CN111756035A - Double-factor robust Bayes power distribution network state estimation method based on uncertainty improvement - Google Patents

Double-factor robust Bayes power distribution network state estimation method based on uncertainty improvement Download PDF

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CN111756035A
CN111756035A CN202010560352.8A CN202010560352A CN111756035A CN 111756035 A CN111756035 A CN 111756035A CN 202010560352 A CN202010560352 A CN 202010560352A CN 111756035 A CN111756035 A CN 111756035A
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徐艳春
刘晓明
谢莎莎
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China Three Gorges University CTGU
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Abstract

The method comprises the steps of establishing a power distribution network system comprising photovoltaic and a fan as a test platform based on an uncertainty improved double-factor robust Bayes power distribution network state estimation method; providing different types of distributed power supplies, accessing uncertainty information in a power distribution network system, and modeling to obtain an information uncertainty model; providing information uncertainty of different measuring devices and modeling to obtain an information uncertainty model of the measuring devices; integrating the uncertainty models into a two-factor uncertainty model; and adopting the input end of the PMU state estimation hybrid measurement model as a state estimation input parameter. The robust Bayes estimation theory is introduced through the two-factor uncertainty model, the Bayes estimation algorithm is improved, and the influence of uncertainty information parameters on power distribution network state estimation can be effectively inhibited through model adjustment and algorithm improvement. The method has the advantages of high estimation result precision, short time consumption, capability of meeting the requirements of intelligent situation perception and real-time state monitoring of the active power distribution network, feasibility and engineering practical value.

Description

Double-factor robust Bayes power distribution network state estimation method based on uncertainty improvement
Technical Field
The invention relates to the technical field of intelligent situation perception and real-time state monitoring of an active power distribution network, in particular to a double-factor robust Bayes power distribution network state estimation method based on uncertainty improvement.
Background
Under the background of new electricity change, policies such as releasing a power generation and utilization plan, promoting clean energy consumption, priority development of clean energy and the like are provided, so that the distributed power supply DG and the new energy power generation become one of main parts of power generation by virtue of the advantages of cleanness, environmental friendliness and the like. Meanwhile, with the gradual maturity of a distributed power generation technology and the continuous development of a power grid technology, a modern power distribution system increasingly tends to be intelligent and highly automatic, renewable energy sources and novel loads are integrated on a large scale and are connected into a power distribution network, the scale and the number of nodes of the power distribution network are increased, and the distributed power supply DG and the novel loads (electric vehicles) are not easily connected, so that bidirectional tide and partial node voltage fluctuation aggravation and other phenomena occur in the power distribution network, and the operation mode, situation perception and measurement configuration of the power distribution network are more complicated and changeable.
The state estimation is used as the core of the intelligent situation perception technology of the power distribution network, the important technical support for maintaining the safe operation of the active power distribution network is provided, meanwhile, the real-time load of a user can be monitored on line, the operation state and parameters of the network at the current moment are obtained, and reliable data guarantee is provided for the aspects of real-time monitoring, scheduling, controlling and fault analysis of a power distribution network system. Although novel high-precision measurement devices such as PMU can provide measurement data with higher precision, the measurement devices cannot be configured on a large scale in a distribution network due to the reasons of technology, economy and the like, so that the state estimation of the active distribution network inevitably has objective gross errors.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for estimating the state of a double-factor robust Bayes power distribution network based on uncertainty improvement, which is characterized in that distributed power supply access information and uncertainty of measurement information of different measurement equipment are modeled, and a Bayes estimation algorithm is improved based on a double-factor uncertainty model formed after uncertainty model integration; the Bayesian estimation algorithm can effectively inhibit the interference of the uncertainty event with certain probability characteristics on the state estimation precision, and can effectively reduce the time consumption of state estimation and improve the state estimation efficiency. The method has the advantages of high estimation result precision, short time consumption, capability of meeting the requirements of intelligent situation perception and real-time state monitoring of the active power distribution network, feasibility and engineering practical value.
