过下式得到：Further, the improved power flow shock entropy is obtained by the following formula:
l(k)＝f(k)g(k)l(k)=f(k)g(k)
式中，l(k)为改进的潮流冲击熵，f(k)为节点故障风险因子，g(k)为节点k的潮流冲击熵，In the formula, l(k) is the improved power flow shock entropy, f(k) is the node failure risk factor, g(k) is the power flow shock entropy of node k,
f(k)＝e αF(k) f(k)=e αF(k)
式中，α为风险加权系数，F(k)为节点k电压越限的概率，In the formula, α is the risk weighting coefficient, F(k) is the probability that the node k voltage exceeds the limit,
Figure PCTCN2021109973-appb-000007
Figure PCTCN2021109973-appb-000007
式中，
Figure PCTCN2021109973-appb-000008
为节点k电压越上限的次数， N(k)为节点k电压越下限的次数，N为采样总次数 In the formula,
Figure PCTCN2021109973-appb-000008
is the number of times the node k voltage exceeds the upper limit, N (k) is the number of times the node k voltage exceeds the lower limit, and N is the total number of sampling times
Figure PCTCN2021109973-appb-000009
Figure PCTCN2021109973-appb-000009
式中，G j(k)为节点k退出运行后对线路j造成的潮流冲击率，M为***中总的支路数， In the formula, G j (k) is the impact rate of power flow to line j after node k is out of operation, M is the total number of branches in the system,
Figure PCTCN2021109973-appb-000010
Figure PCTCN2021109973-appb-000010
式中，ΔP j(k)为节点k因故障退出运行后对线路j造成的潮流冲击量； In the formula, ΔP j (k) is the power flow impact on line j after node k is out of operation due to a fault;
Figure PCTCN2021109973-appb-000011
Figure PCTCN2021109973-appb-000011
式中，P′ j为节点k故障后线路j的当前潮流，
Figure PCTCN2021109973-appb-000012
为线路j的初始潮流；所述改进的最小奇异值变化率通过下式得到： where P'j is the current power flow of line j after node k fails,
Figure PCTCN2021109973-appb-000012
is the initial power flow of line j; the improved minimum singular value change rate is obtained by the following formula:
o(k)＝f(k)h(k)o(k)=f(k)h(k)
式中，o(k)为改进的最小奇异值变化率，h(k)为最小奇异值变化率；In the formula, o(k) is the improved minimum singular value change rate, h(k) is the minimum singular value change rate;
Figure PCTCN2021109973-appb-000013
Figure PCTCN2021109973-appb-000013
式中，
Figure PCTCN2021109973-appb-000014
为节点k移除前***潮流雅可比矩阵的最小奇异值，δ i,min(k)为节点k移除后***潮流雅可比矩阵的最小奇异值。 In the formula,
Figure PCTCN2021109973-appb-000014
is the smallest singular value of the Jacobian matrix of the system power flow before node k is removed, and δ i,min (k) is the smallest singular value of the Jacobian matrix of the system power flow after node k is removed.
进一步的，所述指标权重计算模型的计算过程为：Further, the calculation process of the index weight calculation model is:
将每个节点的网络凝聚度、网络效能变化率、节点电气介数、改进的潮流冲击熵输入堆叠自动编码器神经网络中确定各指标权重。The network cohesion of each node, the rate of change of network efficiency, the electrical betweenness of the node, and the improved power flow shock entropy are input into the stacked autoencoder neural network to determine the weight of each index.
进一步的，所述利用基于布尔矩阵的三参数区间数排序方法结合所述各节点的综合脆弱性指标辨识出脆弱节点的过程包括：Further, the process of identifying vulnerable nodes using the three-parameter interval number sorting method based on Boolean matrix combined with the comprehensive vulnerability index of each node includes:
根据各节点的综合脆弱性指标的值获取每个节点综合脆弱性指标的具体分布，根据等概率准则计算出各节点的综合脆弱性指标的上限值、期望值与下限值，上限值、下限值与期望值共同构成各节点的综合脆弱性指标的取值区间，得到以区间形式表示的综合脆弱性指标；The specific distribution of the comprehensive vulnerability index of each node is obtained according to the value of the comprehensive vulnerability index of each node, and the upper limit, expected value and lower limit of the comprehensive vulnerability index of each node are calculated according to the equal probability criterion. The lower limit value and the expected value together constitute the value interval of the comprehensive vulnerability index of each node, and the comprehensive vulnerability index expressed in the form of interval is obtained;
采用基于布尔矩阵的三参数区间数排序方法对以区间形式表示的综合脆弱性指标进行排序，辨识出脆弱节点。The three-parameter interval number sorting method based on Boolean matrix is used to sort the comprehensive vulnerability indicators expressed in the form of intervals, and identify vulnerable nodes.
进一步的，所述采用基于布尔矩阵的三参数区间数排序方法对以区间形式表示的综合脆弱性指标进行排序，辨识出脆弱节点的过程包括：Further, according to the three-parameter interval number sorting method based on Boolean matrix to sort the comprehensive vulnerability indicators expressed in the form of intervals, the process of identifying vulnerable nodes includes:
设节点i的综合脆弱性指标的区间数为
Figure PCTCN2021109973-appb-000015
节点j的综合脆弱性指标的区间数
Figure PCTCN2021109973-appb-000016
Figure PCTCN2021109973-appb-000017
为节点i的综合脆弱性指标下限值，
Figure PCTCN2021109973-appb-000018
为期望值，
Figure PCTCN2021109973-appb-000019
为上限值；
Figure PCTCN2021109973-appb-000020
为节点j的综合脆弱性指标下限值，
Figure PCTCN2021109973-appb-000021
为期望值，
Figure PCTCN2021109973-appb-000022
为上限值； Let the interval number of the comprehensive vulnerability index of node i be
Figure PCTCN2021109973-appb-000015
The number of intervals of the comprehensive vulnerability index of node j
Figure PCTCN2021109973-appb-000016
Figure PCTCN2021109973-appb-000017
is the lower limit of the comprehensive vulnerability index of node i,
Figure PCTCN2021109973-appb-000018
is the expected value,
Figure PCTCN2021109973-appb-000019
is the upper limit value;
Figure PCTCN2021109973-appb-000020
is the lower limit of the comprehensive vulnerability index of node j,
Figure PCTCN2021109973-appb-000021
is the expected value,
Figure PCTCN2021109973-appb-000022
is the upper limit value;
令
Figure PCTCN2021109973-appb-000023
则a i大于a j的概率
Figure PCTCN2021109973-appb-000024
令c ij＝P(a i＞a j)； make
Figure PCTCN2021109973-appb-000023
then the probability that a i is greater than a j
Figure PCTCN2021109973-appb-000024
Let c ij =P(a i >a j );
构造布尔矩阵E＝(e ij) m×m，E为区间数a 1,a 2,...,a m的排序矩阵，其中， Construct a Boolean matrix E=(e ij ) m×m , where E is a sorting matrix of interval numbers a 1 , a 2 ,...,am, where,
Figure PCTCN2021109973-appb-000025
Figure PCTCN2021109973-appb-000025
令
Figure PCTCN2021109973-appb-000026
得到排序向量λ＝(λ 1,λ 2,...λ n)，λ i是节点i脆弱性水平的排序量值； make
Figure PCTCN2021109973-appb-000026
Obtain the ranking vector λ=(λ 1 ,λ 2 ,...λ n ), where λ i is the ranking value of the vulnerability level of node i;
根据λ i的大小对区间数进行排序，确定每个节点的脆弱性水平，辨识出脆弱节点。 The number of intervals is sorted according to the size of λ i , the vulnerability level of each node is determined, and the vulnerable node is identified.
