A power distribution system state estimation method and system considering measurement communication reliability

Abstract

This invention belongs to the field of power system technology and provides a method and system for state estimation of distribution systems that considers measurement communication reliability. First, a stochastic state estimation model for distribution systems considering measurement loss is established, introducing a series of random 0-1 variables to represent the random loss of measurements. To calculate the values ​​of each random variable, a method for calculating measurement communication reliability considering the operating state of communication equipment is proposed. A depth-first search is used to find the communication path between the measurement and the estimator, and the reliability of each path is calculated based on the current operating time of the equipment. Then, all paths are orthogonalized, and the reliability of all orthogonal paths is summed to obtain the measurement communication reliability. Finally, an extended Kalman filter algorithm for the established stochastic state estimation model is derived based on projective theory. By incorporating the communication reliability of each measurement into the estimation process, the reasonable allocation of measurement weights is achieved, improving the accuracy of state estimation.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and in particular relates to a method and system for estimating the state of a power distribution system that takes into account the reliability of communication measurement. Background Technology

[0002] The accuracy of state estimation of a power distribution system largely depends on the accuracy of measurement information. However, changes in the operating status of communication equipment due to equipment aging, information attacks, etc., can lead to random events such as loss of measurement information, which in turn introduce a series of outliers into the data received by the estimator, affecting the accuracy of state estimation.

[0003] Current state estimation methods that consider random measurement loss often employ Bernoulli distributions or Markov chains to model the random loss characteristics of measurements. Specifically, a series of 0-1 variables are introduced to characterize the loss of measurement information, with the values ​​of these 0-1 variables at each time step following a certain probability distribution. This probability distribution is defined as communication reliability or measurement loss rate, and is typically related to the operating state of the communication equipment.

[0004] The inventors discovered that most existing studies use a fixed value as the communication reliability of each measurement, neglecting the impact of changes in the operating state of the communication equipment. In reality, the operating state of the communication equipment directly determines the communication reliability of the measurements, and the value of the measurement's communication reliability affects the weight of each measurement in the state estimator, thus altering its contribution to the estimation result. Consequently, measurements with higher reliability have a greater impact on the estimation result. Therefore, considering the influence of the communication equipment's operating state during the state estimation process and accurately calculating the communication reliability of each measurement is particularly important. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a power distribution system state estimation method and system that considers measurement communication reliability. This invention introduces a series of random variables to describe the random measurement loss phenomenon of the power distribution system by establishing a stochastic state estimation model. Furthermore, it proposes a method for calculating the measurement communication reliability of the power distribution system to characterize the probability distribution of these random variables. Finally, it proposes a power distribution system state estimation method that considers the operating status of communication equipment, taking measurement communication reliability into account to achieve a more reasonable measurement weight allocation.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] In a first aspect, the present invention provides a method for estimating the state of a power distribution system that takes into account the reliability of communication measurements, comprising:

[0008] Establish a stochastic state estimation model for a power distribution system that takes into account the loss of random measurements;

[0009] The system obtains all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculates the reliability of each communication path based on the operating status of the communication devices.

[0010] All communication paths are orthogonalized, and the reliability of all the obtained orthogonal paths is summed to obtain the measured communication reliability.

[0011] The extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system is derived to achieve a reasonable allocation of the weights for different communication reliability metrics.

[0012] Furthermore, in the established stochastic state estimation model of the power distribution system, a diagonal matrix composed of random 0-1 variables is introduced to characterize the random loss of measurements. The introduced diagonal matrix includes: a real measurement block diagonal matrix with the dimension of the number of real measurements, and a pseudo measurement block diagonal matrix with the dimension of the number of pseudo measurements.

[0013] Furthermore, the pseudo-measurement block diagonal matrix is ​​an identity matrix; the values ​​of each element in the real measurement block diagonal matrix depend on the communication reliability of each measurement.

[0014] Furthermore, when obtaining all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal: first, find a path from the estimator to the measuring device, then backtrack to the nodes before the measuring device node to search for other paths that can reach the measuring device. If they exist, record the paths that can reach the measuring device; if they do not exist, backtrack to the node before the measuring device node, until backtracking to the estimator node and traversing all the nodes connected to the estimator node.

[0015] Furthermore, when calculating the reliability of all communication paths based on all acquired communication paths, the reliability model of the communication equipment is used; whereby the reliability model of the communication equipment is an exponential function that is negatively correlated with the equipment's operating time.

