CN114325234A - Power distribution network fault positioning method based on genetic optimization algorithm - Google Patents
Power distribution network fault positioning method based on genetic optimization algorithm Download PDFInfo
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Abstract
The invention relates to a power distribution network fault positioning method based on a genetic optimization algorithm, which comprises the following steps of: (1) constructing a switching function and a fitness function in a genetic algorithm; (2) the feeder line terminal respectively acquires the acquisition information of the corresponding area and the user side; (3) the feeder line terminal judges whether a fault occurs according to the acquired information, if so, the fault information is recorded and sent to a distribution network automatic control center through the regional substation, the step (4) is executed, and if not, the step (2) is returned; (4) and (3) the distribution network automation control center obtains fault equipment by adopting the genetic algorithm in the step (1) according to the fault information to realize fault positioning. The invention has the advantages that: the concept of referring to the user side information for the first time improves the automation reliability and real-time performance of the power distribution network and reduces the time for fault location and isolation.
Description
Technical Field
The invention relates to the field of power distribution network automation of a power system, relates to a power distribution network fault positioning method, and particularly relates to a power distribution network fault positioning method based on a genetic optimization algorithm.
Background
With the rapid development of society and the improvement of economic level, society and users have higher and higher requirements on power supply reliability. Therefore, distribution automation is also receiving more and more attention as one of important means for improving power supply reliability of a power system, and feeder automation is one of main functions of distribution automation, namely a feeder terminal monitors the operation condition of a power grid in real time. Therefore, the research on the fault location and isolation technology of the power distribution network has very important significance for improving the power supply reliability of the power distribution network.
At present, researches on a power distribution network fault section positioning algorithm can be mainly divided into two categories, and the two categories are judged based on fault overcurrent information reported by a feeder terminal: one is a matrix operation type power distribution network fault section positioning algorithm which combines a topological structure of a power distribution network according to fault overcurrent information reported by feeder terminal equipment; the other type is a fault section positioning method taking an artificial intelligence algorithm as a mathematical model. Before, due to the imperfection of hardware facilities, the information at the user side has no capability of completely collecting the richness mathematical model.
The matrix operation type fault location algorithm is mainly used for completing the location of a fault section of a power distribution network through matrix operation, firstly, a matrix D used for describing a topological structure of the power distribution network is established, then, a matrix G containing all fault information is established, finally, a fault judgment matrix P is obtained after the matrix D and the matrix G are multiplied and normalized, and the fault section is located through the fault judgment matrix P. Practice proves that the method is complex in calculation and cannot accurately position multi-point faults.
The artificial intelligence type fault section positioning algorithm takes artificial intelligence algorithms (such as neural networks, expert systems, genetic algorithms and the like) as a core, and establishes a proper mathematical model by combining fault information uploaded by monitoring terminals at all switch nodes to perform fault section positioning calculation. The fault section positioning algorithm based on the expert system links the topological structure of the power distribution network with a GIS (geographic information system) of the power distribution network, and reasoning and searching are carried out according to certain rules by combining a knowledge base of the expert, so that the problem of the fault position is solved finally. The positioning algorithm has the disadvantages that different knowledge bases are required to be established for different power distribution networks, so that the algorithm is low in adaptability and high in actual popularization difficulty.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a power distribution network fault positioning method based on a genetic optimization algorithm.
The purpose of the invention can be realized by the following technical scheme: a power distribution network fault positioning method based on a genetic optimization algorithm comprises the following steps:
(1) constructing a switching function and a fitness function in a genetic algorithm;
(2) each feeder terminal collects current information of each line and current information of a user side;
(3) the feeder line terminal judges whether a fault occurs according to the acquired information, if so, the fault information is recorded, the fault information is sent to a distribution network automatic control center through a regional substation, the step (4) is executed, and if not, the step (2) is returned;
(4) and determining a final fault area according to the information uploaded by the feeder line terminal through a genetic optimization algorithm, and realizing fault positioning so as to isolate the fault area.
Further, in step (1), for a feeder line powered by a single power supply or a loop-shaped feeder line in an open loop state, the fitness function is defined as:
wherein IjIs the bypass signal of the jth switch, N is the total number of the section switches, Ij' (x) expected value function of fault violation of jth section switch determined for equipment status information, Ik(x)Is a bypass signal of a subscriber side branch switch'k(x)And the expected value function of the fault state of the kth user determined for the state of the equipment at the user side.
In the step (1), the fitness function is defined as:
x≥2 Ik(x)1 otherwise Ik(x)＝0
Wherein x is the number of overcurrent at the user side.
In the formula: i isjFor the signal of the through-flow of the jth switch, I at fault currentjNo fault current or normal time I ═ 1j0; x represents the state of the device, and the value of X is 1 in the case of a fault current and 0 in the case of a normal state.