本发明所达到的有益效果：Beneficial effects achieved by the present invention:
本发明能够在计及新能源接入配电网的情况下，准确和快速地辨识电网中的脆弱节点，对配电网脆弱性进行有效评估，有利于电网运营者全面、深入的掌握配电网的安全状况，消除或缓和脆弱性引发的风险。The invention can accurately and quickly identify the vulnerable nodes in the power grid under the condition that the new energy is connected to the power distribution network, and effectively evaluate the vulnerability of the power distribution network, which is beneficial to the power grid operator to comprehensively and deeply grasp the power distribution. the security status of the network, eliminating or mitigating risks arising from vulnerabilities.
附图说明Description of drawings
图1是本发明的整体流程示意图；Fig. 1 is the overall flow schematic diagram of the present invention;
图2是实施例中提供的一种计及新能源影响的主动配电网脆弱节点辨识方法的总的流程图；2 is a general flow chart of a method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy provided in an embodiment;
图3是实施例中提供的主动配电网节点的综合脆弱性评估指标体系；Fig. 3 is the comprehensive vulnerability assessment index system of the active distribution network node provided in the embodiment;
图4是实施例中提供的含光伏接入的IEEE39节点***接线图；Fig. 4 is the wiring diagram of the IEEE39 node system with photovoltaic access provided in the embodiment;
图5是实施例中计算的配电网综合脆弱性指标值的上限值、期望值与下限 值。Fig. 5 shows the upper limit value, the expected value and the lower limit value of the comprehensive vulnerability index value of the distribution network calculated in the embodiment.
具体实施方式Detailed ways
下面结合附图对本发明作进一步描述。以下实施例仅用于更加清楚地说明本发明的技术方案，而不能以此来限制本发明的保护范围。The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.
如图1-3所示，一种计及新能源影响的主动配电网脆弱节点辨识方法，包括：As shown in Figure 1-3, an active distribution network vulnerable node identification method considering the impact of new energy includes:
步骤1：构建DG(分布式电源)随机出力模型和负荷随机模型。Step 1: Build DG (distributed power supply) random output model and load random model.
步骤2：综合考虑主动配电网固有拓扑结构与DG的不确定性，并兼顾节点故障的概率及其退出运行后由于网络拓扑结构和电力潮流变化对***造成的影响，构建节点脆弱性指标评估体系。Step 2: Comprehensively consider the inherent topology of the active distribution network and the uncertainty of the DG, and take into account the probability of node failure and the impact on the system caused by network topology and power flow changes after exiting operation. Build node vulnerability index evaluation system.
步骤3：利用堆叠自动编码器神经网络求取指标权重，建立节点综合脆弱性指标。Step 3: Use the stacked autoencoder neural network to obtain the index weight, and establish the node comprehensive vulnerability index.
步骤4：采用基于拉丁超立方采样方法计算节点综合脆弱性指标，并利用基于布尔矩阵的三参数区间数排序方法辨识出脆弱节点。Step 4: Calculate the comprehensive vulnerability index of nodes based on the Latin hypercube sampling method, and identify vulnerable nodes using the three-parameter interval number sorting method based on Boolean matrix.
所述步骤1通过以下方式实现的：The step 1 is achieved in the following ways:
1.1光伏随机出力模型1.1 Photovoltaic random output model
通常认为光照强度属于Beta分布，其概率密度函数如下：Light intensity is generally considered to belong to the Beta distribution, and its probability density function is as follows:
Figure PCTCN2021109973-appb-000027
Figure PCTCN2021109973-appb-000027
式中，E表示光照强度，E max为某一时间内的最大光照强度，k′、c′是Beta分布的形状参数。 In the formula, E represents the light intensity, E max is the maximum light intensity in a certain time, and k′ and c′ are the shape parameters of the Beta distribution.
光伏出力P V与光照强度E的关系如下： The relationship between photovoltaic output P V and light intensity E is as follows:
P V＝EAη   (2) P V =EAη (2)
式中，A为光伏方阵的面积，η为光电转换效率。In the formula, A is the area of the photovoltaic square, and η is the photoelectric conversion efficiency.
1.2负荷随机模型1.2 Load stochastic model
对于负荷随机模型，由大量历史实际数据分析可知，正态分布模型可近似模拟其随机波动性。For the load stochastic model, it can be seen from the analysis of a large number of historical actual data that the normal distribution model can approximate its stochastic volatility.
负荷的有功和无功功率的概率密度函数分别为：The probability density functions of the active and reactive power of the load are:
Figure PCTCN2021109973-appb-000028
Figure PCTCN2021109973-appb-000028
Figure PCTCN2021109973-appb-000029
Figure PCTCN2021109973-appb-000029
式中，μ P、μ Q分别为负荷有功、无功功率的均值，σ P、σ Q分别为其对应的方差。 In the formula, μ P and μ Q are the mean values of active and reactive power of the load, respectively, and σ P and σ Q are their corresponding variances, respectively.
所述步骤2是通过以下方式实现的：Said step 2 is achieved by:
2.1节点结构脆弱性指标的构建2.1 Construction of node structure vulnerability index
***结构脆弱性是指在计及新能源随机性与波动性的影响下，自身组成结构改变后保持***完整的能力。所以结构脆弱性指标主要考虑电力网络的拓扑结构以及电气特性，基于复杂网络分析法筛选在电网中处于关键位置的重要节点，对配电网的结构越重要、影响越大的节点的脆弱性越高。System structure vulnerability refers to the ability to maintain the integrity of the system after its own structural changes, taking into account the randomness and volatility of new energy sources. Therefore, the structural vulnerability index mainly considers the topology structure and electrical characteristics of the power network. Based on the complex network analysis method, the important nodes at key positions in the power grid are screened. The more important and influential nodes are to the structure of the distribution network, the greater the vulnerability. high.
2.1.1网络凝聚度2.1.1 Network Cohesion
节点网络凝聚度指标以衡量删除节点后网络整体连通性受到破坏的程度为基础,通过判断节点断开后对***拓扑结构的破坏性大小来判断节点的重要性。The node network cohesion index is based on measuring the degree of damage to the overall connectivity of the network after the node is deleted, and judges the importance of the node by judging the damage to the system topology after the node is disconnected.