[0016] Furthermore, during orthogonalization, one path is taken as the baseline path, and subsequent paths are successively transformed into paths orthogonal to the previous paths. The reliability of the orthogonalized path is the conditional probability that all paths before the baseline path cannot work properly while the current path can work properly.

[0017] Furthermore, based on the projection theorem, an extended Kalman filter algorithm is derived for the stochastic state estimation model of the power distribution system. In the derived extended Kalman filter algorithm, a diagonal matrix composed of the communication reliability of each measurement is introduced to achieve a reasonable allocation of measurement weights and improve estimation accuracy.

[0018] Secondly, the present invention also provides a power distribution system state estimation system that takes into account the reliability of measurement communication, comprising:

[0019] The stochastic state estimation model building module is configured to: establish a stochastic state estimation model for the power distribution system that takes into account the loss of random measurements;

[0020] The communication path acquisition module is configured to: acquire all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculate the reliability of each communication path based on the operating status of the communication devices;

[0021] The communication reliability acquisition module is configured to: orthogonalize all communication paths and sum the reliability of all obtained orthogonal paths to obtain the communication reliability.

[0022] The state estimation module is configured to derive the extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system and to achieve a reasonable allocation of measurement weights for different communication reliability metrics.

[0023] Thirdly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power distribution system state estimation method taking into account the reliability of communication as described in the first aspect.

[0024] Fourthly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the power distribution system state estimation method that takes into account the reliability of communication as described in the first aspect.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0026] The proposed method for calculating communication reliability by measuring data incorporates the impact of the operating status of communication equipment on communication reliability, thus better reflecting the actual operation of power distribution systems. Furthermore, the calculated communication reliability of the measurements is incorporated into the state estimation algorithm, allowing measurements with higher reliability to contribute more significantly to the estimation results and improving the accuracy of state estimation. Attached Figure Description

[0027] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0028] Figure 1 This is a flowchart of Embodiment 1 of the present invention;

[0029] Figure 2This is a structural diagram of the IEEE-33 node power distribution system according to Embodiment 1 of the present invention;

[0030] Figure 3 This is a flowchart of the depth-first traversal method for searching measurement communication paths in Embodiment 1 of the present invention;

[0031] Figure 4 This is a schematic diagram of the measurement communication path in Embodiment 1 of the present invention;

[0032] Figure 5 This is a flowchart of path orthogonalization based on the non-intersection sum method in Embodiment 1 of the present invention;

[0033] Figure 6 The measurement communication reliability calculation results are from Embodiment 1 of the present invention.

[0034] Figure 7 This is a comparison of the voltage amplitude estimation results in Embodiment 1 of the present invention;

[0035] Figure 8 This is a comparison of the voltage phase angle estimation results of Embodiment 1 of the present invention. Detailed implementation method:

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. 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 application pertains.

[0038] Example 1:

[0039] Distribution system state estimation is based on real-time calculation of the system's operating status using real-time and pseudo-measurement information, and further provides a data foundation for advanced applications such as optimized scheduling and voltage control. With the large-scale introduction of highly uncertain source and load equipment such as new energy power generation equipment and controllable loads into distribution systems, distribution system state estimation faces new challenges while playing a crucial role in the system's operation and control.

[0040] The accuracy of state estimation in a power distribution system largely depends on the accuracy of measurement information, and the reliable operation of each device in the power distribution communication network is a prerequisite for the estimator to accurately acquire each measurement information. In reality, changes in the operating state of communication equipment due to equipment aging, information attacks, etc., can lead to random events such as the loss of measurement information, which in turn introduce a series of outliers into the data received by the estimator, affecting the accuracy of state estimation.

[0041] As mentioned in the background section, most existing studies use a fixed value as the communication reliability of each measurement, neglecting the impact of changes in the operating state of the communication equipment. In reality, the value of the measurement's communication reliability affects the weight of each measurement in the state estimator, thus altering its contribution to the estimation result. Consequently, measurements with higher reliability have a greater impact on the estimation result. Therefore, accurately calculating the communication reliability of each measurement and considering its impact during the state estimation process is particularly important.

[0042] To address the issue that existing studies neglect the impact of changes in the operating state of communication equipment, this embodiment provides a power distribution system state estimation method that takes into account the measurement of communication reliability, including:

[0043] Establish a stochastic state estimation model for a power distribution system that takes into account the loss of random measurements;

[0044] The system obtains all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculates the reliability of each communication path based on the operating status of the communication devices.

[0045] All communication paths are orthogonalized, and the reliability of all the obtained orthogonal paths is summed to obtain the measured communication reliability.