The technical scheme adopted by the invention is as follows:
the improved double-factor robust Bayes power distribution network state estimation method based on the uncertainty is characterized by comprising the following steps of:
step 1: building a power distribution network system comprising a photovoltaic system and a fan as a test platform;
step 2: providing different types of distributed power supplies, accessing uncertainty information in a power distribution network system, and modeling to obtain an information uncertainty model; providing information uncertainty of different measuring devices and modeling to obtain an information uncertainty model of the measuring devices;
and step 3: integrating the information uncertainty model and the measuring equipment information uncertainty model in the step 2 into a two-factor uncertainty model;
and 4, step 4: the input end of the PMU state estimation hybrid measurement model is used as a state estimation input parameter, and Gaussian noise is added to simulate random errors introduced by uncertainty information parameters at the input end of the PMU state estimation hybrid measurement model during simulation test.
And 5: the Bayes estimation algorithm is improved by introducing the two-factor uncertainty model in the step 3 into an robust Bayes estimation theory, and the influence of uncertainty information parameters on power distribution network state estimation can be effectively inhibited by model adjustment and algorithm improvement.
The robust Bayesian estimation theory is a method for inhibiting the influence of bad data on parameters to be estimated by improving the prior probability based on an assumption, the probability of observing different data under the given assumption and the observed data by an algorithm, so that the estimation result has better robust performance.
The invention discloses a state estimation method of a double-factor robust Bayes power distribution network based on uncertainty improvement, which has the following technical effects:
1) the uncertainty of the access information of the distributed power supply and the uncertainty of the measurement information between different side devices are considered, the uncertainty information is modeled, the abstract events with probability characteristics are modeled, and the accuracy interference of the uncertainty information on state estimation can be effectively inhibited through model adjustment and an algorithm.
2) And a mixed measurement system consisting of PMU measurement, SCADA measurement system and pseudo measurement is used as an input part of state estimation, so that the observability of the system is ensured.
3) And integrating the uncertainty of the access information of the distributed power supply and the uncertainty model of the measurement information between different measurement devices by adopting a mathematical means to form the double-factor uncertainty so as to optimize a mathematical expression form and improve the calculation efficiency.
4) And the algorithm is improved by adopting an robust Bayes estimation algorithm and based on a two-factor uncertainty model, so that the influence of singular observation values on the estimation parameters can be reduced. The method has good identification effect on bad data, higher estimation precision and good poor resistance.
5) And providing an evaluation standard of the robust estimation under the active power distribution network containing the distributed energy, and reasonably evaluating the method.
6) The invention can model the uncertainty under the condition of considering the uncertainty of the distributed power supply access information and the measurement information among different measurement devices, and improves the robust Bayesian estimation algorithm based on the uncertainty. The method has the advantages of high estimation precision, reliable calculation convergence and less time consumption, can meet the requirement of quick response of real-time situation perception, and has certain engineering practice value.
7) The method for estimating the robust error of the power distribution network in the double-factor robust Bayes area can achieve the following effects when the state estimation is carried out: firstly, the obtained estimation value has reasonability, effectiveness and optimality; when the robust estimation model and the engineering actual have small difference, the obtained estimation value model is less influenced by the difference; thirdly, when the robust estimation model and the engineering actual model have large differences, the obtained estimation value is not greatly influenced; iterative computation is reliable and convergent, time consumption is low, and the requirements of intelligent situation perception and real-time state monitoring quick response of the power distribution network can be met.
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FIG. 1 is a topological diagram of a built test system;
wherein: the system comprises a photovoltaic generator set, a wind driven generator and PMU measuring points.
FIG. 2 is a diagram of a hybrid metrology state estimation voltage amplitude distribution.
FIG. 3 is a hybrid metrology state estimation voltage phase angle distribution plot.
FIG. 4 is a graph of node voltage versus error.
FIG. 5 is a graph of absolute error of node voltage magnitude.
Detailed Description
The improved double-factor robust Bayes power distribution network state estimation method based on the uncertainty is characterized by comprising the following steps of:
step 1: building a power distribution network system comprising a photovoltaic system and a fan as a test platform;
the power distribution network system set up in the step 1 is provided with three feeders, the network comprises 32 branch circuits and 5 interconnection switch branch circuits, the reference voltage of the first section of the network is 12.66KV, the total rated load of the power distribution network is 3.175MW +/-J2.3MVar, the node 1 is a balance node, the nodes 2-33 are PQ type nodes, the distributed power supply access standard refers to the international power commission standard, the peak load distribution of the power distribution network is set to be the rated load distribution of each node, the network topology is shown in figure 1, 6 photovoltaic power generators with independently adjustable active and reactive power are merged into the nodes 4, 6, 17, 19, 21 and 26, and the power factor is 0.95; nodes 12, 14, 25, 28, 32 incorporate 5 wind turbine generators with a power factor of 0.92. The distributed power supply parameter setting is shown in table 1 and table 2:
TABLE 1 wind turbine generator parameter setting table
Figure BDA0002546074610000031
Specification of parameters of the wind turbine generator: prRated power, P, of a wind turbinewThe active power of the wind turbine generator is the active power of the wind turbine generator; v. ofin、vr、voutRespectively representing cut-in wind speed, rated wind speed and cut-off wind speed of the wind turbine; K. c is used to characterize the distribution characteristics of wind speed and average speed.