Further, in the step (3), the method for judging whether the feeder terminal fails according to the acquired information is to adopt a phase current mutation method, and specifically includes:
calculating phase current abrupt change delta i at time pP：
ΔiP＝||iP-iP-M|-|iP-M-iP-2M||
In the formula, M is the sampling point number of each cycle;
judgment of Δ iPWhether a determination condition is satisfied, the determination condition being Δ iP≥λ×ieIn the formula ieAnd lambda respectively represents the rated current value and the mutation coefficient of the circuit breaker, and then the fault of the area corresponding to the feeder terminal is judged.
Further, when the phase current mutation amount method is adopted for judgment, phase current mutation amounts at a plurality of different moments are continuously calculated, and if the phase current mutation amounts at the plurality of different moments all meet judgment conditions, the fault of the area corresponding to the feeder line terminal is judged.
Further, in the step (3), the feeder terminal simultaneously acquires the on-off state of the circuit breaker through a remote signaling function, and sends the on-off state of the circuit breaker to the distribution network automation control center, and the distribution network automation control center judges whether to execute the step 4) according to the fault information and the on-off state of the circuit breaker.
Further, in the step (4), the obtaining of the faulty device by using the genetic algorithm specifically includes: and calculating the fitness of the switch information containing all the nodes by using the constructed fitness function, selecting individuals with high fitness according to the number of the populations to perform genetic operation, generating the next generation population, and if the termination condition is not met, continuously performing iterative operation on the next generation population until the termination condition is met, wherein the equipment corresponding to the solution with the minimum fitness is the fault equipment.
The invention has the obvious advantages that:
(1) when a power distribution network fault occurs, the fault current signals at each section switch of the power distribution network are collected through the feeder terminal and transmitted to the main station, and the main station rapidly positions the fault by using the genetic algorithm in the invention, so that the power distribution automation level can be improved, and the safety, the stability and the reliability of power distribution can be improved.
(2) The method has the advantages of simple principle, good real-time performance, certain fault tolerance and robustness, and can reduce the calculation time of fault judgment and reduce the time of fault location and isolation by utilizing the parallelism and the global optimization of the genetic algorithm.
(3) The invention first refers the user side as a component of the fitness function, and the accuracy of fault positioning is improved.
(4) The invention has high popularization.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of a system structure corresponding to the method of the present invention.
Fig. 3 is a diagram of an exemplary application of a single loop network for medium and low voltage power distribution.
FIG. 4 is a diagram of the positioning result of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the present embodiment provides a power distribution network fault location method based on a genetic optimization algorithm, including the following steps:
(1) constructing a switching function and a fitness function in a genetic algorithm;
(2) the feeder line terminal respectively acquires the acquisition information of the corresponding area and the user side;
(3) the feeder line terminal judges whether a fault occurs according to the acquired information, if so, the fault information is recorded and sent to a distribution network automatic control center through the regional substation, the step (4) is executed, and if not, the step (2) is returned;
(4) and (3) the distribution network automation control center obtains fault equipment by adopting the genetic algorithm in the step (1) according to the fault information to realize fault positioning.
In the embodiment, the genetic algorithm is adopted for fault location, but the genetic algorithm cannot directly operate parameters, so that 0-1 coding must be performed on the grid fault location problem, for example, overcurrent or equipment fault is represented as 1, and no overcurrent or equipment fault is represented as 0 normally.
The method comprises the steps of utilizing a genetic algorithm to carry out fault location, enabling a switch function to reflect the mapping relation between the equipment state and the switch state, converting the equipment state into the state of whether the switch has fault current or not, and analyzing fault current data of the switch through an evaluation function to search and obtain one or more fault equipment.
In the step (1), for a feeder circuit powered by a single power supply or an annular feeder circuit in an open-loop state, the switching function is defined as:
ICB1(x)＝x(1)∨x(2)∨x(3)∨x(4)；
IS1(x)＝x(2)∨x(3)∨x(4)；
IS2(x)＝x(3)∨x(4)；
IS3(x)＝x(4)。
in the formula, x (i) and x (i) respectively represent the switch function value of the jth switch on the line and the state of the ith device, and in order to keep consistent with the values during encoding, the values of x (i) are as follows: the value when there is a fault current is 1, the value when there is no fault or normal is 0; the symbol "V-shaped" is a logical OR operation.
