Power system measurement failure state estimation and optimization method with positioning feedback

Abstract

This invention discloses a method for estimating and optimizing the measurement failure state of a power system with location feedback. The method acquires measurement data of the first injected power of the bus and the first branch power of the branches connected to the bus in the power system; it also acquires the second injected power of the bus and the second branch power. Based on the second injected power, a power dissimilarity matrix is ​​constructed to screen out the initial critical bus failure point. After obtaining the density function value corresponding to the initial critical bus failure point using the historical branch power and density function of the initial critical bus failure point, the target critical bus failure point and critical branch failure point are screened from the initial critical bus failure points. Based on the second injected power, the second branch power, and an inertial particle swarm optimization algorithm, the optimal injected power and branch power are obtained and added. The voltage amplitude and voltage phase angle of the bus are estimated using the original measurement data after the addition.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and relates to, but is not limited to, a method for estimating and optimizing the measurement failure state of a power system with location feedback. Background Technology

[0002] With the deep integration of information control technology and new power system entities, measurement data is the foundation for realizing system state perception and control. However, the inherent uncertainties of new energy access, as well as data failures caused by the inaccuracy or malfunction of smart terminal measurement data, make it impossible to accurately estimate the voltage amplitude and voltage phase angle of the bus in the power system.

[0003] In related technologies, deep neural networks are used to identify multi-source data of power systems and then estimate the voltage amplitude and voltage phase angle of the bus. However, this method has the problems of relying heavily on the quality of historical data during model training, having poor generalization ability for new operating conditions of power systems beyond the training range, inaccurate estimation results, and difficulty in updating online to adapt to topology changes.

[0004] Therefore, how to efficiently and accurately estimate the voltage amplitude and voltage phase angle of the bus has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a method for estimating and optimizing the measurement failure state of a power system with location feedback, which at least solves the problem that related technologies cannot efficiently and accurately estimate the voltage amplitude and voltage phase angle of the bus.

[0006] According to a first aspect of the present invention, a method for estimating and optimizing the measurement failure state of a power system with location feedback is provided, comprising: Acquire target measurement data of the power system; the target measurement data includes the first injected power of the bus and the first branch power of the branch connected to the bus; The second injected power of the bus and the second branch power of the branch connected to the bus are obtained within a preset historical time period, and the power dissimilarity matrix corresponding to the bus is constructed based on the second injected power. The initial failure critical bus is selected from the bus based on the power dissimilarity matrix; and the density function value corresponding to the initial failure critical bus is obtained based on the power of the second branch corresponding to the initial failure critical bus and the preset density function. Based on the density function value and the screening threshold, the target critical bus and the critical branch connected to the target critical bus are screened out from the initial critical bus. The optimization is performed based on the second injection power, the second branch power, and the inertial particle swarm algorithm until the optimal injection power and the optimal branch power are obtained. The optimal injected power and optimal branch power are added to the target measurement data to obtain the target measurement data after addition. Based on the target measurement data after addition, the estimated voltage amplitude and voltage phase angle of the bus are obtained.

[0007] According to a second aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform an operation corresponding to the method described in the first aspect.