[0046] The extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system is derived to achieve a reasonable allocation of the weights for different communication reliability metrics.

[0047] like Figure 1 As shown, the specific content of the power distribution system state estimation method considering communication reliability in this embodiment is as follows:

[0048] S1. Establish a stochastic state estimation model for the power distribution system and introduce random binary variables to represent the random loss of measurements.

[0049] S2. Calculate the communication reliability of each measurement based on the operating status of the communication equipment;

[0050] S3. Derive the state estimation algorithm based on the stochastic state estimation model of the power distribution system.

[0051] In this embodiment, step S1 specifically includes:

[0052] S1.1 First, establish a stochastic state estimation model for the power distribution system that considers random measurement loss. In this model, a random diagonal matrix composed of random binary variables is introduced to represent the random loss of measurements, specifically:

[0053]

[0054] In the formula: the subscript k represents the k-th time; x kFor x k =[V k ,θ k ] T The state vector of the power distribution system includes the voltage magnitude V of all buses. k With phase angle θ k f and h are the nonlinear system state transition equation and measurement equation, respectively; v k With ω k The noise is Gaussian white noise, representing the system's state error and measurement error, respectively, and their covariance matrices are Q... k With R k ; The measurement vector for the power distribution system includes real-time measurement vectors consisting of node voltage amplitudes, branch active and reactive power measurements, and branch current measurements, as well as pseudo-measurements consisting of active and reactive power injections at each node; Ψ k This is a diagonal matrix consisting of 0-1 variables, where the elements on the diagonal represent the loss of measurement information, specifically:

[0055]

[0056] In the formula: E is an identity matrix with appropriate dimensions, where the dimension in this case represents the number of pseudo-measurements in the power distribution system; m is the number of actual measurements in the power distribution system; α i,k The value of the 0-1 random variable characterizing whether the i-th measurement is missing at time k depends on:

[0057]

[0058] Where: β i With η i Let represent the communication reliability and loss rate of the i-th measurement, where represents the probability of successful transmission and loss of the measurement, respectively. At each time step, the random matrix Ψ... k The values ​​of each element on the diagonal are determined by the matrix Γ, which measures communication reliability, i.e.:

[0059] Γ=E{Ψ k}=diag{γ,E} (4)

[0060] In the formula: γ=diag(β1,β2,…β m ).

[0061] S1.2. Since the state estimation model of the power distribution system contains nonlinear functions, the computational complexity and amount of computation will increase in the subsequent derivation and implementation of the state estimation method. Therefore, it is linearized. The state estimation model of the power distribution system is Equation (1). In this embodiment, a quasi-steady-state power flow model based on load forecasting can be used to realize the state transition of the power distribution system, that is:

[0062]

[0063] In the formula: J k-1 The Jacobian matrix of the power distribution system; Δu k This represents the change in injected power at each node between time k and time k-1.

[0064] The measurement equation h of the power distribution system can be linearized by using Taylor series expansion while retaining linear terms. The linearized measurement equation is as follows:

[0065]

[0066] In the formula: C k =J k-1 ,

[0067] Therefore, the linearized power distribution system state estimation model is as follows:

[0068]

[0069] In the formula: A k It is an identity matrix;

[0070] Step S2 specifically includes:

[0071] To calculate the probability that at least one channel between the estimator and the measurement is working properly, we can first search for all communication paths between the measurement device and the estimator using a depth-first search method. Then, we can establish a reliability model based on the operating status of the communication device and calculate the reliability of each communication path. Finally, we can orthogonalize these paths and add up the reliability of all orthogonal paths to obtain the communication reliability of the measurement.

[0072] S2.1, Search for all communication paths between the measurement and estimator using a depth-first search method:

[0073] Considering that optical fibers in distribution communication networks are generally installed on the same poles as the power conductors, we assume that the topology of the distribution communication network is the same as that of the distribution network. We also assume that all nodes in the distribution communication network operate in full-duplex mode. Figure 2 The power distribution network shown can be abstracted as an undirected graph, and searching for communication paths is essentially finding all the direct paths between the points representing the estimator and the measuring device on this undirected graph.

[0074] The search process based on depth-first traversal is as follows: Figure 2As shown, the basic idea is to first find a path from the estimator to the measurement, then backtrack to the nodes before the measurement node, searching for other paths to the measurement. If a path exists, it is recorded; otherwise, backtrack to the node before that node, until the estimator node is reached and all nodes connected to the estimator node have been traversed. Figure 3 The IEEE-33 node distribution network shown is a case study. Partial communication paths between the No. 5 measuring device found in this step and the estimator located at node 1 are as follows: Figure 4 As shown.