Figure PCTCN2021109973-appb-000030
Figure PCTCN2021109973-appb-000030
式中a(k)为采用节点收缩法对节点k进行收缩后网络中的平均最短电气距离，d ij为收缩后网络中任意俩节点i和节点j之间的最短电气距离，即节点i和节点j之间电能传输路径上输电线路的阻抗值之和最小，n′为收缩后网络中节点的个数。 网络凝聚度可由下式得出： where a(k) is the average shortest electrical distance in the network after the node k is contracted by the node contraction method, and d ij is the shortest electrical distance between any two nodes i and j in the contracted network, that is, the nodes i and j. The sum of the impedance values of the transmission lines on the power transmission path between nodes j is the smallest, and n' is the number of nodes in the network after the contraction. The degree of network cohesion can be obtained by the following formula:
Figure PCTCN2021109973-appb-000031
Figure PCTCN2021109973-appb-000031
式中：b(k)为节点k的网络凝聚度。In the formula: b(k) is the network cohesion degree of node k.
网络凝聚度描述了该节点居于网络中心的程度和保持网络连通的能力,。配电网中节点的网络凝聚度越大，表明该节点越重要。The degree of network cohesion describes the degree to which the node resides in the center of the network and the ability to maintain network connectivity. The greater the network cohesion of a node in the distribution network, the more important the node is.
2.1.2网络效能变化率2.1.2 Change rate of network performance
配电网的网络效能既要能够反映网络的拓扑结构，又要考虑到电网的电气特性，因此定义为：The network efficiency of the distribution network should not only reflect the topology of the network, but also take into account the electrical characteristics of the power grid, so it is defined as:
Figure PCTCN2021109973-appb-000032
Figure PCTCN2021109973-appb-000032
式中：C表示网络能效，G和D分别为发电机和负荷节点集合。N G和N D分别为发电机和负荷节点的个数，min(P Gi′,P Dj′)为发电机负荷节点对(i′,j′)中有功功率较小值，其代表节点对(i′,j′)间能传输的最大电能。P Gi′为发电机节点i′的有功功率，P Di′为负荷节点j′的有功功率。 In the formula: C represents the network energy efficiency, G and D are the generator and load node sets, respectively. N G and N D are the number of generators and load nodes, respectively, min(P Gi′ , P Dj′ ) is the smaller value of active power in the generator load node pair (i′, j′), which represents the node pair (i′,j′) The maximum power that can be transferred between. P Gi' is the active power of the generator node i', and P Di' is the active power of the load node j'.
电力网络的连通性受到每一个节点的影响，若失去关键节点，其连通性及网络效能将发生改变。因此可定义节点k移除前后网络效能的变化率来衡量节点的重要程度，即：The connectivity of the power network is affected by each node. If key nodes are lost, the connectivity and network performance will change. Therefore, the change rate of network performance before and after the removal of node k can be defined to measure the importance of the node, namely:
Figure PCTCN2021109973-appb-000033
Figure PCTCN2021109973-appb-000033
式中：c(k)为节点k失效前后网络效能的变化率，C(k)为节点k失效后的网络效能。In the formula: c(k) is the change rate of the network performance before and after the failure of node k, and C(k) is the network performance after the failure of node k.
节点的网络效能变化率越大，表明该节点失效后对电网的电能传输影响越大，该节点在电网中的重要程度也就越高。The greater the change rate of the network efficiency of the node, the greater the impact on the power transmission of the power grid after the failure of the node, and the higher the importance of the node in the power grid.
2.1.3节点电气介数2.1.3 Node Electrical Betweenness
节点电气介数将电网拓扑结构与电气参量有机结合，弥补了传统介数忽略***电气特性的缺陷，更加符合电网的运行状况。定义为：The electrical betweenness of the node organically combines the topology of the power grid with the electrical parameters, which makes up for the defect that the traditional betweenness ignores the electrical characteristics of the system, and is more in line with the operating conditions of the power grid. defined as:
Figure PCTCN2021109973-appb-000034
Figure PCTCN2021109973-appb-000034
式中：e(k)为节点k的电气介数，
Figure PCTCN2021109973-appb-000035
为功率传输分布系数，即在发电机负荷节点对(i′,j′)中注入单位功率，支路l上的潮流承载量。E(k)为在发电机负荷节点对(i′,j′)中注入单位功率时节点k的潮流变化量，Ω k为与节点k直接相连的线路集合。节点电气介数越大，该节点的重要性越高。 where e(k) is the electrical betweenness of node k,
Figure PCTCN2021109973-appb-000035
is the power transmission distribution coefficient, that is, the unit power injected into the generator load node pair (i', j'), the load flow capacity on branch l. E(k) is the power flow variation of node k when unit power is injected into the generator load node pair (i′, j′), and Ω k is the set of lines directly connected to node k. The greater the electrical betweenness of a node, the higher the importance of the node.
2.2节点状态脆弱性指标的构建2.2 Construction of Node State Vulnerability Indicators
节点状态脆弱性指标从配电网运行状态出发，考察故障发生后电气量的偏移程度，表征了电网承受干扰或故障的能力。The node state vulnerability index starts from the operating state of the distribution network, examines the degree of deviation of electrical quantities after a fault occurs, and represents the ability of the power grid to withstand disturbances or faults.
2.2.1节点故障风险因子2.2.1 Node Failure Risk Factor
通过基于拉丁超立方采样的概率潮流计算方法可以计算出各节点的电压越限风险概率：Through the probability power flow calculation method based on Latin hypercube sampling, the risk probability of voltage violation of each node can be calculated:
Figure PCTCN2021109973-appb-000036
Figure PCTCN2021109973-appb-000036
式中：F(k)为节点k电压越限的概率，
Figure PCTCN2021109973-appb-000037
为节点k电压越上限的次数， N(k)为节点k电压越下限的次数，N为采样总次数。 In the formula: F(k) is the probability that the node k voltage exceeds the limit,
Figure PCTCN2021109973-appb-000037
is the number of times the node k voltage exceeds the upper limit, N (k) is the number of times the node k voltage exceeds the lower limit, and N is the total number of sampling times.
由上式计算出的节点越限的概率越大，就表明该节点在DG及负荷波动时越容易发生故障，所以定义节点故障风险因子为：The higher the probability that the node exceeds the limit calculated by the above formula, it indicates that the node is more likely to fail when the DG and load fluctuate. Therefore, the node failure risk factor is defined as:
f(k)＝e αF(k)    (11) f(k)=e αF(k) (11)
式中：α为风险加权系数，取α＝2.56。In the formula: α is the risk weighting coefficient, take α=2.56.
2.2.2节点潮流冲击熵2.2.2 Node power flow shock entropy
设节点k因故障退出运行后对线路j造成的潮流冲击量为：Assume that the power flow impact on line j after node k is out of operation due to a fault is:
Figure PCTCN2021109973-appb-000038
Figure PCTCN2021109973-appb-000038
式中：P′ j为节点k故障后线路j的当前潮流，
Figure PCTCN2021109973-appb-000039
为线路j的初始潮流。 where: P'j is the current power flow of line j after node k fails,
Figure PCTCN2021109973-appb-000039
is the initial flow of line j.