[0008] According to the scheme provided by the embodiments of the present invention, target measurement data of a power system is obtained; the target measurement data includes the first injected power of the bus and the first branch power of the branch connected to the bus; the second injected power of the bus and the second branch power of the branch connected to the bus are obtained within a preset historical time period, and a power dissimilarity matrix corresponding to the bus is constructed based on the second injected power; the initial failure critical bus is screened out from the buses based on the power dissimilarity matrix; the initial failure critical bus is screened out from the buses based on the power dissimilarity matrix; and the initial failure critical bus is obtained based on the second branch power corresponding to the initial failure critical bus and a preset density function. The density function value corresponding to the bus is used; based on the density function value and the screening threshold, the target critical bus and the critical branch connected to the target critical bus are screened from the initial critical bus failures; optimization is performed based on the second injected power, the second branch power, and the inertial particle swarm optimization algorithm until the optimal injected power and the optimal branch power are obtained; where the inertial particle swarm optimization is a particle swarm optimization algorithm with an exponential variation perturbation mechanism; the optimal injected power and the optimal branch power are added to the target measurement data to obtain the target measurement data after addition, and the estimated voltage amplitude and voltage phase angle of the bus are obtained based on the target measurement data after addition. In this process, the target critical bus and the critical branch failure are determined based on the power dissimilarity matrix and the preset density function, which improves the calculation speed and fault response resilience while ensuring the global estimation accuracy. Based on the inertial particle swarm optimization algorithm, offline compensation and optimization of the target failed critical bus and failed critical branch are achieved using historical data. This avoids the optimization and re-iteration of a large amount of measurement data in the entire power system, thereby improving the estimation speed and calculation accuracy. In summary, it can efficiently and accurately estimate the voltage amplitude and voltage phase angle of the bus. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart illustrating a method for estimating and optimizing the measurement failure state of a power system with location feedback, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the effect of voltage amplitude estimation of a bus without positioning feedback and defect compensation provided in an embodiment of the present invention. Figure 3 This is a schematic diagram illustrating the effect of voltage angle estimation values ​​for a bus without positioning feedback and defect compensation provided in an embodiment of the present invention. Figure 4 This is an estimation error diagram of voltage phase angle and voltage amplitude without defect compensation provided in an embodiment of the present invention; Figure 5 A graph illustrating the dissimilarity provided in an embodiment of the present invention; Figure 6 This is a curve diagram corresponding to the branch power density provided in an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the effect of the target failure critical node feedback marking provided in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the effect of the voltage amplitude error result after compensating the injected power of the target failure critical node using the PSO algorithm, as provided in an embodiment of the present invention. Figure 9 This is a graph showing the change of the inertia weight factor throughout the entire iteration process, provided by an embodiment of the present invention. Figure 10 The graphs showing the changes of c1 and c2 with the number of iterations throughout the entire iteration process are provided in this embodiment of the invention. Figure 11 This is a schematic diagram illustrating the effect of compensating for the branch power of the critical branch corresponding to the target critical failure node in an embodiment of the present invention on the error result of the voltage amplitude. Figure 12 This is a schematic diagram illustrating the effect of the present invention on the voltage amplitude estimation value after compensation for defect measurement information without defect measurement information compensation. Figure 13 This is a schematic diagram illustrating the effect of the present invention on the voltage angle estimation value after compensation for defect measurement information without compensation and after compensation for defect measurement information. Figure 14 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0012] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.

[0013] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments of the invention pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0014] Figure 1 This is a flowchart illustrating a method for estimating and optimizing the measurement failure state of a power system with location feedback, provided in an embodiment of the present invention. This method can be executed by an electronic device, such as a computer or server.

[0015] like Figure 1 As shown, a method for estimating and optimizing the measurement failure state of a power system with location feedback includes: S101. Obtain target measurement data of the power system; the target measurement data includes the first injected power of the bus and the first branch power of the branch connected to the bus.

[0016] In embodiments of the present invention, the power system includes multiple buses and branches formed between different buses. A bus is a common connection point connecting multiple devices in the power system. The first injected power of all available buses and the first branch power of the branches connected to the buses can be collected by a data acquisition and monitoring control system. The first branch power includes the active power flow and reactive power flow of the branch. The first injected power and the first branch power constitute the target measurement data.

[0017] S102. Obtain the second injected power of the bus and the second branch power of the branch connected to the bus within a preset historical time period, and construct the power dissimilarity matrix corresponding to the bus based on the second injected power.

[0018] In embodiments of the present invention, the power dissimilarity matrix is ​​a symmetric matrix, where each element represents the degree of dissimilarity in power characteristics. The power dissimilarity matrix can be used to identify buses of particular importance or influence among multiple buses. When constructing the power dissimilarity matrix, historical data of the buses over a past time period can be obtained. This historical data includes the second injected power and the second branch power of the branches connected to the buses. Then, the power dissimilarity matrix corresponding to the buses is further constructed based on the second injected power.