TABLE 2 photovoltaic cell parameter setting Table
Figure BDA0002546074610000041
Photovoltaic cell group parameter description: a is the area of the photovoltaic array; eta is the photoelectric conversion efficiency of the photovoltaic cell; r represents the illumination intensity, and alpha and Beta are the shape parameters of the Beta distribution. The photovoltaic battery pack is set to operate at a constant power factor, active power is provided only to the power distribution network, and the power factor is set to be 1.
And the state estimation value of the constructed power distribution network system is the voltage amplitude and the phase angle of each node.
Step 2: considering that the uncertainty of the access information of the distributed power supplies can cause certain adverse effects on the estimated state quantity, the access uncertainty information of different types of distributed power supplies in the power distribution network system is provided, modeling is carried out, and an information uncertainty model is obtained.
And considering that uncertainty information of measured data among different measuring devices interferes with the precision of the parameter to be estimated, providing the uncertainty of the information of the different measuring devices and modeling to obtain an uncertainty model of the information of the measuring devices.
The information uncertainty model is as follows:
pk＝pkm+pΔkuk(1)
in the formula (1), pkDenotes the uncertainty of the distributed power access, k denotes the position of the uncertainty parameter in the parameter set, pkmIs pkNominal value of (a), p△kIs the maximum deviation, and p△k>0，ukRepresents the uncertainty of the lead-in parameter, and uk∈[-1,1]。
The uncertainty model of the measuring equipment information is as follows:
zi＝zim+zi△wi(5)
in the formula (5), zimIs ziNominal value of (a), zi△Is the maximum deviation, wiIs uncertainty, and wi∈[-1,1]Z can be expressed as:
z＝zm+Z△w(6)
in the formula (6), zm＝[z1m,…,zMm]T，ZΔ＝diag{z1Δ,…,zMΔWhere w ═ w1,…,wM]TRepresents uncertainty vector of information of measuring equipment, and | | w | | luminance∝≤1。
And step 3: integrating the information uncertainty model and the measuring equipment information uncertainty model in the step 2 into a double-factor uncertainty model through a mathematical means; by simplifying mathematical expression and further optimizing the calculation efficiency, the integrated two-factor uncertainty model is expressed as follows: t is tT＝[wTuT]The residual vector r is expressed by formula (7):
Figure BDA0002546074610000051
in the formula (7), qx＝zm-A0x，Mx＝[zd-Ax]And Ax ═ a1x A2x … Apx]The module value of uncertainty | | t | non-woven counting∝Less than or equal to 1. And 4, step 4: the input end of the PMU state estimation hybrid measurement model is used as a state estimation input parameter, and Gaussian noise is added to simulate random errors introduced by uncertainty information parameters at the input end of the PMU state estimation hybrid measurement model during simulation test.
The data at the input end of the hybrid measurement model containing the PMU state estimation comprises three parts of SCADA system measurement data, PMU measurement data and pseudo measurement redundant data, the observability of the power distribution network is ensured by a multi-source data fusion mode, and the state estimation precision can be further improved by improving the data redundancy. In the simulation test link, on the basis of randomly selecting 10 groups of measured value data at the state estimation input end, 2% Gaussian noise is added for 4 times to simulate random errors caused by uncertainty information parameters, so that the simulation conditions are closer to the actual engineering.
And 5: the two-factor uncertainty model in the step 3 is introduced into an robust Bayes estimation theory, a Bayes estimation algorithm is improved, and the influence of uncertainty information parameters on power distribution network state estimation can be effectively inhibited through model adjustment and algorithm improvement, so that the robust performance is good.
