Power distribution network state estimation method and system based on adaptive particle filtering

Abstract

The disclosure provides a power distribution network state estimation method and system based on adaptive particle filtering, comprising: generating particles required by particle filtering based on a random pseudo-Monte Carlo sampling method; based on the obtained current measurement data of the power distribution network system, the previous time state estimation value and the generated particles, the particle filtering algorithm is used to realize the state estimation of the current time of the power distribution network system; wherein, when the power distribution network state estimation is first performed, the number of particles required by the particle filtering adopts a preset reference value; when the subsequent power distribution network state estimation is performed, the number of particles required by the particle filtering is determined based on the preset expected estimation accuracy, the preset reference particle number and the state estimation accuracy of the previous time.

Description

Technical Field

[0001] This disclosure belongs to the field of distribution network state estimation technology, and in particular relates to a distribution network state estimation method and system based on adaptive particle filtering. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Distribution system state estimation is an effective means of sensing the system's operating status and a core function of distribution management systems. It can improve data accuracy by utilizing the redundancy of measurement information, automatically eliminate errors caused by random interference, estimate or predict the system's operating status, and provide a data foundation for other advanced applications. With the large-scale integration of distributed power sources and controllable loads, the uncertainty of the source-grid-load relationship in active distribution networks is increasing. Accurate and efficient distribution network state estimation is crucial for the safe and optimized operation of active distribution networks.

[0004] The inventors discovered that current power distribution system state estimation methods suffer from the following problems: Static state estimation methods use only measurement information from a single time segment to estimate the state of the power distribution network, making them highly sensitive to changes in initial state values ​​and measurement redundancy of the power distribution network, and unsuitable for systems with non-Gaussian noise; Kalman filter-based prediction-assisted estimation methods, although considering the state transition process, are constrained by the Gaussian noise assumption and are difficult to apply to real-world situations where non-Gaussian noise is prevalent; Although particle filtering is an effective state estimation method for nonlinear systems with non-Gaussian noise, its high computational complexity and poor scalability limit its application in real-time state estimation of large-scale power distribution networks. Summary of the Invention

[0005] To address the aforementioned issues, this disclosure provides a distribution network state estimation method and system based on adaptive particle filtering. The scheme employs a random pseudo-Monte Carlo sampling method based on Sobol low-difference sequences to generate particle filtering particles, and proposes an adaptive particle number adjustment method based on the processability analysis of random pseudo-Monte Carlo sampling. This adaptive adjustment of the particle number can effectively balance the accuracy and computational efficiency of distribution network state estimation.

[0006] According to a first aspect of the embodiments of this disclosure, a distribution network state estimation method based on adaptive particle filtering is provided, comprising:

[0007] The particles required for particle filtering are generated based on a random pseudo-Monte Carlo sampling method.

[0008] Based on the acquired current measurement data of the distribution network system, the state estimate of the previous moment, and the generated particles, the particle filtering algorithm is used to estimate the state of the distribution network system at the current moment.

[0009] Specifically, during the initial distribution network state estimation, the number of particles required for particle filtering is based on a preset benchmark value; during subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset benchmark particle number, and the state estimation accuracy of the previous moment.

[0010] Furthermore, the number of particles required for particle filtering is determined based on a preset expected estimation accuracy, a preset reference particle number, and the state estimation accuracy at the previous moment. The number of particles required for particle filtering is not less than the product of the power of the ratio of the state estimation accuracy at the previous moment to the expected estimation accuracy and the reference particle number.

[0011] Furthermore, the state estimation accuracy is calculated based on the root mean square of the standard deviation of the state variable estimates.

[0012] Furthermore, the number of particles required for the particle filtering must satisfy the following formula:

[0013]

[0014] Where, N k+1 σ is the minimum number of particles required for particle filtering at time k+1. des To achieve the expected estimation accuracy, σ k Let N1 be the estimated accuracy at time k, N1 be the preset number of reference particles that meet the expected accuracy, and λ be the convergence velocity parameter, which is a positive number that is independent of the dimension of the state variables.