[0019] S103. Select the initial failure critical bus based on the power dissimilarity matrix; and obtain the density function value corresponding to the initial failure critical bus based on the power of the second branch corresponding to the initial failure critical bus and the preset density function.

[0020] In embodiments of the present invention, the critical bus is a bus in the power system that has special importance or influence. First, the elements in the power dissimilarity matrix are used for initial screening across all buses to obtain the initial failed critical bus. Then, the power of the second branch connected to the initial failed critical bus is obtained. This second branch power is substituted into a preset density function corresponding to the initial failed critical bus to obtain the corresponding density function value. This value represents the sum of the influence functions of all branches connected to the initial failed critical bus, which is also the overall intensity of power exchange between the branches connected to the initial failed critical bus and the other buses in the power system. Power exchange refers to the active or reactive power flowing from the initial failed critical bus along the branches to adjacent buses.

[0021] S104. Based on the density function value and the screening threshold, select the target failure critical bus and the failure critical branch connected to the target failure critical bus from the initial failure critical bus.

[0022] In an embodiment of the present invention, the density function of each initial critical bus failure is compared with a pre-set screening threshold. The initial critical bus failures that are not greater than the screening threshold are taken as target critical buses failures. The target critical bus failures are the buses with the most connected branches in the power system. The branches corresponding to the maximum value of the branch influence function are taken as the critical branches failures corresponding to the target critical bus failures.

[0023] Among them, the first injected power loss is corresponding to the critical busbar of the target failure, or the power loss of the corresponding first branch, or both.

[0024] S105. Optimize based on the second injection power, the second branch power, and the inertial particle swarm algorithm until the optimal injection power and the optimal branch power are obtained; where the inertial particle swarm is a particle swarm algorithm that introduces an exponential variation perturbation mechanism.

[0025] In some embodiments of the present invention, the inertial particle swarm optimization (PSO) algorithm is an improved PSO algorithm. When updating the current velocity of particles in the standard PSO algorithm, an inertial weight factor is used to update the current velocity, obtaining the initial velocity of the particles for the next iteration. This inertial weight factor can balance global and local optimization capabilities. After obtaining the target failure critical bus and failure critical branch, the inertial particle swarm optimization algorithm is used to perform optimization processing based on the historical second injection power and second branch power until the iteration termination condition is obtained, yielding the optimal injection power and optimal branch power.

[0026] S106. Add the optimal injection power and optimal branch power to the target measurement data to obtain the target measurement data after addition, and obtain the estimated voltage amplitude and voltage phase angle of the bus based on the target measurement data after addition.

[0027] In an embodiment of the present invention, the optimal injected power and the optimal branch power are added to the target measurement data to obtain the target measurement data after the addition. A measurement equation is established based on the target measurement data after the addition, and a state vector (the variable to be estimated) is defined. An initial value is set for the state vector, and then the initial value is substituted into the established measurement equation. The weighted least squares method is used to solve the problem and calculate the theoretical measurement value after the first iteration. The residual is calculated based on the theoretical measurement value and the target measurement data after the addition. A Jacobian matrix is ​​constructed based on the residual. A correction equation is further constructed through the Jacobian matrix, and the correction value is calculated through the correction equation. Finally, the initial value of the first iteration is corrected using the correction value. Finally, the correction initial value is used to perform optimization until the correction value meets the conditions, and the final estimated voltage amplitude and voltage phase angle of the bus are obtained.