In the genetic algorithm, a fitness function is a basis for evaluating individual fitness, namely fault equipment which can explain fault current information of each section switch or interconnection switch most can be found according to fault current information which is uploaded to a main station by a feeder terminal. In the step (1), the fitness function is defined as:
in the formula IjIs the bypass signal of the jth switch, N is the total number of the section switches, Ij' (x) expected value function of fault violation of jth section switch determined for equipment status information, Ik(x)Is a bypass signal of a subscriber side branch switch'k(x) The method for judging the fault current of the expected value function of the fault state of the kth user determined for the state of the equipment at the user side is different according to the complexity of the network structure of the feeder circuit, and the research object of the invention is limited to a radiation network or a ring network in open-loop operation. For a single fault, a fault zone may be defined as the area from the first device that detects a feeder fault to the last device that detects a feeder fault. For the judgment of the fault current, two methods are generally adopted at present, wherein the judgment is directly carried out according to the magnitude of an effective value, and the judgment is carried out by adopting a phase current sudden change method. The fault judgment according to the effective value has the problems of long time, difficult determination of fault current threshold value and the like, and the phase current sudden change method is not influenced by a fault position, transition resistance and load current and has higher reliability. In this embodiment, a phase current mutation method is specifically adopted to perform feeder line fault judgment.
The judgment of the fault current by adopting a phase current sudden change method needs to meet the following two conditions: firstly, the sudden change of the phase current exceeds the preset threshold value, and secondly, the value of the phase current always tends to increase. When the power distribution network has a short-circuit fault and the short-circuit current value far exceeds the rated current value of the vacuum circuit breaker, the circuit breaker is switched off within a certain time, and the switching value input interface of the feeder terminal scans the switching on/off information of the circuit breaker and generates a switching displacement information in the data frame sent to the main station.
The judgment by adopting a phase current sudden change method specifically comprises the following steps: calculate p time (t pTs, sample interval Ts is real-time adjusted by microprocessor according to grid frequency, Ts is omitted below)) phase current break amount Δ iP：ΔiP＝||iP-iP-M|-|iP-M-iP-2M | |, where M is the number of sampling points per cycle, the value is generally fixed, and the magnitude of the phase current burst is determined by the current value ik at the time of a cycle k and the current value i at the same time of the previous cycle iP-N and the current value i of the first two cycles at the same timeP-2N co-determination. Judgment of Δ iPWhether a determination condition is satisfied, the determination condition being Δ iP≥λ×ieIn the formula ieAnd lambda respectively represents the rated current value and the mutation coefficient of the circuit breaker, and then the fault of the area corresponding to the feeder terminal is judged.
In order to improve the reliability and accuracy of fault current judgment, when the phase current sudden change quantity method is adopted for judgment, a plurality of phase current sudden change quantities, such as delta i, at different moments are continuously calculatedP,ΔiP+1 and Δ iPAnd +2, if the phase current break variables at the different moments all meet the judgment condition, judging that the area corresponding to the feeder line terminal has a fault.
In actual operation, a plurality of factors interfering fault judgment exist, for example, when a transformer is switched on in an idle state, phase current can be suddenly changed due to surge current generated by an excitation winding of the transformer, but phase current i can be further comparedPAnd iPAnd judging the size and the change trend of the-N. Considering that the nonideal transformer has different influence degrees on current mutation harmonic waves with different frequencies, proper higher harmonic waves can be filtered by a digital filter and then judged. In addition, in the step 3), the feeder terminal can also acquire the on-off state of the circuit breaker through a remote signaling function, and send the on-off state of the circuit breaker to the distribution network automatic control center, and the distribution network automatic control center judges whether to execute the step 4) according to the fault information and the on-off state of the circuit breaker.
In the step 4), the step of obtaining the fault equipment by using a genetic algorithm specifically comprises the following steps: and calculating the fitness of the switch information containing all the nodes by using the constructed fitness function, selecting individuals with high fitness according to the number of the populations according to a certain rule to perform genetic operation, generating the population of the next generation, and if the termination condition is not met, continuously performing iterative operation on the population of the next generation until the termination condition is met, wherein the equipment corresponding to the solution with the minimum fitness is fault equipment. Generally, genetic manipulation includes processes of selection, crossing, and mutation, and the purpose of which is to ensure rapid and accurate convergence of genetic algorithms.
As shown in fig. 2, for a system structure diagram for implementing fault location in this embodiment, each Feeder Terminal (FTU) respectively acquires voltage, current, active power, reactive power, switch position information (opening and closing conditions), storage battery energy storage completion conditions, and the like of a corresponding column switch, and transmits the acquired information to a distribution substation through a communication network, and then to a distribution network automation control center (SCADA) through the communication substation. Meanwhile, feeder terminals at the on-pole (section) switches can also receive switching-on and switching-off commands issued by the SCADA to optimize the operation mode of the distribution network.