[0015] According to a second aspect of the present disclosure, a distribution network state estimation system based on adaptive particle filtering is provided, comprising:

[0016] The particle generation unit is used to generate particles required for particle filtering based on a random pseudo-Monte Carlo sampling method.

[0017] The state estimation unit is used to estimate the state of the distribution network system at the current moment based on the acquired current measurement data of the distribution network system, the state estimation value of the previous moment, and the generated particles, using a particle filtering algorithm. In the first distribution network state estimation, the number of particles required for particle filtering adopts a preset benchmark value. In subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset benchmark particle number, and the state estimation accuracy of the previous moment.

[0018] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory, wherein the processor executes the program to implement the aforementioned method for estimating the state of a power distribution network based on adaptive particle filtering.

[0019] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the aforementioned method for estimating the state of a distribution network based on adaptive particle filtering.

[0020] Compared with the prior art, the beneficial effects of this disclosure are:

[0021] (1) This disclosure provides a distribution network state estimation method and system based on adaptive particle filtering. The scheme uses a random pseudo Monte Carlo sampling method based on Sobol low difference sequence to generate particle filtering particles, and proposes an adaptive particle number adjustment method based on the processability analysis of random pseudo Monte Carlo sampling. The adaptive adjustment of the particle number can effectively balance the accuracy and computational efficiency of distribution network state estimation.

[0022] (2) The proposed scheme uses the particle filtering method to estimate the three-phase state of the distribution network, which can make full use of historical state information and current measurement information to achieve high-precision estimation of the distribution network and effectively handle the state estimation problem of nonlinear and non-Gaussian systems.

[0023] (3) The scheme described in this disclosure is based on the processability analysis of random pseudo Monte Carlo sampling. It derives the minimum number of particles required for particle filtering based on random pseudo Monte Carlo sampling to meet the expected accuracy requirements. It has good adaptive adjustment capability of particle number and can effectively balance estimation accuracy and computational efficiency.

[0024] Advantages of this disclosure in additional aspects will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0025] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0026] Figure 1 This is a flowchart of the power distribution network state estimation method based on adaptive particle filtering described in the embodiments of this disclosure;

[0027] Figure 2 This is a schematic diagram of the inverse transform method based on the cumulative distribution function as described in the embodiments of this disclosure;

[0028] Figure 3This is the wiring diagram of the IEEE 33-node three-phase distribution network system described in the embodiments of this disclosure.

[0029] Figure 4 This is a probability distribution diagram of the injected power at node 30B of the IEEE node three-phase distribution network system described in this embodiment of the present disclosure.

[0030] Figure 5 This is a schematic diagram of the adaptive adjustment process of the particle number in the first 24 moments as described in the embodiments of this disclosure (corresponding to the standard deviation of voltage amplitude, standard deviation of voltage phase angle, and particle number from top to bottom);

[0031] Figure 6(a) is a box plot of the voltage amplitude estimation accuracy described in the embodiments of this disclosure;

[0032] Figure 6(b) is a box plot of the voltage phase angle estimation accuracy described in the embodiments of this disclosure;

[0033] Figure 7(a) is a box plot of the estimation efficiency corresponding to different particle number estimation methods described in the embodiments of this disclosure;

[0034] Figure 7(b) is a box plot of the estimation efficiency of different estimation methods corresponding to the estimation time described in the embodiments of this disclosure. Detailed Implementation

[0035] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0037] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0038] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0039] Example 1:

[0040] The purpose of this embodiment is to provide a power distribution network state estimation method based on adaptive particle filtering.

[0041] A distribution network state estimation method based on adaptive particle filtering includes:

[0042] The particles required for particle filtering are generated based on a random pseudo-Monte Carlo sampling method.

[0043] Based on the acquired current measurement data of the distribution network system, the state estimate of the previous moment, and the generated particles, the particle filtering algorithm is used to estimate the state of the distribution network system at the current moment.

[0044] Specifically, during the initial distribution network state estimation, the number of particles required for particle filtering is a preset value; during subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset baseline particle number, and the state estimation accuracy of the previous moment.