[0028] It is understood that, in the embodiments of the present invention, target measurement data of the power system is obtained; the target measurement data includes the first injected power of the bus and the first branch power of the branch connected to the bus; the second injected power of the bus and the second branch power of the branch connected to the bus are obtained within a preset historical time period, and a power dissimilarity matrix corresponding to the bus is constructed based on the second injected power; the initial failure critical bus is screened out from the buses based on the power dissimilarity matrix; and the density function value corresponding to the initial failure critical bus is obtained based on the second branch power corresponding to the initial failure critical bus and a preset density function. Based on density function values ​​and screening thresholds, target critical buses and their connected critical branches are selected from the initial critical buses. Optimization is then performed using a second injected power, a second branch power, and an inertial particle swarm optimization (PSO) algorithm to obtain the optimal injected power and optimal branch power. The inertial PSO algorithm incorporates an exponential perturbation mechanism. The optimal injected power and optimal branch power are added to the target measurement data to obtain the added target measurement data. Based on this added target measurement data, the estimated voltage amplitude and voltage phase angle of the bus are obtained. In this process, the target critical buses and critical branches are determined based on the power dissimilarity matrix and a preset density function, ensuring global estimation accuracy while improving computational speed and fault resilience. The inertial PSO algorithm utilizes historical data offline to achieve loss compensation and optimization of the target critical buses and critical branches, avoiding optimization and re-iteration of a large amount of measurement data from the entire power system, thus improving estimation speed and computational accuracy. In summary, this method can efficiently and accurately estimate the voltage amplitude and voltage phase angle of the bus.

[0029] In some embodiments of the present invention, the power dissimilarity matrix corresponding to the bus constructed based on the second injected power in S102 can be implemented by S1021 to S1022, which will be explained by the following steps.

[0030] S1021. Construct a power matrix using the second injected power; the power matrix represents the second injected power of different buses at a specific time, and the columns of the power matrix represent the second injected power of the same bus at different times.

[0031] In some embodiments of the present invention, the second injected power includes active power and reactive power. The power matrix represents the second injected power of different buses at a specific time, and the columns of the power matrix represent the second injected power of the same bus at different times. The power matrix is ​​as follows: In the power matrix above, The power matrix has the following dimensions: N represents the number of buses in the historical measurement data, M represents the number of historical data points in the historical measurement data, P in the power matrix represents active power, and Q represents reactive power in the power matrix.

[0032] S1022. Calculate the dissimilarity based on the power matrix and the Euclidean distance formula, and construct the power dissimilarity matrix based on the dissimilarity.

[0033] In some embodiments of the present invention, the Euclidean distance formula is as follows: In the above formula, Let M represent the dissimilarity between bus j and bus i, where M represents the number of historical measurement data points, and N represents the number of buses in the historical measurement data. Let i be the active power value of the i-th bus in the k-th historical data. Let i be the reactive power value of the i-th bus in the k-th historical data. This represents the apparent power value of the j-th bus in the k-th historical data point. This represents the apparent power value of the i-th bus in the k-th historical data.

[0034] The power dissimilarity matrix is ​​as follows: In some embodiments of the present invention, the initial failure critical bus based on the power dissimilarity matrix in S103 can be implemented by S1031 to S1032, which will be described by the following steps.

[0035] S1031. Obtain the initial maximum value of each column in the power dissimilarity matrix, and sort the initial maximum values ​​of each column from high to low to obtain the sorting result.

[0036] S1032. Take the bus corresponding to the highest initial maximum value in the sorting results as the initial failure critical bus.

[0037] In some embodiments of the present invention, the values ​​of each column in the power dissimilarity matrix are compared to find the maximum value in each column as the initial maximum value. Then, the initial maximum values ​​in each column are sorted from high to low to obtain the largest initial maximum value, and the bus corresponding to the largest initial maximum value is taken as the initial failure critical bus.

[0038] In some embodiments of the present invention, obtaining the density function value corresponding to the initial failure critical bus based on the second branch power and the preset density function in S103 can be achieved through S103A, as described in the following steps.

[0039] S103A, Substitute the power of the second branch into the preset density function to obtain the density function value corresponding to the initial failure critical bus.