[0075] S2.2 To calculate the reliability of all communication paths traversed in step S2.1, a reliability model is first established based on the operating status of the communication equipment. Considering that the reliability of various types of equipment decreases with the increase of operating time, reliability mathematics generally uses an exponential function to calculate the reliability based on the operating time of the equipment, specifically:

[0076] μ(t)=e -λt (8)

[0077] In the formula: t is the continuous operating time of the equipment; λ is the evaluation failure rate of the communication node or the communication line per unit length, which can generally be obtained from the equipment manufacturer.

[0078] At this point, the reliability of the i-th communication path can be expressed as:

[0079]

[0080] In the formula: N is the number of communication elements contained in the communication path.

[0081] S2.3 Since all communication paths obtained in the above steps contain the same communication nodes or branches, they are not orthogonal paths. Therefore, in this step, the disjoint product sum method is used to orthogonalize all paths, and the reliability of all orthogonal paths is summed to finally obtain the probability that at least one path is working normally. The basic idea is as follows: Figure 5 As shown.

[0082] The basic idea of ​​the disjoint product sum method is to take a path as a baseline and then transform subsequent paths into paths orthogonal to the previous paths. The reliability of the orthogonalized path is the conditional probability that all previous paths fail while the current path succeeds. This conditional probability essentially involves finding communication nodes and branches that are not shared with previous paths, and then determining the probability that at least one of the non-shared branches or nodes fails while ensuring the current path functions normally.

[0083] by Figure 4 and Figure 2Taking the three paths shown as an example, with path 1 as the baseline path, to ensure that path 2 is orthogonal to path 1, its non-shared branches (branch 9-branch 15) must not function properly, provided that path 2 can operate normally. The conditional probability is the reliability of path 2 multiplied by the failure rate of branch 9-branch 15. This results in a reliability of 0.0271 for the orthogonalized path 2 after one year of operation. The reliability of the other measurements in scenarios with operation times of 1 year, 3 years, and 5 years are as follows... Figure 6 As shown, it can be seen that the reliability of the communication equipment decreases with increasing operating time, which in turn leads to a decrease in the communication reliability of various measurements.

[0084] Step S3 specifically includes:

[0085] Based on projective theory, the extended Kalman filter state estimation algorithm designed for the stochastic state estimation model of the power distribution system considering measurement loss in formula (7) is as follows:

[0086] Prediction step:

[0087]

[0088]

[0089] Filtering step:

[0090]

[0091]

[0092]

[0093]

[0094] In the formula: P is the predicted state value at time k; k|k-1 The error covariance for state prediction; Here is the state estimate at time k; K k 0 represents the Kalman gain; o represents the Hadamard product operation.

[0095] As can be seen from the equations of the filtering step, the matrix Γ, composed of the communication reliability of each measurement, plays a certain role in the filtering process. Specifically, it measures the quantities with higher communication reliability in the Kalman gain K. k The state is given a higher weight, thus giving it a greater influence on the state estimation results.

[0096] based on Figure 2The effectiveness of the proposed method is verified using the IEEE-33 node power distribution system, and the measurement configuration is shown in the figure. The estimation results of the proposed method in this embodiment are compared with the estimation results when the communication reliability is uniformly set to 0.7 without considering the equipment operating status, under scenarios of 1 year, 3 years, and 8 years of communication equipment operation time. Figure 7 and Figure 8 As shown in the figure, the comparison of results demonstrates that the method proposed in this embodiment has better robustness and estimation accuracy for random measurement loss caused by changes in the operating state of communication equipment.

[0097] In this embodiment, considering that the operating state of communication equipment affects the reliability of measurement information transmission, thus leading to random measurement loss, a stochastic state estimation model for a power distribution system considering measurement loss is first established. This model introduces a series of random 0-1 variables to represent the random loss of measurement information. These random variables each follow a certain probability distribution, referred to as the measurement communication reliability. Then, to calculate the communication reliability of each measurement under different communication equipment operating states, a depth-first search is used to find the communication path between the measurement and the estimator. The reliability of each path is calculated based on the current operating time of the equipment. All paths are orthogonalized, and the reliability of all orthogonal paths is summed to obtain the measurement communication reliability. Finally, an extended Kalman filter algorithm is derived based on projective theory to establish the stochastic state estimation model. By incorporating the communication reliability of each measurement into the filtering process, the measurement weights are rationally allocated, improving the estimation accuracy.