[0045] In specific implementation, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset reference particle number, and the state estimation accuracy at the previous moment. The number of particles required for particle filtering is not less than the product of the power of the ratio of the state estimation accuracy at the previous moment to the expected estimation accuracy and the reference particle number.

[0046] The state estimation accuracy is calculated based on the root mean square of the standard deviation of the state variable estimates, and the reference particle number is obtained based on the offline simulation results.

[0047] In one or more embodiments, the number of particles required for the particle filtering must satisfy the following formula:

[0048]

[0049] Where, N k+1 σ is the minimum number of particles required for particle filtering at time k+1. des To achieve the expected estimation accuracy, σ k Let N1 be the estimated accuracy at time k, N1 be the number of reference particles that meet the expected accuracy, and λ be the convergence velocity parameter, which is a positive number independent of the dimension of the state variables.

[0050] In specific implementation, the measurement data mainly consists of real-time measurements provided by remote terminal units (RTUs) in the data acquisition and monitoring system installed at preset nodes in the distribution network system and synchronous phasor measurement units (PMUs) in the wide-area measurement system, as well as pseudo measurements obtained based on historical data statistical results. These mainly include voltage amplitude, branch current amplitude, and branch power measurements provided by the RTUs, voltage amplitude and phase angle measurements provided by the PMUs, and pseudo measurements of node injected power.

[0051] In specific implementation, the generation of particles required for particle filtering based on random pseudo Monte Carlo sampling method specifically involves: generating random numbers of a preset dimension that follow a uniform distribution based on Sobol low-difference sequences, and generating deterministic sampling points that meet the requirements of the particle filtering algorithm based on the random numbers; and using a linear matrix scrambling method to convert the obtained deterministic sampling points into random sampling points, thereby obtaining the particles required for particle filtering.

[0052] For ease of understanding, the solution described in this embodiment will be explained in detail below with reference to the accompanying drawings:

[0053] like Figure 1 As shown, a distribution network state estimation method based on adaptive particle filtering includes the following steps:

[0054] Step 1: Initialize time k=1, and set the expected estimation accuracy and the initial reference particle number N1;

[0055] Step 2: Generate the particles required for particle filtering using a random pseudo-Monte Carlo sampling method based on Sobol low-difference sequences;

[0056] Step 2 specifically includes the following processing procedures:

[0057] Random pseudo-Monte Carlo sampling is based on generating low-difference sequences from n Sobol sequences. x 1D unit hypercube The numbers n are random numbers that follow a uniform distribution, where n is n. x Let be the dimension of the state variables for the distribution network state estimation. For the i-th dimension, construct the primitive polynomial as follows:

[0058] p i (X)=X r +a 1,i X r-1 +…+a r-1,i X+1

[0059] Where r is the order of the i-th primitive polynomial. j = 1, ..., r-1 is the primitive polynomial p. i The coefficients of each item.

[0060] For the i-th dimension Sobol sequence, arbitrarily choose r distinct odd numbers {d} 1,i ,...,d q,i}, and for 1≤j≤r, satisfying 0 <d j,i <2 j Therefore, the direction number of the first r elements of the i-th dimension Sobol sequence can be defined as...

[0061] v j,i =d j,i / 2j =(0.v j,i,1 v j,i,2 …)2

[0062] When j > r, according to {d i The primitive polynomial p corresponding to the i-th dimension Sobol sequence i coefficient {a j,i}, d is calculated recursively according to the following formula j,i Then, the corresponding number of directions is calculated.

[0063]

[0064] in This represents the binary bitwise XOR operator.

[0065] For any natural number The x-th element of the i-th dimension Sobol sequence can be obtained by the following formula.

[0066]

[0067] in, χ represents the χ-th element of the i-th dimension Sobol sequence. i (i = 0, ..., M) χ-1 Let be the (i+1)th digit in the binary representation of the natural number χ, satisfying M χ The number of digits in its binary representation. Therefore, n x 1D unit hypercube [0,1) nx The Sobol' sequence can be represented as This allows us to work within a unit hypercube [0,1). nx Generate N uniformly distributed deterministic sampling points, where N is the number of particles sampled by particle filtering.