[0040] In some embodiments of the present invention, the power of the second branch corresponding to each of the initially failed critical buses is substituted into a preset density function to obtain the influence function values ​​of all branches connected to each of the initially failed critical buses. Then, these values ​​are added together to obtain the corresponding density function values. The preset density function is as follows: In the above formula, Let a be the density function value of the a-th initial failure critical busbar. For Gaussian threshold, take , are the second branch power of the xth and yth branches connected to the initial failure critical bus, respectively, and nb is the power of all initial failure critical buses.

[0041] For example, when a is 2, and the connecting branches include S2, S6, S7, and S8, S2 and S6, S7, and S8 are respectively substituted into the above-mentioned preset density function to obtain the influence function values ​​of three branches. Then, S6 and S7 and S8 are respectively substituted into the above-mentioned preset density function to obtain the influence function values ​​of two branches. Then, S7 and S8 are substituted into the above-mentioned preset density function to obtain the influence function value of one branch. Finally, the influence function values ​​of the six branches are summed according to the summation formula in the above-mentioned preset density function to obtain the density function value of the second initial failure critical bus.

[0042] In some embodiments of the present invention, the optimization based on the second injection power, the second branch power, and the inertial particle swarm algorithm in S105 until the optimal injection power and the optimal branch power are obtained can be achieved through S1051 to S1052, which will be explained through the following steps.

[0043] S1051. Obtain the initial particle swarm represented in matrix form, consisting of the second injection power and the second branch power.

[0044] S1052. The evolutionary equation with an exponential perturbation mechanism introduced in the inertial particle swarm algorithm is used to update the velocity of the initial particle swarm and perform multiple iterations to find the optimal value until the iteration condition is met, thereby obtaining the optimal injection power and the optimal branch power.

[0045] In some embodiments of the present invention, the inertial particle swarm optimization algorithm introduces an inertial weight factor into the velocity update formula of the original particle swarm optimization algorithm. The second injected power and the second branch power constitute the initial particle swarm. Then, using the evolutionary equation with an exponential perturbation mechanism introduced in the inertial particle swarm optimization algorithm, the initial particle swarm is updated in velocity and iterated multiple times for optimization. Combined with the objective function in the iteration process, the iteration condition is met until the optimal injected power and the optimal branch power are obtained.

[0046] The evolutionary equation that incorporates the exponential variation perturbation mechanism is as follows: In the above formula, The speed at which the current particle in the m-th row and n-th column compensates for missing power information at the (t+1)-th iteration. The speed at which the current particle in the m-th row and n-th column compensates for missing power information at the t-th iteration. c1 is the inertia weighting factor; c2 is the individual learning factor; c2 is the social learning factor; r1 and r2 are uniform random numbers in [0,1], respectively. P represents the speed at which the historical best value information of the compensated target failure critical bus and failure critical branch is calculated for the current particle in row m and column n at the t-th iteration. P represents the individual best position in the compensation optimization. Let be the velocity of the particle in the m-th row and n-th column at the time of the t-th iteration, when the compensated target failure critical bus and failure critical branch are compensated. G represents the optimal value experienced by the compensated target failure critical bus and failure critical branch in the entire cluster at the nth column, determined at the tth iteration; where G represents the global optimal position in the compensation optimization; and the current particle is the compensated injection power and branch power.

[0047] The inertia weighting factor is calculated as follows: In the above formula, and These are the maximum and minimum values ​​of the inertia weighting factor, respectively. The maximum value of the inertia weighting factor can be between 0.8 and 0.95, and the minimum value can be between 0.3 and 0.5. Let k be the number of random perturbations in the interval [0,1], and k be the attenuation coefficient, with a value of 10. This indicates the maximum number of iterations.

[0048] The objective function is: In the above formula, E is the objective function, x is the voltage amplitude and voltage phase angle estimated based on undamaged measurement data, and z0 is the injected power and branch power after each iteration. The voltage amplitude and voltage phase angle are estimated based on the optimized injected power and branch power.