The model established by the robust bayes estimation theory adopted in the step 5 is shown as a formula (19).
Figure BDA0002546074610000052
In the formula (19), musMean value of the power matrix S, Z impedance matrix, S power matrix ∑SRepresents the variance of a power matrix S, P is an N-order matrix formed according to the network topology of a specific power distribution network directed graph, ZeAn impedance matrix representing the topology without the monitoring device node configuration and is a 2(N-L) × 2 Nth order matrix, ZlIs a power matrix including a root node and is a matrix of order 2(L-1) × 2N,
Figure BDA0002546074610000053
represents ZlTranspose of matrix, △ MlAnd the difference matrix represents the effective value and the phase angle of the voltage of the node where the measuring device is located.
Figure BDA0002546074610000054
In formula (18), △ VeAnd △ thetaeFor the difference between the voltage effective value and the phase angle, S represents the power matrix, △ MeSection for indicating no monitoring deviceDot voltage effective value and phase angle difference matrix, PeMatrix, Z, representing the network topology of a directed graph of a distribution network without configuration nodes of monitoring deviceseRepresents the impedance matrix without node configuration of the monitoring device, and PeAnd ZeAre all 2(N-L) × 2 Nth order matrix, ZnRepresenting the impedance matrix derived from the flow equation calculations.
In step 5, the improved two-factor robust bayesian estimation model based on uncertainty is:
Figure BDA0002546074610000061
in the formula (25), musRepresenting the mean value, Z, of the power matrix SeRepresenting an impedance matrix, S representing a power matrix, ∑SRepresents the variance, A, of the power matrix SlIs a 2(L-1) order square matrix, matrix Al1Middle row vector is linearly independent, ZlA 2(L-1) × 2N-th order matrix of measurement data for L configured instrumentation nodes,
Figure BDA0002546074610000062
representation matrix ZlTranspose of (2), △ MeRepresenting the voltage effective value and the phase angle difference matrix, tTRepresenting a two-factor uncertainty matrix, Al1Is a 2(L-1) order square matrix and matrix Al1The middle row vectors are linearly independent of each other,
Figure BDA0002546074610000063
representing transposes of matrices, △ MlIndicating a difference with a monitoring configuration node.
Example (b):
the test system of the present invention is a power distribution network that improves IEEE-33 nodes, the topology of which is shown in figure 1,
the invention takes the access information of the distributed power supply and the uncertainty of the measurement information among different measurement devices into consideration for modeling, and the model is as follows:
although high-precision PMU measurement is introduced in regional power distribution network state estimation, the estimated state quantity has certain uncertainty due to errors among different measurement devices, different distributed power supply access positions and other information. The uncertainty model is as follows:
let p bek＝pkm+pΔkuk(1)
In the formula (1), pkDenotes the uncertainty of the distributed power access, k denotes the position of the uncertainty parameter in the parameter set, pkmIs pkNominal value of (a), p△kIs the maximum deviation, and p△k>0，ukRepresents an uncertainty of a distributed power access information parameter, and uk∈[-1,1]。
The defining function is shown in equation (2):
Figure BDA0002546074610000064
in formula (2), for convenience of expression, it is defined as:
Ak＝p△kGxHk，(k＝1，…，s) (3)
Figure BDA0002546074610000065
order matrix uk T＝[u1,…,us]Represents the uncertainty vector of the access information of the measured distributed power supply, and | | | u | | ventilation∝≤1。Gx，A0And AiAre all linear with x.