[0068] A linear matrix scrambling method is used to transform the acquired deterministic sampling points into random sampling points. This linear matrix scrambling method mainly consists of two steps:

[0069] 1) The direction number v of the j-th element in the deterministic Sobol low-difference sequence. j,i Using the random matrix L i =[l i,1 ,l i,2 The scrambled direction numbers v′ are obtained by scrambling the ,…] j,i

[0070]

[0071] 2) Based on the scrambled direction number v′ j,iThe j-th Sobol' sequence element is obtained according to the formula, and then an offset is applied by superimposing a randomly selected binary vector.

[0072]

[0073] Where ... gc2(χ)gc1(χ)gc0(χ) is the Gray code representation of the natural number χ-1.

[0074] Based on the above random pseudo-Monte Carlo sampling method, it is possible to sample the unit hypercube [0,1). nx Generate N uniformly distributed random sampling points.

[0075] The problem of active distribution network state estimation based on particle filtering can be constructed as an integral problem. That is,

[0076]

[0077] Where, x k Let k be the state variable at time k. Its estimated value; z 1:k The system's measurement sequence up to time k; the integrand is ρ(x k )=p(x k |z 1:k )x k The integration domain Θ of this integral problem is usually not a unit hypercube [0,1). nx , where p(x k |z 1:k Let be the probability density function of the independent variable x on Θ. This embodiment uses the inverse transform of the cumulative distribution function to achieve sampling over an arbitrary integration domain Θ, as illustrated in the diagram below. Figure 2 As shown.

[0078] Based on the above, N unit hypercubes [0,1) are generated. nx Uniformly distributed random sampling points Each sampling point is obtained by inverse transform of the cumulative distribution function. The corresponding sample point {x} on the integration domain Θ (i) The i = 1, 2, ..., N} represents the number of particles required for particle filtering.

[0079]

[0080] Where τ is the sampling point x on the integration domain Θ and the unit hypercube [0,1). nx Upsampling points The mapping function between them, in this embodiment, is p(x). k |z 1:kThe cumulative probability density distribution CDF(x) is used as the mapping function τ.

[0081] This integral problem can be solved using the following formula.

[0082]

[0083] Operators This represents the composition of functions, i.e.

[0084] Step 3: Based on Bayesian theory, use the particle filter algorithm to estimate the state of the distribution network at the current moment;

[0085] Step 3 specifically includes the following processing procedures:

[0086] Based on Bayesian theory, and taking into account the state transition process of the distribution network, particle filtering is used to estimate the three-phase state of the distribution network. This state estimation mainly consists of two stages: prediction and filtering. Its model is based on the discrete-time state-space model of the distribution network.

[0087] x k =f(x) k-1 ,u k )+ξ k

[0088] z k =h(x k ,u k )+υ k

[0089] Among them, u k The system input variable matrix, z k Here is the system measurement matrix. f(·) and h(·) are the nonlinear and non-Gaussian state transition equations and measurement equations, respectively; ξ k With υ k Let P be the system process noise and measurement noise matrices, and let P be their error covariance matrices. k With R k This reflects the accuracy of the system model.

[0090] Based on Bayesian theory, the weights of each particle can be calculated recursively. The corresponding posterior distribution and its mean, along with the calculation method for the weights of each particle, are as follows:

[0091]

[0092]

[0093]

[0094] Where q(x)k |x k-1 ,z 1:k ) represents the reference distribution; δ(·) represents the Dirac-Delta function; This represents the Wik-normalized importance weights. 0:k Let z be the sequence of state variables of the system up to time k, and assume that they are independent and identically distributed. 1:k This is the measurement sequence of the system up to time k. p(x) k |z 1:k-1 Let p(x) be the probability density function of the state variables during the prediction phase. k |x k-1 Let p(z) be the prior density of the state variable. k |x k ) is the measure of likelihood density, p(x) k |z 1:k Let be the posterior density of the state variables at time k.

[0095] When using p(x) k |x k-1 As a reference distribution, the weights of each particle can be determined solely based on its weight w at its previous time step. k With the measurement likelihood probability p(z) k |x k Recursive calculation, i.e.