此时，节点k退出运行后对线路j造成的潮流冲击率为：At this time, the impact rate of power flow to line j after node k is out of operation is:
Figure PCTCN2021109973-appb-000040
Figure PCTCN2021109973-appb-000040
式中：M为***中总的支路数。Where: M is the total number of branches in the system.
利用熵理论可以得到节点k的潮流冲击熵为：Using the entropy theory, the power flow shock entropy of node k can be obtained as:
Figure PCTCN2021109973-appb-000041
Figure PCTCN2021109973-appb-000041
节点断开后，潮流冲击熵越小，说明***的潮流集中分布在某几条支路上，易出现过载支路甚至引发连锁故障，严重影响***的安全水平。After the node is disconnected, the smaller the power flow shock entropy is, it means that the power flow of the system is concentrated on some branches, which is prone to overload branches or even cause cascading failures, which seriously affects the safety level of the system.
2.2.3节点最小奇异值变化率2.2.3 Node minimum singular value change rate
对于有p个独立节点、q个PV节点的电力***，潮流方程的极坐标形式为：For a power system with p independent nodes and q PV nodes, the polar coordinate form of the power flow equation is:
Figure PCTCN2021109973-appb-000042
Figure PCTCN2021109973-appb-000042
式中：P i和Q i为节点i注入的有功和无功功率，U i和U j为节点i的电压幅值，G ij和B ij为导纳矩阵的元素，θ ij为节点之间的相角差，i＝1,2,...,p且j＝1,2,...,p。 In the formula: Pi and Q i are the active and reactive power injected by node i , U i and U j are the voltage amplitudes of node i, G ij and B ij are the elements of the admittance matrix, and θ ij is the distance between nodes The phase angle difference of i=1,2,...,p and j=1,2,...,p.
对式(17)用泰勒级数展开，获得雅可比矩阵J，并对其进行奇异值分解可得：Expand Equation (17) with Taylor series to obtain the Jacobian matrix J, and perform singular value decomposition on it to obtain:
Figure PCTCN2021109973-appb-000043
Figure PCTCN2021109973-appb-000043
式中：J∈P c×c，V 1和U 1均为c×c的正交矩阵，Λ为奇异值δ i(i＝1,2,...,c)组成的非负对角阵，v i和u i分别为V 1和U 1中δ i所对应的列向量。 In the formula: J∈P c×c , V 1 and U 1 are orthogonal matrices of c×c, Λ is the non-negative diagonal composed of singular values δ i (i=1,2,...,c) matrix, vi and ui are the column vectors corresponding to δ i in V 1 and U 1 , respectively.
潮流雅可比矩阵J的最小奇异值δ i,min能够表征***电压稳定的相对程度，最小奇异值越小，***电压越不稳定；反之，***电压相对越稳定。因此定义节点k移除前后的最小奇异值的变化率为： The minimum singular value δ i,min of the power flow Jacobian matrix J can represent the relative degree of system voltage stability. The smaller the minimum singular value is, the more unstable the system voltage is; otherwise, the more stable the system voltage is. Therefore, the rate of change of the smallest singular value before and after the removal of node k is defined as:
Figure PCTCN2021109973-appb-000044
Figure PCTCN2021109973-appb-000044
式中：
Figure PCTCN2021109973-appb-000045
为节点k移除前***潮流雅可比矩阵的最小奇异值，δ i,min(k)为节点k移除后***潮流雅可比矩阵的最小奇异值。 where:
Figure PCTCN2021109973-appb-000045
is the smallest singular value of the Jacobian matrix of the system power flow before node k is removed, and δ i,min (k) is the smallest singular value of the Jacobian matrix of the system power flow after node k is removed.
节点退出运行后，最小奇异值的变化率越小，该节点越重要。After the node is out of operation, the smaller the rate of change of the smallest singular value, the more important the node is.
2.2.4综合考虑节点断开的可能性和断开后果，定义改进的潮流冲击熵指标为：2.2.4 Considering the possibility of node disconnection and the consequences of disconnection, the improved power flow shock entropy index is defined as:
l(k)＝f(k)g(k)   (18)l(k)=f(k)g(k) (18)
定义改进的最小奇异值变化率指标为：The improved minimum singular value change rate indicator is defined as:
o(k)＝f(k)h(k)   (19)o(k)=f(k)h(k) (19)
本发明中，步骤(3)是通过以下方式实现的：In the present invention, step (3) is realized in the following manner:
3.1确定指标权重3.1 Determine indicator weights
将每个节点的指标值输入堆叠自动编码器神经网络中确定各指标权重。Input the index value of each node into the stacked autoencoder neural network to determine the weight of each index.
3.2建立节点综合脆弱性指标3.2 Establish node comprehensive vulnerability index
将步骤(2)中基于电网拓扑结构提出的网络凝聚度指标，网络效能变化率指标，节点电气介数指标，以及针对节点退出运行后电网的状态提出的改进的潮流冲击熵指标与改进的最小奇异值变化率指标综合起来，定义节点k的综合脆弱性指标为：The network cohesion index, the network efficiency change rate index, the node electrical betweenness index proposed in step (2) based on the power grid topology structure, and the improved power flow shock entropy index proposed for the state of the power grid after the node is out of operation, and the improved minimum The singular value change rate indicators are combined to define the comprehensive vulnerability index of node k as:
θ(k)＝ω bb(k)+ω cc(k)+ω ee(k)+ω ll(k)+ω oo(k)    (20) θ(k)=ω b b(k)+ω c c(k)+ω e e(k)+ω l l(k)+ω o o(k) (20)
式中：ω代表各指标权重。In the formula: ω represents the weight of each indicator.
本发明中，步骤4是通过以下方式实现的：In the present invention, step 4 is realized in the following ways:
步骤3中建立的节点综合脆弱性指标在确定的方法下进行计算仅能得到一个确定性的指标计算值，无法体现新能源出力和负荷在电网正常运行下***随机波动的不确定性。因此，为计及新能源出力与负荷不确定性的影响，采用概率潮流的方法对节点综合脆弱性指标进行计算。拉丁超立方采样法作为一种求解概率潮流的模拟法，其计算效率较蒙特卡洛抽样法由极大的提高，且能够提供输出随机变量的丰富信息，从而使得计算的综合脆弱性指标从统计上更好的满足要求。The node comprehensive vulnerability index established in step 3 can only be calculated with a certain method to obtain a deterministic index calculation value, which cannot reflect the uncertainty of the random fluctuation of the new energy output and load under the normal operation of the power grid. Therefore, in order to take into account the influence of new energy output and load uncertainty, the probabilistic power flow method is used to calculate the node comprehensive vulnerability index. As a simulation method for solving probabilistic power flow, Latin hypercube sampling method has greatly improved computational efficiency compared with Monte Carlo sampling method, and can provide rich information of output random variables, so that the calculated comprehensive vulnerability index can be calculated from statistical to better meet the requirements.