When a feeder line has a fault, a feeder terminal acquires fault current and voltage waveforms through a high-speed AD (analog-to-digital) module, calculates the maximum power of the fault, records current waveforms before and after the fault, sends the fault information to a control center, positions a fault section through a related algorithm of the control center, and finally sends a switching-on/switching-off remote control command to each feeder terminal so as to isolate a fault area and recover normal power supply of a non-fault area.
As shown in FIG. 3, CB1For line-feeding circuit breakers, S1，S2，S3Is a section switch; z1、Z2、Z3、Z4Is a device in an electrical distribution network. To obtain an overcurrent signal, each switch is equipped with an FTU, which is analyzed in accordance with the actual situation, in Z4For fault analysis, the genetic algorithm is simulated through matlab, and the result is shown in fig. 4, so that the fault area is accurately found out.
In summary, according to the power distribution network fault location method based on the genetic optimization algorithm, when a power distribution network fault occurs, fault current signals at section switches of the power distribution network and current signals at a user side are collected through the feeder line terminal and transmitted to the main station, and the main station rapidly locates the fault by using the genetic algorithm. The invention can not only improve the distribution automation level, but also increase the safety, stability and reliability of distribution.
Finally, it should be noted that the above embodiments are only for technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention has been described in detail with reference to the best embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (6)
1. A power distribution network fault positioning method based on a genetic optimization algorithm is characterized by comprising the following steps: the method comprises the following steps:
(1) constructing a switching function and a fitness function in a genetic algorithm;
(2) the feeder line terminal respectively acquires the acquisition information of the corresponding area and the user side;
(3) the feeder line terminal judges whether a fault occurs according to the acquired information, if so, the fault information is recorded and sent to a distribution network automatic control center through the regional substation, the step (4) is executed, and if not, the step (2) is returned;
(4) and (3) the distribution network automation control center obtains fault equipment by adopting the genetic algorithm in the step (1) according to the fault information to realize fault positioning.
2. The method for locating the fault of the power distribution network based on the genetic optimization algorithm, according to claim 1, is characterized in that: in the step (1), for a feeder circuit powered by a single power supply or an annular feeder circuit in an open-loop state, the switching function is defined as:
wherein IjIs the bypass signal of the jth switch, N is the total number of the section switches, Ij' (x) expected value function of fault violation of jth section switch determined for equipment status information, Ik(x)For the bypass signal of the subscriber side branch switch,
and the expected value function of the fault state of the kth user determined for the state of the equipment at the user side.
In the step (1), the fitness function is defined as:
x≥2 Ik(x)1 otherwise Ik(x)＝0
Wherein x is the number of overcurrent at the user side;
in the formula: i isjFor the signal of the through-flow of the jth switch, I at fault currentjNo fault current or normal time I ═ 1j0; x represents the state of the device, and the value of X is 1 in the case of a fault current and 0 in the case of a normal state.
3. The method for locating the fault of the power distribution network based on the genetic optimization algorithm is characterized by comprising the following steps of: in the step (3), the method for judging whether the feeder terminal fails according to the acquired information is to adopt a phase current mutation method for judgment, and specifically comprises the following steps:
ΔiP＝||iP-iP-M|-|iP-M-iP-2M||
in the formula, M is the sampling point number of each cycle;
judgment of Δ iPWhether a determination condition is satisfied, the determination condition being Δ iP≥λ×ieIn the formula ieAnd lambda respectively represents the rated current value and the mutation coefficient of the circuit breaker, and then the fault of the area corresponding to the feeder terminal is judged.
4. The power distribution network fault location method based on the genetic optimization algorithm according to claim 3, wherein when the phase current mutation amount method is adopted for judgment, phase current mutation amounts at a plurality of different moments are continuously calculated, and if the phase current mutation amounts at the plurality of different moments all meet judgment conditions, a fault is judged to occur in an area corresponding to a feeder line terminal.
5. The power distribution network fault location method based on the genetic optimization algorithm as claimed in claim 1, wherein in the step (3), the feeder terminal simultaneously collects the switch states of the circuit breakers through a remote signaling function, and sends the switch states of the circuit breakers to a distribution network automation control center, and the distribution network automation control center determines whether to execute the step (4) according to the fault information and the switch states of the circuit breakers.
6. The power distribution network fault location method based on the genetic optimization algorithm according to claim 1, wherein in the step (4), the obtaining of the fault equipment by using the genetic algorithm specifically comprises:
and calculating the fitness of the switch information containing all the nodes by using the constructed fitness function, selecting individuals with high fitness according to the number of the populations to perform genetic operation, generating the next generation population, and if the termination condition is not met, continuously performing iterative operation on the next generation population until the termination condition is met, wherein the equipment corresponding to the solution with the minimum fitness is the fault equipment.
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