[0096] w k =w k-1 p(z k |x k )

[0097] To address the particle degradation problem in particle filtering, a sampling-importance-resampling method is employed. Resampling is performed based on the normalized weights of each particle, replicating particles with high weights and discarding those with low weights, thereby suppressing degradation and ensuring estimation accuracy. The normalized weights of each particle after resampling are then calculated. It is 1 / N.

[0098] The state variable estimates and errors of the joint estimation of distribution networks based on particle filtering can be given by their expectation and covariance, i.e.

[0099]

[0100]

[0101] Step 4: Based on the convergence speed of the error bound of random pseudo Monte Carlo sampling, and according to the state estimation accuracy at the current moment, determine the minimum number of particles required for the particle filter to meet the given accuracy requirements at the next moment.

[0102] Step 4 specifically includes the following processing procedures:

[0103] By analyzing the tractability of random pseudo-Monte Carlo sampling, it is proven that its error bound convergence rate can be expressed as O(N). -1+λ That is, satisfying

[0104] ε x ≤c(N) -1+λ

[0105] Where c is a constant independent of the dimension s of the integrand space; λ is a positive number independent of the dimension s of the integrand space. Considering that the estimation error decreases with the increase of the number of particles N, λ < 1 is generally assumed. In practical engineering applications, the values ​​of c and λ are determined by their respective integral problems. Since there is no empirical range of values, the set values ​​of c and λ can be obtained by performing a large number of offline simulations by changing the number of particles N, and then fitting the corresponding error convergence rate curve.

[0106] In practical power distribution networks, considering that the true values ​​of state variables cannot be obtained, the root mean square form of the standard deviation of the estimated state variables, σ, is used. k To approximate the state estimation accuracy of the particle filter, we have:

[0107]

[0108]

[0109] Where, σ ii,k For state variable x i,k The standard deviation of the estimated value; Σ ii,k For state variable x i,k The corresponding estimated variance.

[0110] Based on the estimation accuracy σ at time k k Based on the convergence speed of the error bound, the number of particles required for adaptive particle filtering in stochastic pseudo-Monte Carlo simulation is derived. To meet the expected estimation accuracy, the minimum number of particles N required for adaptive particle filtering at time k+1 is determined. k+1 The following inequalities should be satisfied

[0111] σ k ≤c(N k+1 ) -1+λ =σ des ≤c(N1) -1+λ

[0112]

[0113] Where, σ des To achieve the expected estimation accuracy, N1 is the number of reference particles that meet this expected accuracy, which can be obtained from offline simulation. If the estimation error at time k is σ... k The accuracy of the estimate exceeded expectations σ desAt time k+1, the distribution network state estimation will use more particles to improve estimation accuracy, which will increase the computational load and time of state estimation, and vice versa. By adaptively adjusting the number of particles, the active distribution network state estimation based on this adaptive particle filtering algorithm can balance estimation accuracy and computational efficiency.

[0114] Figure 3 A system architecture diagram of an IEEE 33-node three-phase unbalanced distribution network is presented for simulation verification of the proposed method. State estimation is performed every 5 minutes. In the IEEE 33-node distribution network system, phases 12A, 23C, and 30B of each node are connected with an area of ​​300 m². 2 The photovoltaic system, with a photoelectric conversion efficiency of 13.44%, accounts for 13.76% of the total grid load. The system measurement configuration is shown in Table 1. To highlight the power grid state estimation method based on adaptive particle filtering (hereinafter referred to as RQMC-APF, randomized quasi Monte Carlo-based adaptive particle filter) described in this embodiment for handling non-Gaussian noise, it is assumed that the measurement error of RTU measurements follows a Laplace distribution with a mean of 0, and its scale parameter is set to 1% of the true measurement value; the measurement error of PMU measurements follows a Laplace distribution with a mean of 0, and its scale parameter is set to 0.7% of the true measurement value; the probability distribution of each node's pseudo-measurement is described by a Gaussian mixture model. Figure 4 The probability distribution of the active power load of phase 30B node is shown, and the distribution exhibits obvious non-Gaussianity.