4.1拉丁超采样法4.1 Latin Supersampling
拉丁超立方采样法的主要思想基于逆函数转换方法。该方法分成以下两步：①采样：对每个输入随机变量进行采样，确保随机分布区域能够被采样点完全覆盖。②排列：改变各随机变量采样值的排列顺序，使每个随机变量采样值的相关性趋于最小。The main idea of the Latin hypercube sampling method is based on the inverse function transformation method. The method is divided into the following two steps: ① Sampling: Sampling each input random variable to ensure that the random distribution area can be completely covered by the sampling points. ② Arrangement: Change the arrangement order of the sampling values of each random variable to minimize the correlation of the sampling values of each random variable.
4.1.1采样4.1.1 Sampling
假设待求问题中有K个随机输入变量，X k(k＝1,2,...,K)为其中任意一个随机输入变量，其累积概率分布函数可表示为Y k＝F k(X k)，设X kn为第k个随机变量的第n个采样值，则其可以表示为： Assuming that there are K random input variables in the problem to be solved, X k (k=1,2,...,K) is any one of the random input variables, and its cumulative probability distribution function can be expressed as Y k =F k (X k ), let X kn be the nth sampling value of the kth random variable, then it can be expressed as:
Figure PCTCN2021109973-appb-000046
Figure PCTCN2021109973-appb-000046
式中，n＝1,2,...,N。N为采样总次数。In the formula, n=1,2,...,N. N is the total number of sampling times.
当所有的随机输入变量采样结束后，把每个随机变量的采样值随机排列为矩阵的一行，则所有的采样值形成一个K×N阶的采样矩阵，表示为：After all random input variables are sampled, the sampled values of each random variable are randomly arranged into a row of the matrix, then all the sampled values form a K×N-order sampling matrix, which is expressed as:
Figure PCTCN2021109973-appb-000047
Figure PCTCN2021109973-appb-000047
由于上述采样矩阵中的元素是随机排列的，其每个随机变量采样值之间的相关性是随机的、不可控的。所以，还需要通过排列来降低采样矩阵中每个随机变量之间的相关性。Since the elements in the above sampling matrix are randomly arranged, the correlation between the sampling values of each random variable is random and uncontrollable. Therefore, it is also necessary to reduce the correlation between each random variable in the sampling matrix by permutation.
4.1.2基于Cholesky分解的排序方法4.1.2 Sorting method based on Cholesky decomposition
4.1.2.1初始化排列矩阵L KN，它的每一行由整数1，2，...，N的随机排列组成。排列矩阵L KN是一个K×N阶的矩阵，其每一行的元素值代表采样矩阵X KN对应行元素的排列位置。 4.1.2.1 Initialize the permutation matrix L KN , each row of which consists of a random permutation of integers 1, 2, ..., N. The arrangement matrix L KN is a K×N order matrix, and the element value of each row represents the arrangement position of the corresponding row element of the sampling matrix X KN .
4.1.2.2计算排列矩阵L KN各行之间的相关系数矩阵ρ L,ρ L为K×K阶矩阵，可表示为： 4.1.2.2 Calculate the correlation coefficient matrix ρ L between the rows of the arrangement matrix L KN , ρ L is a K×K order matrix, which can be expressed as:
Figure PCTCN2021109973-appb-000048
Figure PCTCN2021109973-appb-000048
式中ρ ij为排列矩阵L KN第i行与第j行之间的相关系数。ρ ij由下式计算可得： where ρ ij is the correlation coefficient between the i-th row and the j-th row of the arrangement matrix L KN . ρ ij can be calculated by the following formula:
Figure PCTCN2021109973-appb-000049
Figure PCTCN2021109973-appb-000049
式中：L ik为排列矩阵L KN第i行第k列元素的值，
Figure PCTCN2021109973-appb-000050
为排列矩阵L KN第i行元素的均值。 In the formula: L ik is the value of the element in the i-th row and the k-th column of the arrangement matrix L KN ,
Figure PCTCN2021109973-appb-000050
is the mean of the elements in the ith row of the permutation matrix L KN .
4.1.2.3可以证明相关系数矩阵ρ L为正定对称矩阵，所以用Cholesky分解法对其进行分解可以得到一个实数的非奇异下三角矩阵D,并且其满足DD T＝ρ L。 4.1.2.3 It can be proved that the correlation coefficient matrix ρ L is a positive definite symmetric matrix, so a real non-singular lower triangular matrix D can be obtained by decomposing it by the Cholesky decomposition method, and it satisfies DD T =ρ L .
4.1.2.4由于D是非奇异的，所以其逆矩阵存在，结合原排列矩阵L KN可以构造出一个新的排列矩阵G KN。 4.1.2.4 Since D is non-singular, its inverse matrix exists, and a new permutation matrix G KN can be constructed by combining the original permutation matrix L KN .
G KN＝D -1L KN   (25) G KN = D -1 L KN (25)
4.1.2.5用排列矩阵G KN的元素从大到小的排列顺序来指示采样矩阵X KN中元素的排列位置，对采样矩阵X KN中元素进行重新排列。 4.1.2.5 The arrangement order of the elements of the arrangement matrix G KN from large to small is used to indicate the arrangement position of the elements in the sampling matrix X KN , and the elements in the sampling matrix X KN are rearranged.
4.2根据步骤(1)建立的DG随机出力模型和负荷随机模型，利用拉丁超立方采样法进行随机抽样，抽样的结果为随机负荷和可再生能源的有功功率，将抽样结果带入式(17)潮流方程进行潮流计算可求得节点电压和支路潮流，最后根据抽样结果、节点电压与支路潮流可计算出各节点的综合脆弱性指标值。4.2 According to the DG random output model and load random model established in step (1), random sampling is carried out using the Latin hypercube sampling method. The sampling results are the active power of random loads and renewable energy, and the sampling results are brought into formula (17) The node voltage and branch power flow can be obtained by the power flow calculation of the power flow equation. Finally, the comprehensive vulnerability index value of each node can be calculated according to the sampling results, node voltage and branch power flow.
4.3由步骤4.2的拉丁超立方采样法可得到设定采样规模内节点综合脆弱性指标值，进而可以获取每个节点综合脆弱性指标值的具体分布，根据等概率准则能够计算出各节点的综合脆弱性指标值的上限值、期望值与下限值。上下限值与期望值共同构成了各节点的综合脆弱性指标值的取值区间，从而以区间形式代替确定值形式来反映各节点的脆弱性水平。4.3 The comprehensive vulnerability index value of nodes within the set sampling scale can be obtained by the Latin hypercube sampling method in step 4.2, and then the specific distribution of the comprehensive vulnerability index value of each node can be obtained, and the comprehensive vulnerability index value of each node can be calculated according to the equal probability criterion. Upper limit, expected value and lower limit of vulnerability index value. The upper and lower limit values and the expected value together constitute the value range of the comprehensive vulnerability index value of each node, so the vulnerability level of each node is reflected by the interval form instead of the definite value form.