[0049] Here, c1 is the individual learning factor, representing the tendency of the current particle to tend towards its own historical best position. c2 is the social learning factor, representing the tendency of the current particle to tend towards the group's best position. The improved PSO algorithm dynamically adjusts c1 and c2, decreasing c1 and increasing c2 during iteration to encourage individual exploration in the early stages and group convergence in the later stages, ensuring speed and accuracy in large-scale system compensation. r1 and r2 are randomly generated at each update to increase the randomness of the search and avoid the algorithm getting stuck in local optima. Since they are random numbers, they will be different at each update, making the trajectory during power compensation random. Since the optimization is for the missing power, the initial position is calculated based on the historical voltage nodes and the power flow of input and output power (the convergence error of the power flow calculation is determined to be 0.001 pu). The initial velocity is set to zero. State estimation problems are usually nonlinear and may have multiple local optima, so the initial velocity is set to 0 to avoid missing the optimal region.

[0050] Figure 2 This is a schematic diagram illustrating the effect of voltage amplitude estimation for a bus without positioning feedback and loss compensation provided in an embodiment of the present invention. Figure 2 In the diagram, the horizontal axis contains 30 buses (nodes), and the vertical axis represents the corresponding voltage estimate (i.e., voltage amplitude estimate). Figure 3 This is a schematic diagram illustrating the effect of voltage angle estimation values ​​for a bus without positioning feedback and defect compensation provided in an embodiment of the present invention. Figure 3 In the diagram, the horizontal axis contains 30 busbars (nodes), and the vertical axis represents the corresponding voltage angle estimate (i.e., voltage phase angle estimate). Figure 2 and Figure 3 The voltage amplitude and voltage phase angle are estimated using undamaged measurement data.

[0051] Figure 4 This is a diagram illustrating the estimation error of voltage phase angle and voltage amplitude without defect compensation, provided in an embodiment of the present invention. Figure 4 In this study, assuming the measurement data is accurate and complete, the weighted least squares method of the basic state estimation model is used for iterative calculation, and the estimation error is 0.11%, which meets the calculation requirements.

[0052] In an embodiment of the present invention, to verify the feasibility of the invention, the injected power of the target failed bus and the branch power of the failed critical branch are set to be unavailable. That is, the extreme failure scenario of the target failed bus power failure and the extreme failure scenario of the branch power having spurious data due to power loss are combined. The state estimation results of the network using the present invention are as follows. First, based on the power flow calculation results, the power dissimilarity, branch power and density values ​​of the measurement data are extracted as features. The calculation results for the 30-node network system are shown in Table 1.

[0053] Table 1. Calculation of Feature Value Extraction for Key Nodes of Measurement Vectors In an embodiment of the present invention, Figure 5 A graph of dissimilarity provided in an embodiment of the present invention. Figure 6 The graph shows the branch power density provided in the embodiment of the present invention. Figure 5 and Figure 6 The curve values ​​in the table correspond to those in Table 1. According to Table 1, Figure 5 and Figure 6 Analysis shows that the critical failure nodes for the target include nodes 5, 6, 8, 25, 26, and 28. Figure 7 This is a schematic diagram illustrating the effect of the target failure critical node feedback marking provided in an embodiment of the present invention. G represents a generator, which includes 30 buses and 41 branches, with node 1 being the balancing node. The diagram includes target failure critical nodes such as nodes 5, 6, 8, 25, 26, and 28, with the red area representing the critical branch corresponding to each target failure critical node.

[0054] In an embodiment of the present invention, Figure 8 This is a schematic diagram illustrating the effect of compensating for the injected power of a target failure critical node using the PSO algorithm, as provided in an embodiment of the present invention. Because... Figure 7 The active and reactive power of six nodes (5, 6, 8, 25, 26, and 28) are all deficient, rendering the basic state estimation model ineffective. A particle swarm optimization algorithm is used for injected power compensation, and the voltage amplitude error is calculated as follows: Figure 8 As shown.