Meanwhile, the measurement uncertainty is considered, and the measurement uncertainty is expressed by the following function, as shown in formula (5):
zi＝zim+zi△wi(5)
in the formula (5), zimIs ziNominal value of (a), zi△Is the maximum deviation, wiIs an uncertainty parameter, and wi∈[-1,1]. Z can be expressed as:
z＝zm+Z△w (6)
in the formula (6), zm＝[z1m,…,zMm]T，ZΔ＝diag{z1Δ,…,zMΔWhere w is [ w ]1,…,wM]TRepresents uncertainty vector of measurement information, and | | | w | | non-woven phosphor∝≤1。z1m,…,zMmRespectively representing the nominal values of the measurement values from the node 1 to the node M; z is a radical of1Δ,…,zMΔRespectively representing the maximum deviation of the measurement values from the node 1 to the node M; w is a1,…,wMRespectively, representing the measurement uncertainty parameters for node 1 through node M. The invention provides a concept of a two-factor uncertainty model, and integrates the established model, which is described as follows:
comprehensively considering the access information and the uncertainty factor of the measurement information of the distributed power supply, simplifying the expression by mathematical means, optimizing the calculation time and setting tT＝[wTuT]Representing a two-factor uncertainty vector matrix, its residual vector r can be expressed as:
Figure BDA0002546074610000071
in the formula (7), qx＝zm-A0x，Mx＝[zd-Ax]And Ax ═ a1x A2x … Apx]The module value of uncertainty | | t | non-woven counting∝≤1。
zmA matrix of nominal values representing the measurement information,
Figure BDA0002546074610000072
and is in linear relation with x, x represents the parameter to be estimated, zdRepresents the maximum deviation matrix of the measured information, A ═ A1A2… Ap]Represents a measurement information weight matrix, and A1x A2x… Apx represents the weight corresponding to the measurement information from the node 1 to the node p.
The target model can be expressed as shown in equation (8):
Figure BDA0002546074610000073
scenarios with a state variable x of a two-factor uncertainty parameter p can be passed from a lower bound p by the uncertainty parameter plChange to upper bound puAnd calculating phi (x) and optimizing the phi (x). Thus, the robust estimation model that takes uncertainty into account is actually solving for an estimate x such that, regardless of the change in the uncertainty parameter p, the value of φ (x) should be less than the minimum of the function corresponding to the upper bound of uncertainty.
The invention adopts an robust Bayes estimation algorithm to solve robust estimation, improves the algorithm based on a two-factor uncertainty model, and realizes model adjustment and algorithm suppression, and the method comprises the following steps:
in the radial area power distribution network, the relationship between the effective value of the voltage and the phase angle between the adjacent node i and the node j is shown as the following formula (9) and formula (10):
Figure BDA0002546074610000081
Figure BDA0002546074610000082
in the formula, PjiAnd QjiRespectively representing active power and reactive power of the node j injected into the feeder line section ji; rjiAnd XjiRespectively the resistance and reactance of the feeder line section; vjAnd ViAnd thetajAnd thetaiThe effective voltage value and the phase angle of the node j and the node i respectively.
Ideally, when power loss on the feeder line segment is assumed to be ignored, the effective value of the voltage on the right side of the equal sign in the above equations (9) and (10) can be considered to be 1.0, and therefore the equations (9) and (10) can be approximately expressed as shown in equations (11) and (12):
Figure BDA0002546074610000083
Figure BDA0002546074610000084
in formulae (11) and (12), CiIs a maximum spanning tree with node i as the root node, where PkAnd QkIs the active and reactive power flowing from the current node k.
Writing voltage effective values and phase angle differences for the rest N nodes except the root node (balance node) of the power distribution network, wherein the voltage effective values and the phase angle differences are respectively expressed as shown in formulas (13) and (13):
Figure BDA0002546074610000085
Figure BDA0002546074610000086
wherein △ V represents distribution network voltage effective value difference matrix, △ theta represents phase angle difference matrix, wherein riSince △ V and △ θ are both homogeneous matrices, for ease of calculation, they are integrated as shown in equation (15):
Figure BDA0002546074610000087
wherein S is a power matrix and is expressed as S ═ P1P2… PnQ1Q2… Qn]T；ZnThe impedance matrix derived from the power flow equation calculation is represented as shown in equation (16):
Figure BDA0002546074610000088
in formula (16), R and X are both N-th order diagonal matrices, where the diagonal elements RkkAnd XkkRespectively, resistance and reactance on the feeder line of the root node thereof, wherein the matrix P is an N-order matrix formed according to the network topology of the specific power distribution network directed graph, if and only if the node j ∈ CiWhen it is an element P ij1, otherwise Pij＝0。
When high-precision monitoring devices are placed at L nodes including the root node, the difference value between the voltage effective value and the phase angle of the node where the measuring device is located is shown as a formula (17):
Figure BDA0002546074610000091
in the formula (17), PlAnd ZlThe matrix is 2(L-1) × 2N order, and the effective voltage values and phase angle difference values of the remaining N-L +1 nodes without monitoring devices are shown in the formula (18):
Figure BDA0002546074610000092
in the formula (18), PeAnd ZeIs a 2(N-L) × 2N order matrix.