4.4为了表征在不确定情况下各个节点的脆弱性水平，需要采用基于布尔矩阵的三参数区间数排序方法对所求的区间形式的综合脆弱性指标进行排序。4.4 In order to characterize the vulnerability level of each node under uncertainty, it is necessary to use the three-parameter interval number ranking method based on Boolean matrix to rank the comprehensive vulnerability indicators in the form of intervals.
4.4.1设任意的两个区间记
Figure PCTCN2021109973-appb-000051
为俩区间数，令
Figure PCTCN2021109973-appb-000052
4.4.1 Let any two interval notation
Figure PCTCN2021109973-appb-000051
be two interval numbers, let
Figure PCTCN2021109973-appb-000052
则
Figure PCTCN2021109973-appb-000053
but
Figure PCTCN2021109973-appb-000053
4.4.2构造布尔矩阵E＝(e ij) m×m，称E为区间数a 1,a 2,...,a m的排序矩阵，其中：
Figure PCTCN2021109973-appb-000054
4.4.2 Construct a Boolean matrix E=(e ij ) m×m , and call E the sorting matrix of interval numbers a 1 , a 2 ,...,am, where:
Figure PCTCN2021109973-appb-000054
4.4.3令
Figure PCTCN2021109973-appb-000055
得到排序向量λ＝(λ 1,λ 2,...λ n). 4.4.3 Order
Figure PCTCN2021109973-appb-000055
Get the sorted vector λ=(λ 1 ,λ 2 ,...λ n ).
4.4.4根据λ i的大小对区间数进行排序，确定每个节点的脆弱性水平，从而辨识出脆弱节点。 4.4.4 Sort the interval number according to the size of λ i to determine the vulnerability level of each node, so as to identify the vulnerable node.
如图4所示，本发明采用标准IEEE39节点***进行算例验证，序号1-39表示第1-39个节点，在节点21处接入额定容量为100MW的光伏。光照强度服从Beta分布，形状参数k′＝0.2274，c′＝1.2995。负荷服从正态分布，均值取***负荷额定功率，方差则取均值的20％。As shown in FIG. 4 , the present invention adopts the standard IEEE39 node system for calculation example verification, and the serial numbers 1-39 represent the 1-39th nodes, and the photovoltaic with a rated capacity of 100MW is connected to the node 21 . The light intensity follows a Beta distribution, with shape parameters k'=0.2274 and c'=1.2995. The load obeys the normal distribution, the mean value takes the rated power of the system load, and the variance takes 20% of the mean value.
经拉丁超立方采样抽样出2000组光伏发电的功率与负荷的有功和无功功率，分别将采样结果带入配电网数据，进行潮流计算。计算出节点度数、网络效能变化率、网络凝聚度，改进的潮流冲击熵及改进的最小奇异值变化率这5项指标，并且将数据带入自动编码器神经网络计算出各指标权重值。进一步的，计算出配电网综合脆弱性指标值。The power of 2000 groups of photovoltaic power generation and the active and reactive power of the load are sampled by Latin hypercube sampling, and the sampling results are respectively brought into the distribution network data for power flow calculation. The five indexes of node degree, network efficiency change rate, network cohesion, improved power flow shock entropy and improved minimum singular value change rate are calculated, and the data is brought into the autoencoder neural network to calculate the weight value of each index. Further, the comprehensive vulnerability index value of the distribution network is calculated.
此时，计算的配电网综合脆弱性指标值的上限值、期望值与下限值如图5所示。At this time, the upper limit, expected value and lower limit of the calculated comprehensive vulnerability index value of the distribution network are shown in Figure 5.
在此基础将上下限值与期望值共同构成了各节点的综合脆弱性指标值的取值区间，在此基础上采用基于布尔矩阵的三参数区间数排序方法辨识脆弱节点。辨识结果如下表所示：On this basis, the upper and lower limit values and the expected value together constitute the value range of the comprehensive vulnerability index value of each node. On this basis, the three-parameter interval number sorting method based on Boolean matrix is used to identify vulnerable nodes. The identification results are shown in the following table:
节点脆弱性评估结果比较Comparison of Node Vulnerability Assessment Results
Figure PCTCN2021109973-appb-000056
Figure PCTCN2021109973-appb-000056
Figure PCTCN2021109973-appb-000057
Figure PCTCN2021109973-appb-000057
由上表可知，3种评估方法的结果具有良好的一致性，即用本专利的所提方法得到的排名前十的节点有6种与前3种方法重合，如节点4、16和17。这些节点都处于电网的核心位置，是重要的输电枢纽，一旦发生故障而退出运行，会是***发生较大的潮流转移，对线路的冲击极大，容易引起线路过载，甚至会使电网进入自组织临界状态，引发灾变。It can be seen from the above table that the results of the three evaluation methods have good consistency, that is, six of the top ten nodes obtained by the method proposed in this patent coincide with the top three methods, such as nodes 4, 16 and 17. These nodes are located at the core of the power grid and are important power transmission hubs. Once a fault occurs and the operation is withdrawn, a large power flow transfer will occur in the system, which will have a great impact on the line, easily cause the line to overload, and even cause the power grid to enter its own power grid. Organizing a critical state, causing catastrophe.
并且，本专利方法考虑了新能源接入后对脆弱性的影响，反映出新能源并网后不确定性带来的就近影响较大原则，如21号节点接入新能源之后，其就近的16、22号节点及其后续影响的节点就会变得非常脆弱。Moreover, this patented method takes into account the impact of new energy on vulnerability after the connection of new energy, reflecting the principle that the uncertainty brought about by the connection of new energy to the grid will have a greater impact on the nearest neighbor. For example, after node 21 is connected to new energy, Nodes 16, 22 and their subsequent affected nodes will become very vulnerable.
本发明方法基于配电网结构和状态两个方面构建了科学全面的脆弱性评价指标，能够在计及新能源接入配电网的情况下，准确和快速地辨识电网中的脆弱节点，对配电网脆弱性进行有效评估，从而降低事故安全风险的发生几率。The method of the invention constructs a scientific and comprehensive vulnerability evaluation index based on the structure and state of the distribution network, and can accurately and quickly identify the vulnerable nodes in the power grid under the condition that the new energy is connected to the distribution network. The vulnerability of the distribution network can be effectively assessed, thereby reducing the probability of accident safety risks.