[0055] In some embodiments of the present invention, the optimization speed and accuracy of the interference signal testing algorithm are improved by incorporating an inertial weighting factor. Figure 9 This is a graph showing the change of the inertia weight factor throughout the entire iteration process, provided by an embodiment of the present invention. Figure 10 This is a graph showing the changes of c1 and c2 with the number of iterations throughout the entire iteration process, provided as an embodiment of the present invention. Figure 9 As can be seen, adding an inertia weight factor (i.e., ...) to the standard particle swarm optimization algorithm... Figure 9The inertia weighting factor (in the search algorithm) effectively suppresses the continuous oscillation of particles near the optimal solution, prompting the population to quickly converge to a stable search region. Although premature heavy mining may increase the risk of getting trapped in local optima, for problems with relatively smooth search spaces and well-defined optimal regions, this strategy can significantly accelerate the convergence speed. Furthermore, by using extremely low inertia weighting in the later stages, particle motion is entirely dominated by individual and swarm historical optimalities, thus achieving very fine-grained local tuning and significantly improving the accuracy and stability of the final solution. Figure 10 As can be seen, the introduction of the dynamic adjustment mechanism of c1 and c2 into the standard particle swarm optimization algorithm significantly improves the optimization accuracy. Experimental data shows that, regardless of whether it's a positive or linear curve, the objective function value exhibits a stable decreasing trend with increasing iterations. Especially in the later stages of iteration, the curve converges more smoothly, and the final value is lower, indicating that the algorithm can more effectively approximate the global optimum, avoiding premature convergence and improving optimization accuracy and stability. This improvement gives the PSO algorithm stronger search capabilities and higher convergence accuracy when dealing with complex optimization problems.

[0056] In some embodiments of the present invention Figure 11 This is a schematic diagram illustrating the effect of compensating for the voltage amplitude error after adjusting the branch power of the critical branch corresponding to the target critical failure node, as provided in this embodiment of the invention. An extreme data failure scenario is set up, i.e., by superposition operation, the historical measurement data of the critical branch becomes inaccurate, thereby simulating extreme communication interference. The error result of compensating for the branch power using the improved PSO algorithm is shown below. Figure 11 As shown.

[0057] In some embodiments of the present invention, Table 2 shows a comparison of the final convergence errors of the standard PSO and the improved PSO algorithms for defect compensation, as follows: Table 2. Comparison of final convergence errors of standard PSO and improved PSO algorithms for defect compensation. Through path search, based on historical data from the smart grid wide-area measurement system, missing measurement information used for state estimation can be compensated, and the correct direction of particle optimization can be ensured by adjusting the updated fitness value of the particles. Even with node defects and communication-induced spurious data in the historical data, it still exhibits good optimization capabilities, with the absolute error of the improved PSO algorithm remaining within 0.08%.

[0058] Figure 12 This is a schematic diagram illustrating the effect of the present invention on the voltage amplitude estimation value after compensation for defect measurement information and the voltage amplitude estimation value after compensation for defect measurement information. Figure 13This is a schematic diagram illustrating the effect of the present invention on the voltage angle estimation value after compensation for defect measurement information and without compensation for defect measurement information. From... Figure 12 and Figure 13 It can be concluded that even under extreme failure conditions of measurement data, analytical solutions can still be obtained after loss compensation and optimized state estimation, with good accuracy and calculation speed.

[0059] Reference Figure 14 The diagram shows a structural schematic of an electronic device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.

[0060] like Figure 14 As shown, the electronic device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.

[0061] in: The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.

[0062] Communication interface 504 is used to communicate with other electronic devices or servers.

[0063] The processor 502 is used to execute program 510, specifically the relevant steps in the above method embodiments.

[0064] Specifically, program 510 may include program code that includes computer operation instructions.

[0065] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The smart device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.