[0098] Example 2:

[0099] This embodiment provides a power distribution system state estimation system that considers the measurement of communication reliability, including:

[0100] The stochastic state estimation model building module is configured to: establish a stochastic state estimation model for the power distribution system that takes into account the loss of random measurements;

[0101] The communication path acquisition module is configured to: acquire all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculate the reliability of each communication path based on the operating status of the communication devices;

[0102] The communication reliability acquisition module is configured to: orthogonalize all communication paths and sum the reliability of all obtained orthogonal paths to obtain the communication reliability.

[0103] The state estimation module is configured to derive the extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system and to achieve a reasonable allocation of measurement weights for different communication reliability metrics.

[0104] The operating method of the system is the same as that of the power distribution system state estimation method that takes into account the communication reliability in Example 1, and will not be repeated here.

[0105] Example 3:

[0106] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the power distribution system state estimation method that takes into account the reliability of communication as described in Embodiment 1.

[0107] Example 4:

[0108] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power distribution system state estimation method that takes into account the reliability of communication as described in Embodiment 1.

[0109] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for estimating the state of a power distribution system that takes into account the reliability of measurement communication, characterized in that, include: Establish a stochastic state estimation model for a power distribution system that takes into account the loss of random measurements; The system obtains all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculates the reliability of each communication path based on the operating status of the communication devices. All communication paths are orthogonalized, and the reliability of all the obtained orthogonal paths is summed to obtain the measured communication reliability. The extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system is derived to achieve a reasonable allocation of the weights of different communication reliability measures. Specifically, the extended Kalman filter algorithm is derived for the stochastic state estimation model of the power distribution system based on the projection theorem. A matrix composed of the communication reliability of each measure is introduced into the derived extended Kalman filter algorithm.

2. The power distribution system state estimation method considering communication reliability as described in claim 1, characterized in that, When establishing a stochastic state estimation model for a power distribution system, a diagonal matrix composed of random 0-1 variables is introduced to characterize the random loss of measurements. The introduced diagonal matrix includes: a true measurement block diagonal matrix with the dimension of the number of true measurements, and a pseudo measurement block diagonal matrix with the dimension of the number of pseudo measurements.

3. The power distribution system state estimation method considering communication reliability as described in claim 2, characterized in that, The pseudo-measurement block diagonal matrix is ​​an identity matrix; The values ​​of each element in the actual measurement block diagonal matrix depend on the communication reliability of each measurement.

4. The power distribution system state estimation method considering communication reliability as described in claim 1, characterized in that, When obtaining all communication paths between the measuring device and the estimator in the power distribution system: first, find a path from the estimator to the measuring device, then backtrack to the node before the measuring device node to search for other paths that can reach the measuring device. If they exist, record the paths that can reach the measuring device. If they do not exist, backtrack to the node before the measuring device node, until backtracking to the estimator node and traversing all the nodes connected to the estimator node.

5. The power distribution system state estimation method considering communication reliability as described in claim 1, characterized in that, When calculating the reliability of all communication paths based on all obtained communication paths, the reliability model of the communication equipment is used; where the reliability model of the communication equipment is an exponential function that is negatively correlated with the equipment's operating time.

6. The power distribution system state estimation method considering communication reliability as described in claim 1, characterized in that, When performing orthogonalization, one path is taken as the baseline path, and then subsequent paths are successively transformed into paths orthogonal to the previous paths. The reliability of the orthogonalized path is the conditional probability that all paths before the baseline path cannot work properly, while the current path can work properly.

7. A power distribution system state estimation system that takes into account the reliability of measurement communication, characterized in that, include: The stochastic state estimation model building module is configured to: establish a stochastic state estimation model for the power distribution system that takes into account the loss of random measurements; The communication path acquisition module is configured to: acquire all communication paths between the measuring devices and the estimator in the power distribution system based on depth-first traversal, and calculate the reliability of each communication path based on the operating status of the communication devices; The communication reliability acquisition module is configured to: orthogonalize all communication paths and sum the reliability of all obtained orthogonal paths to obtain the communication reliability. The state estimation module is configured to: derive the extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system, and realize the reasonable allocation of the weights of different communication reliability measures. Specifically, it derives the extended Kalman filter algorithm for the stochastic state estimation model of the power distribution system based on the projection theorem; and introduces a matrix composed of the communication reliability of each measure into the derived extended Kalman filter algorithm.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the steps of the power distribution system state estimation method that takes into account the reliability of communication as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the power distribution system state estimation method that takes into account the reliability of communication as described in any one of claims 1-6.

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