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明技术原理的前提下，还可以做出若干改进和变形，这些改进和变形也应视为本发明的保护范围。The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principle of the present invention, several improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (7)

  1. 一种计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，A method for identifying vulnerable nodes in an active distribution network considering the impact of new energy, characterized in that:
    对预先构建的DG随机出力模型和负荷随机模型进行抽样，获取新能源和负荷的随机出力数据，根据随机出力数据确定随机出力矩阵，对随机出力矩阵进行排列，得到使每个随机变量采样值的相关性趋于最小的排列矩阵；Sampling the pre-built DG random output model and load random model, obtain the random output data of new energy and load, determine the random output matrix according to the random output data, and arrange the random output matrix to obtain the sampling value of each random variable. The permutation matrix whose correlation tends to be the smallest;
    将排列矩阵中的数据输入到预先构建的节点脆弱性指标评估体系模型，得到节点综合脆弱性指标；所述预先构建的节点脆弱性指标评估体系模型为综合考虑主动配电网固有拓扑结构与DG的不确定性，并兼顾节点故障的概率及其退出运行后由于网络拓扑结构和电力潮流变化对***造成的影响的计算模型；The data in the arrangement matrix is input into the pre-built node vulnerability index evaluation system model, and the node comprehensive vulnerability index is obtained; the pre-built node vulnerability index evaluation system model is a comprehensive consideration of the inherent topology structure of the active distribution network and the DG The uncertainty of the node, taking into account the probability of node failure and the calculation model of the impact of the network topology and power flow changes on the system after it is out of operation;
    利用基于布尔矩阵的三参数区间数排序方法结合节点综合脆弱性指标辨识出脆弱节点。Vulnerable nodes are identified by the three-parameter interval number sorting method based on Boolean matrix combined with node comprehensive vulnerability index.
  2. 根据权利要求1所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，所述预先构建的节点脆弱性指标评估体系模型包括：结构脆弱性指标计算模型、状态脆弱性指标计算模型以及指标权重计算模型；The method for identifying vulnerable nodes in an active distribution network considering the impact of new energy sources according to claim 1, wherein the pre-built node vulnerability index evaluation system model includes: a structural vulnerability index calculation model, a state vulnerability Index calculation model and index weight calculation model;
    所述结构脆弱性指标计算模型，用于计算网络凝聚度、网络效能变化率和节点电气介数；The structural vulnerability index calculation model is used to calculate network cohesion, network efficiency change rate and node electrical betweenness;
    所述状态脆弱性指标计算模型，用于计算改进的潮流冲击熵和改进的最小奇异值变化率；The state vulnerability index calculation model is used to calculate the improved power flow shock entropy and the improved minimum singular value change rate;
    所述指标权重计算模型，用于计算网络凝聚度、网络效能变化率、节点电气介数、改进的潮流冲击熵和改进的最小奇异值变化率的权重。The index weight calculation model is used to calculate the weights of network cohesion, network efficiency change rate, node electrical betweenness, improved power flow impulse entropy and improved minimum singular value change rate.
  3. 根据权利要求2所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，所述网络凝聚度通过下式得到：The method for identifying vulnerable nodes in an active distribution network considering the impact of new energy sources according to claim 2, wherein the network cohesion is obtained by the following formula:
    Figure PCTCN2021109973-appb-100001
    Figure PCTCN2021109973-appb-100001
    式中，a(k)为采用节点收缩法对节点k进行收缩后网络中的平均最短电气距离，n′为收缩后网络中节点的个数；In the formula, a(k) is the average shortest electrical distance in the network after the node k is contracted by the node contraction method, and n′ is the number of nodes in the network after the contraction;
    Figure PCTCN2021109973-appb-100002
    Figure PCTCN2021109973-appb-100002
    式中，d ij为收缩后网络中任意俩节点i和节点j之间的最短电气距离，V表示网络中所有节点的集合； In the formula, d ij is the shortest electrical distance between any two nodes i and j in the contracted network, and V represents the set of all nodes in the network;
    所述网络效能变化率通过下式得到：The network performance change rate is obtained by the following formula:
    Figure PCTCN2021109973-appb-100003
    Figure PCTCN2021109973-appb-100003
    式中，c(k)为节点k失效前后网络效能的变化率，C(k)为节点k失效后的网络效能，C为网络能效；In the formula, c(k) is the change rate of the network efficiency before and after the failure of node k, C(k) is the network efficiency after the failure of node k, and C is the network energy efficiency;
    Figure PCTCN2021109973-appb-100004
    Figure PCTCN2021109973-appb-100004
    式中，G和D分别为发电机和负荷节点集合，N G和N D分别为发电机和负荷节点的个数，min(P Gi′,P Dj′)为发电机负荷节点对(i′,j′)中有功功率较小值，P Gi′为发电机节点i′的有功功率，P Di′为负荷节点j′的有功功率； In the formula, G and D are the generator and load node sets, respectively, N G and N D are the number of generator and load nodes, respectively, min(P Gi′ , P Dj′ ) is the generator load node pair (i′ ) ,j′) the smaller value of the active power, P Gi′ is the active power of the generator node i′, P Di′ is the active power of the load node j′;
    所述节点电气介数通过下式得到：The electrical betweenness of the node is obtained by the following formula:
    Figure PCTCN2021109973-appb-100005
    Figure PCTCN2021109973-appb-100005
    式中，e(k)为节点k的电气介数，
    Figure PCTCN2021109973-appb-100006
    为在发电机负荷节点对(i′,j′)中注入单位功率，支路l上的潮流承载量，E(k)为在发电机负荷节点对(i′,j′)中注入单位功率时节点k的潮流变化量，Ω k为与节点k直接相连的线路集合。     where e(k) is the electrical betweenness of node k,
    Figure PCTCN2021109973-appb-100006
    In order to inject unit power into the generator load node pair (i', j'), the load flow capacity on branch l, E(k) is the unit power injected into the generator load node pair (i', j') is the power flow variation of node k at time, and Ω k is the set of lines directly connected to node k.