[0115] Table 1 Measurement Configuration of IEEE 33-Node Three-Phase Unbalanced Distribution Network

[0116]

[0117] To verify the ability of the proposed randomized quasi Monte Carlo-based adaptive particle filter (RQMC-APF) to balance estimation accuracy and efficiency, the state estimation results of RQMC-APF are compared with those of RQMC-PF and standard particle filter (PF). Specifically, the initial particle number N1 of RQMC-APF is set to 100, and the expected accuracy σ of voltage amplitude and phase angle is... des,V With σ des,θSet to 2.85×10 -4 pu and 2.85×10 -4 rad. Voltage amplitude convergence speed related parameter c v With λ V They are 1.129×10 -3 And 0.400, the voltage phase angle convergence speed related parameter c θ With λ θ They are 8.343×10 -4 And 0.604, used for the particle number adaptive adjustment process. Figure 5 The estimation accuracy of voltage amplitude and phase angle for the first 24 time points and the corresponding number of particles are presented to illustrate the adaptive particle number adjustment process of the proposed method. Box plots of estimation accuracy and efficiency for the three estimation methods are shown below. Figures 6(a) to 6(b) and Figures 7(a) to 7(b) As shown.

[0118] like Figure 5 As shown, the active distribution network state estimation based on pseudo-Monte Carlo adaptive particle filtering proposed in this embodiment can adaptively adjust the required number of particles according to the estimation accuracy, so as to achieve a balance between estimation accuracy and efficiency. If the root mean square form of the standard deviation of the state estimate at time k is σ... V,k or σ θ,k Higher than its expected accuracy σ des,V or σ des,θ If the number of particles required for RQMC-PF at time k+1 increases, then the number of particles required at time k+1 will increase, and vice versa. Taking time 8 as an example, the standard deviation σ of its voltage phase angle estimate... θ,k =8 is 3.329 × 10 -4 The rad (i.e., point A') exceeds its allowable value σ. des,θ =2.85×10 -4 rad, according to the formula, the number of particles N required at time 9 k =9 will rise to 153 (i.e., point A”). As the number of particles used increases, the estimation accuracy at time 9 recovers to the expected accuracy requirement, but the calculation time increases accordingly. Conversely, at time 12, the root mean square form σ of the standard deviation of the voltage amplitude and phase angle estimates... V,k =12 and σ θ,k =12 are 1.599×10 -4 pu (i.e., point B) and 1.559 × 10 -4 The rad (i.e., point B”) is within the allowable range. According to the formula, the number of particles N required at time 13 is... k =13 decreased to 37 (i.e., point B”), thereby improving the estimation efficiency.

[0119] like Figures 6(a) to 6(b) and Figures 7(a) to 7(b)As shown, after adopting the adaptive particle number adjustment method proposed in this embodiment, the average estimation time of the distribution network state estimation based on pseudo-Monte Carlo adaptive particle filtering decreased from 0.265 seconds to 0.226 seconds. The estimation accuracy of both voltage amplitude and phase angle remained near the expected accuracy, and the range of estimation error was narrowed. This also indicates that the distribution network state estimation based on pseudo-Monte Carlo adaptive particle filtering has good adaptive particle number adjustment capability, effectively balancing estimation accuracy and computational efficiency.

[0120] Example 2:

[0121] The purpose of this embodiment is to provide a power distribution network state estimation system based on adaptive particle filtering.

[0122] A power distribution network state estimation system based on adaptive particle filtering includes:

[0123] The particle generation unit is used to generate particles required for particle filtering based on a random pseudo-Monte Carlo sampling method.

[0124] The state estimation unit is used to estimate the state of the distribution network system at the current moment based on the acquired current measurement data of the distribution network system, the state estimation value of the previous moment, and the generated particles, using a particle filtering algorithm. In the first distribution network state estimation, the number of particles required for particle filtering adopts a preset benchmark value. In subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset benchmark particle number, and the state estimation accuracy of the previous moment.

[0125] Furthermore, the system described in this embodiment corresponds to the method described in Embodiment 1, and its technical details have been explained in Embodiment 1, so they will not be repeated here.