Establishing a model according to Bayesian theory, and obtaining a state estimation model of nodes without monitoring devices as shown in formula (19):
Figure BDA0002546074610000093
in the formula (19), musMean value of the power matrix S, ∑SThe variance of the power matrix S is indicated.
Considering the influence of different metrology device introduced information on the state estimation, the state estimator model of equation (19) can be expressed as shown in equation (20):
Figure BDA0002546074610000094
state estimation using the difference of information from different monitoring devices, i.e. △ MeRemains unchanged and △ MlTake another node different from △ Ml△ M of node voltage effective value and phase angle difference value of combined and containing L monitoring devicesl1Matrix, △ Ml1And △ MlIs shown in equation (21):
△Ml1＝Al1△Ml(21)
in the formula, Al1Is a square matrix of order 2(L-1) due to △ Ml1And △ MlThe matrix comprises L node voltage effective values and phase angles containing monitoring devices, and the matrix Al1The middle row vectors are linearly independent, hence Al1The matrix is an invertible matrix. From equation (17), the following equation (22) can be derived:
Zl1＝Al1Zl(22)
from equation (20), the state estimator model at this time is obtained, as shown in equation (23):
Figure BDA0002546074610000101
formula (22) is substituted for formula (23), and the result is shown in formula (24):
△Me＝ZetT(μs+∑s(Al1Zl)T[(Al1Zl)∑s(Al1Zl)T]-1·(Al1△Ml-Al1Zlμs)) (24)
the formula is obtained by extracting the formula, expanding the items in brackets, extracting the formula and integrating the formula:
Figure BDA0002546074610000102
due to Al1、
Figure BDA0002546074610000103
And Zl∑s
Figure BDA0002546074610000104
The matrices are all 2(M-1) order reversible square matrices, so equation (25) can be transformed as shown in equation (26):
Figure BDA0002546074610000105
when the measurement configuration node information (quantity, position) is the same, △ M is formed by combining the difference values of different nodes without measurement deviceseAnd the matrix can effectively inhibit the influence on the estimation results of the rest nodes without measurement configuration through a double-factor robust model.
By improving an IEEE-33 node power distribution network system as a test platform of the method, the obtained state estimation voltage amplitude distribution diagram is shown in figure 2, the obtained state estimation voltage phase angle distribution diagram is shown in figure 3, the dispersion of the estimated value distribution result and the power flow true value is small, the estimated value distribution result and the power flow true value are approximately overlapped at partial nodes, and the accuracy is obviously improved. The node voltage relative error ratio is shown in fig. 4, and it is obvious that the total minimum node voltage relative error obtained by robust estimation in the method of the present invention is obtained, and the error curve is relatively stable. The absolute error comparison graph of the node voltage phase angle is shown in fig. 5, and it can be seen that the voltage phase angle error obtained by the method in robust estimation solution has a small degree of dispersion and an obvious improvement on precision.
In order to evaluate the quick response characteristic of the method provided by the invention to the intelligent situation perception of the power distribution network, the method and the Particle Swarm Optimization (PSO) algorithm are used for carrying out state estimation on the improved examples and carrying out comparative analysis on the state estimation of the improved examples based on weighted least square estimation (WLS), the time consumed by the estimation is counted, and the counting result is shown in Table 3.
TABLE 3 time-consuming comparison table for state estimation
Figure BDA0002546074610000106
Figure BDA0002546074610000111
It can be seen that the PSO state estimation is poor in terms of accuracy performance, and the difference between the error maximum and error minimum is also the highest. The method of the invention is superior to PSO state estimation and WLS state estimation in the aspects of improvement of estimation precision and average time consumption estimation. Therefore, the state estimation precision of the regional power distribution network can be remarkably improved, iterative computation is reliable and convergent, time consumption is low, the requirement of quick response can be met, the method can adapt to the situation that different types of distributed power supplies are connected to the power distribution network, and the method is also suitable for state estimation of various mixed measurement systems under the background of big data, so that the method has a certain engineering practice value.