[0066] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0067] Specifically, program 510 can be used to cause processor 502 to perform the operations corresponding to the methods described in the above method embodiments.

[0068] The specific implementation of each step in program 510 can be found in the corresponding descriptions of the steps and units in the above method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0069] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of the present invention can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.

[0070] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0071] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein 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 implementations should not be considered beyond the scope of the embodiments of the present invention.

[0072] The above embodiments are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A method for estimating and optimizing the measurement failure state of a power system with location feedback, characterized in that, include: Acquire target measurement data of the power system; the target measurement data includes the first injected power of the bus and the first branch power of the branch connected to the bus; The second injected power of the bus and the second branch power of the branch connected to the bus are obtained within a preset historical time period, and the power dissimilarity matrix corresponding to the bus is constructed based on the second injected power. The initial failure critical bus is selected from the bus based on the power dissimilarity matrix; and the density function value corresponding to the initial failure critical bus is obtained based on the power of the second branch corresponding to the initial failure critical bus and the preset density function. Based on the density function value and the screening threshold, the target critical bus and the critical branch connected to the target critical bus are screened out from the initial critical bus. The optimization is performed based on the second injection power, the second branch power, and the inertial particle swarm optimization algorithm until the optimal injection power and the optimal branch power are obtained; where the inertial particle swarm optimization algorithm is a particle swarm optimization algorithm with an exponential change perturbation mechanism. The optimal injected power and optimal branch power are added to the target measurement data to obtain the target measurement data after addition. Based on the target measurement data after addition, the estimated voltage amplitude and voltage phase angle of the bus are obtained.

2. The method according to claim 1, characterized in that, The construction of the power dissimilarity matrix corresponding to the bus based on the second injected power includes: A power matrix is ​​constructed using the second injected power; the power matrix represents the second injected power of different buses at a specific time, and the columns of the power matrix represent the second injected power of the same bus at different times. The dissimilarity is calculated based on the power matrix and the Euclidean distance formula, and a power dissimilarity matrix is ​​constructed based on the dissimilarity. The Euclidean distance formula is as follows: In the above formula, Let M represent the dissimilarity between bus j and bus i, where M represents the number of historical measurement data points, and N represents the number of buses in the historical measurement data. Let i be the active power value of the i-th bus in the k-th historical data. Let i be the reactive power value of the i-th bus in the k-th historical data. This represents the apparent power value of the j-th bus in the k-th historical data point. This represents the apparent power value of the i-th bus in the k-th historical data.

3. The method according to claim 1, characterized in that, The method of selecting the initial failure critical bus based on the power dissimilarity matrix includes: Obtain the initial maximum value of each column in the power dissimilarity matrix, and sort the initial maximum values ​​of each column from high to low to obtain the sorting result; The bus corresponding to the highest initial maximum value in the sorting results is taken as the initial critical failure bus.

4. The method according to claim 1, characterized in that, The power of the second branch includes historical reactive power and historical active power; The process of obtaining the density function value corresponding to the initial failed critical bus based on the power of the second branch corresponding to the initial failed critical bus and the preset density function includes: Substituting the power of the second branch into the preset density function yields the density function value corresponding to the initial failure critical bus; the preset density function is as follows: In the above formula, Let a be the density function value of the a-th initial failure critical busbar. For Gaussian threshold, take , are the second branch power of the xth and yth branches connected to the initial failure critical bus, respectively, and nb is the power of all initial failure critical buses.

5. The method according to claim 1, characterized in that, The optimization based on the second injection power, the second branch power, and the inertial particle swarm optimization algorithm until the optimal injection power and the optimal branch power are obtained includes: Obtain the initial particle swarm represented in matrix form, consisting of the second injection power and the second branch power; An evolutionary equation with an exponential perturbation mechanism, as introduced in the inertial particle swarm optimization algorithm, is used to update the velocity of the initial particle swarm and perform multiple iterations to find the optimal value until the iteration condition is met, thereby obtaining the optimal injection power and the optimal branch power.

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