  4. 根据权利要求2所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，The method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy sources according to claim 2, characterized in that:
    所述改进的潮流冲击熵通过下式得到：The improved power flow shock entropy is obtained by the following formula:
    l(k)＝f(k)g(k)l(k)=f(k)g(k)
    式中，l(k)为改进的潮流冲击熵，f(k)为节点故障风险因子，g(k)为节点k的潮流冲击熵，In the formula, l(k) is the improved power flow shock entropy, f(k) is the node failure risk factor, g(k) is the power flow shock entropy of node k,
    f(k)＝e αF(k) f(k)=e αF(k)
    式中，α为风险加权系数，F(k)为节点k电压越限的概率，In the formula, α is the risk weighting coefficient, F(k) is the probability that the node k voltage exceeds the limit,
    Figure PCTCN2021109973-appb-100007
    Figure PCTCN2021109973-appb-100007
    式中，
    Figure PCTCN2021109973-appb-100008
    为节点k电压越上限的次数， N(k)为节点k电压越下限的次数，N为采样总次数     In the formula,
    Figure PCTCN2021109973-appb-100008
    is the number of times the node k voltage exceeds the upper limit, N (k) is the number of times the node k voltage exceeds the lower limit, and N is the total number of sampling times
    Figure PCTCN2021109973-appb-100009
    Figure PCTCN2021109973-appb-100009
    式中，G j(k)为节点k退出运行后对线路j造成的潮流冲击率，M为***中总的支路数， In the formula, G j (k) is the impact rate of power flow to line j after node k is out of operation, M is the total number of branches in the system,
    Figure PCTCN2021109973-appb-100010
    Figure PCTCN2021109973-appb-100010
    式中，ΔP j(k)为节点k因故障退出运行后对线路j造成的潮流冲击量； In the formula, ΔP j (k) is the power flow impact on line j after node k is out of operation due to a fault;
    Figure PCTCN2021109973-appb-100011
    Figure PCTCN2021109973-appb-100011
    式中，P′ j为节点k故障后线路j的当前潮流，
    Figure PCTCN2021109973-appb-100012
    为线路j的初始潮流；所述改进的最小奇异值变化率通过下式得到：     where P'j is the current power flow of line j after node k fails,
    Figure PCTCN2021109973-appb-100012
    is the initial power flow of line j; the improved minimum singular value change rate is obtained by the following formula:
    o(k)＝f(k)h(k)o(k)=f(k)h(k)
    式中，o(k)为改进的最小奇异值变化率，h(k)为最小奇异值变化率；In the formula, o(k) is the improved minimum singular value change rate, h(k) is the minimum singular value change rate;
    Figure PCTCN2021109973-appb-100013
    Figure PCTCN2021109973-appb-100013
    式中，
    Figure PCTCN2021109973-appb-100014
    为节点k移除前***潮流雅可比矩阵的最小奇异值，δ i,min(k)为节点k移除后***潮流雅可比矩阵的最小奇异值。     In the formula,
    Figure PCTCN2021109973-appb-100014
    is the smallest singular value of the Jacobian matrix of the system power flow before node k is removed, and δ i,min (k) is the smallest singular value of the Jacobian matrix of the system power flow after node k is removed.
  5. 根据权利要求2所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，所述指标权重计算模型的计算过程为：The method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy sources according to claim 2, wherein the calculation process of the index weight calculation model is:
    将每个节点的网络凝聚度、网络效能变化率、节点电气介数、改进的潮流冲击熵输入堆叠自动编码器神经网络中确定各指标权重。The network cohesion of each node, the change rate of network efficiency, the electrical betweenness of the node, and the improved power flow shock entropy are input into the stacked autoencoder neural network to determine the weight of each index.
  6. 根据权利要求1所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，所述利用基于布尔矩阵的三参数区间数排序方法结合所述各节点的综合脆弱性指标辨识出脆弱节点的过程包括：The method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy sources according to claim 1, wherein the method of using a three-parameter interval number sorting method based on Boolean matrix is combined with the comprehensive vulnerability index identification of each node. The process of identifying vulnerable nodes includes:
    根据各节点的综合脆弱性指标的值获取每个节点综合脆弱性指标的具体分 布，根据等概率准则计算出各节点的综合脆弱性指标的上限值、期望值与下限值，上限值、下限值与期望值共同构成各节点的综合脆弱性指标的取值区间，得到以区间形式表示的综合脆弱性指标；The specific distribution of the comprehensive vulnerability index of each node is obtained according to the value of the comprehensive vulnerability index of each node, and the upper limit, expected value and lower limit of the comprehensive vulnerability index of each node are calculated according to the equal probability criterion. The lower limit value and the expected value together constitute the value interval of the comprehensive vulnerability index of each node, and the comprehensive vulnerability index expressed in the form of interval is obtained;
    采用基于布尔矩阵的三参数区间数排序方法对以区间形式表示的综合脆弱性指标进行排序，辨识出脆弱节点。The three-parameter interval number sorting method based on Boolean matrix is used to sort the comprehensive vulnerability indicators in the form of intervals, and identify vulnerable nodes.
  7. 根据权利要求6所述的计及新能源影响的主动配电网脆弱节点辨识方法，其特征在于，所述采用基于布尔矩阵的三参数区间数排序方法对以区间形式表示的综合脆弱性指标进行排序，辨识出脆弱节点的过程包括：The method for identifying vulnerable nodes in an active distribution network that takes into account the impact of new energy sources according to claim 6, wherein the comprehensive vulnerability index expressed in the form of intervals is analyzed by a three-parameter interval number sorting method based on Boolean matrix. The process of sorting and identifying vulnerable nodes includes:
    设节点i的综合脆弱性指标的区间数为
    Figure PCTCN2021109973-appb-100015
    节点j的综合脆弱性指标的区间数
    Figure PCTCN2021109973-appb-100016
    为节点i的综合脆弱性指标下限值，
    Figure PCTCN2021109973-appb-100017
    为期望值，
    Figure PCTCN2021109973-appb-100018
    为上限值；
    Figure PCTCN2021109973-appb-100019
    为节点j的综合脆弱性指标下限值，
    Figure PCTCN2021109973-appb-100020
    为期望值，
    Figure PCTCN2021109973-appb-100021
    为上限值；     Let the interval number of the comprehensive vulnerability index of node i be
    Figure PCTCN2021109973-appb-100015
    The number of intervals of the comprehensive vulnerability index of node j
    Figure PCTCN2021109973-appb-100016
    is the lower limit of the comprehensive vulnerability index of node i,
    Figure PCTCN2021109973-appb-100017
    is the expected value,
    Figure PCTCN2021109973-appb-100018
    is the upper limit value;
    Figure PCTCN2021109973-appb-100019
    is the lower limit value of the comprehensive vulnerability index of node j,
    Figure PCTCN2021109973-appb-100020
    is the expected value,
    Figure PCTCN2021109973-appb-100021
    is the upper limit value;
    令
    Figure PCTCN2021109973-appb-100022
    则a i大于a j的概率
    Figure PCTCN2021109973-appb-100023
    令c ij＝P(a i＞a j)；     make
    Figure PCTCN2021109973-appb-100022
    then the probability that a i is greater than a j
    Figure PCTCN2021109973-appb-100023
    Let c ij =P(a i >a j );
    构造布尔矩阵E＝(e ij) m×m，E为区间数a 1,a 2,…,a m的排序矩阵，其中， Construct a Boolean matrix E=(e ij ) m×m , where E is the sorting matrix of interval numbers a 1 , a 2 ,...,am, where,
    Figure PCTCN2021109973-appb-100024
    Figure PCTCN2021109973-appb-100024
    令
    Figure PCTCN2021109973-appb-100025
    得到排序向量λ＝(λ 1,λ 2,…λ n)，λ i是节点i脆弱性水平的排序量值；     make
    Figure PCTCN2021109973-appb-100025
    Obtain the ranking vector λ=(λ 1 ,λ 2 ,...λ n ), and λ i is the ranking value of the vulnerability level of node i;
    根据λ i的大小对区间数进行排序，确定每个节点的脆弱性水平，辨识出脆弱节点。 The number of intervals is sorted according to the size of λ i , the vulnerability level of each node is determined, and the vulnerable node is identified.
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