Claims (9)

1. The improved double-factor robust Bayes power distribution network state estimation method based on the uncertainty is characterized by comprising the following steps of:
step 1: building a power distribution network system comprising a photovoltaic system and a fan as a test platform;
step 2: providing different types of distributed power supplies, accessing uncertainty information in a power distribution network system, and modeling to obtain an information uncertainty model; providing information uncertainty of different measuring devices and modeling to obtain an information uncertainty model of the measuring devices;
and step 3: integrating the information uncertainty model and the measuring equipment information uncertainty model in the step 2 into a two-factor uncertainty model;
and 4, step 4: adopting the input end of a PMU state estimation hybrid measurement model as a state estimation input parameter, and adding Gaussian noise to simulate random errors introduced by uncertainty information parameters at the input end of the PMU state estimation hybrid measurement model during simulation test;
and 5: the Bayes estimation algorithm is improved by introducing the two-factor uncertainty model in the step 3 into an robust Bayes estimation theory, and the influence of uncertainty information parameters on power distribution network state estimation can be effectively inhibited by model adjustment and algorithm improvement.
2. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 1, the built power distribution network systems are improved IEEE-33 node power distribution network systems containing fans and photovoltaic, the total active load of the power distribution network system is 90MW, 6 photovoltaic power generation with independently adjustable active power and reactive power are merged into the nodes 4, 6, 17, 19, 21 and 26, and the power factor is 0.95; nodes 12, 14, 25, 28, 32 incorporate 5 wind turbine generators with a power factor of 0.92.
3. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 1, the state estimation of the constructed power distribution network system is the voltage amplitude and the phase angle of each node.
4. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 2, the information uncertainty model is as shown in formula (1):
pk＝pkm+pΔkuk(1)
in the formula (1), pkDenotes the uncertainty of the distributed power access, k denotes the position of the uncertainty parameter in the parameter set, pkmIs pkNominal value of (a), p△kIs the maximum deviation, and p△k>0，ukRepresents the uncertainty of the lead-in parameter, and uk∈[-1,1]。
5. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 2, the uncertainty model of the measuring equipment information is as follows:
zi＝zim+zi△wi(5)
in the formula (5), zimIs ziNominal value of (a), zi△Is the maximum deviation, wiIs uncertainty, and wi∈[-1,1]Z can be expressed as:
z＝zm+Z△w(6)
in the formula (6), zm＝[z1m,…,zMm]T，ZΔ＝diag{z1Δ,…,zMΔTherein ofw＝[w1,…,wM]TRepresents uncertainty vector of information of measuring equipment, and | | w | | luminance∝≤1。
6. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 3, the two-factor uncertainty model is expressed as: t is tT＝[wTuT]The residual vector r is expressed by formula (7):
Figure FDA0002546074600000021
in the formula (7), qx＝zm-A0x，Mx＝[zd-Ax]And Ax ═ a1x A2x … Apx]The module value of uncertainty | | t | non-woven counting∝≤1。
7. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 4, the data at the input end of the hybrid measurement model with the PMU state estimation includes three parts, namely SCADA system measurement data, PMU measurement data and pseudo measurement redundant data, and 4 times of 2% Gaussian noise is added to simulate a random error introduced by the distributed power supply at the input end on the basis of 10 groups of randomly selected measurement data during simulation of the test platform.
8. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in the step 5, a model established by the robust Bayesian estimation theory is shown as a formula (19);
Figure FDA0002546074600000022
Figure FDA0002546074600000023
in the formula (19), musMean value of the power matrix S, Z impedance matrix, S power matrix ∑SRepresenting the variance of a power matrix S, wherein P is an N-order matrix formed according to a specific power distribution network directed graph network topology;
in formula (18), △ VeAnd △ thetae△ M being the difference between the effective value of the voltage and the phase angleeRepresenting a voltage effective value and a phase angle difference matrix.
9. The uncertainty improvement-based two-factor robust bayesian power distribution network state estimation method according to claim 1, wherein: in step 5, the improved two-factor robust bayesian estimation model based on uncertainty is:
Figure FDA0002546074600000031
in the formula (25), musRepresenting the mean value, Z, of the power matrix SeRepresenting an impedance matrix, S representing a power matrix, ∑SRepresents the variance, A, of the power matrix SlIs a 2(L-1) order square matrix, matrix Al1Middle row vector is linearly independent, ZlA 2(L-1) × 2N-th order matrix, △ M, of measurement data for L configured instrumentation nodeseRepresenting the voltage effective value and the phase angle difference matrix, tTRepresenting a two-factor uncertainty matrix.
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