[0126] In further embodiments, the following is also provided:

[0127] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor. When executed by the processor, the computer instructions perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0128] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0129] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0130] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0131] The method in Embodiment 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0132] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0133] The above embodiments provide a distribution network state estimation method and system based on adaptive particle filtering, which can be implemented and has broad application prospects.

[0134] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A distribution network state estimation method based on adaptive particle filtering, characterized in that, include: The particles required for particle filtering are generated based on a random pseudo-Monte Carlo sampling method. Based on the acquired current measurement data of the distribution network system, the state estimate of the previous moment, and the generated particles, the particle filtering algorithm is used to estimate the state of the distribution network system at the current moment. Specifically, during the initial distribution network state estimation, the number of particles required for particle filtering is a preset baseline particle number; during subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset baseline particle number, and the state estimation accuracy of the previous moment. The number of particles required for particle filtering is determined based on a preset expected estimation accuracy, a preset reference number of particles, and the state estimation accuracy of the previous moment. The number of particles required for particle filtering is not less than the product of the power of the ratio of the state estimation accuracy of the previous moment to the expected estimation accuracy and the reference number of particles. The number of particles required for the particle filter must satisfy the following formula: in, for k The minimum number of particles required for particle filtering at time +1. σ des To achieve the expected estimation accuracy, For the accuracy of the time-k estimation, N 1 represents the number of reference particles required to achieve the expected accuracy. λ It is a positive number that is independent of the dimension of the state variable.

2. The distribution network state estimation method based on adaptive particle filtering as described in claim 1, characterized in that, The accuracy of the state estimation is calculated based on the root mean square of the standard deviation of the state variable estimates.

3. The distribution network state estimation method based on adaptive particle filtering as described in claim 1, characterized in that, The baseline particle number was obtained based on offline simulation results.

4. The distribution network state estimation method based on adaptive particle filtering as described in claim 1, characterized in that, The measurement data mainly consists of real-time measurements provided by remote terminals in the data acquisition and monitoring system installed at preset nodes in the power distribution network system and synchronous phasor measurement units in the wide-area measurement system, as well as pseudo measurements obtained based on historical data statistical results.

5. The distribution network state estimation method based on adaptive particle filtering as described in claim 1, characterized in that, The process of generating particles for particle filtering based on random pseudo Monte Carlo sampling method specifically involves: generating random numbers of a preset dimension that follow a uniform distribution based on Sobol low-difference sequences, and generating a number of deterministic sampling points that meet the requirements of the particle filtering algorithm based on the random numbers. The linear matrix scrambling method is used to transform the acquired deterministic sampling points into random sampling points, thereby obtaining the particles required for particle filtering.

6. A power distribution network state estimation system based on adaptive particle filtering, characterized in that, include: The particle generation unit is used to generate particles required for particle filtering based on a random pseudo-Monte Carlo sampling method. The state estimation unit is used to estimate the state of the distribution network system at the current moment based on the acquired current measurement data of the distribution network system, the state estimation value at the previous moment, and the generated particles, using a particle filtering algorithm. In the first distribution network state estimation, the number of particles required for particle filtering adopts a preset reference number of particles. In subsequent distribution network state estimations, the number of particles required for particle filtering is determined based on the preset expected estimation accuracy, the preset reference number of particles, and the state estimation accuracy at the previous moment. The number of particles required for particle filtering is determined based on a preset expected estimation accuracy, a preset reference number of particles, and the state estimation accuracy of the previous moment. The number of particles required for particle filtering is not less than the product of the power of the ratio of the state estimation accuracy of the previous moment to the expected estimation accuracy and the reference number of particles. The number of particles required for the particle filter must satisfy the following formula: in, for k The minimum number of particles required for particle filtering at time +1. σ des To achieve the expected estimation accuracy, For the accuracy of the time-k estimation, N 1 represents the reference number of particles that meet this expected accuracy. λ It is a positive number that is independent of the dimension of the state variable.

7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements a power distribution network state estimation method based on adaptive particle filtering as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a power distribution network state estimation method based on adaptive particle filtering as described in any one of claims 1-5.

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