Power grid short-circuit parameter high-precision measurement method and system based on non-fault disturbance

Abstract

The invention discloses a power grid short-circuit parameter high-precision measurement method and system based on non-fault disturbance. The method comprises the following steps: collecting phasor data of voltage and current by using a multi-point synchronous phasor measurement technology; carrying out data processing and analysis; constructing a dynamic simulation model of the power system, and analyzing dynamic characteristics of a power grid under non-fault disturbance; and an optimization algorithm is used to accurately estimate short-circuit parameters. The method has the advantages that the measurement precision and reliability are improved, real-time monitoring and online analysis are realized, the implementation cost and complexity are reduced, the system stability and safety are improved, and the method flexibly adapts to various application scenes. The beneficial effects provide powerful technical guarantee for safe and stable operation of a power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power system protection, and in particular to a high-precision measurement method and system for power grid short-circuit parameters based on non-fault disturbance. Background Art

[0002] With the continuous expansion of the scale and complexity of power systems, the stable operation and reliable maintenance of power grids have become increasingly important. In modern power systems, accurate measurement and monitoring of power grid short-circuit parameters are the key to ensuring system safety and stability. However, traditional short-circuit parameter measurement methods mainly rely on fault disturbances, which are not only prone to cause system disturbances, but also may bring risks to power equipment and users. Therefore, in recent years, power grid short-circuit parameter measurement technology based on non-fault disturbances has gradually attracted attention. The development of synchronized phasor measurement units (PMUs) and the application of wide area measurement systems (WAMS) have made it possible to collect power grid operation data in real time. These technological advances provide new means for high-precision measurement of power grid short-circuit parameters.

[0003] Nevertheless, the existing related technologies still have some shortcomings in practical applications. First, the traditional short-circuit parameter measurement method relies on large disturbance events, which will not only affect the normal operation of the power grid, but may also cause secondary faults. In addition, the existing methods usually require complex on-site testing and special equipment, with high implementation costs and lack of real-time performance. Secondly, the limitations of data processing and analysis methods also affect the accuracy and reliability of short-circuit parameter measurement. In the prior art, the noise processing and fusion algorithms of the data are still imperfect, and it is difficult to extract high-precision phasor information from multi-point measurement data. Finally, the existing optimization algorithms have the problem of being prone to falling into local optimal solutions during the parameter estimation process, resulting in inaccurate estimation results of short-circuit parameters. Therefore, there is an urgent need for a new method that can achieve high-precision measurement of power grid short-circuit parameters under non-fault disturbance conditions by using multi-point synchronous phasor measurement technology and advanced data processing and optimization algorithms. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by the present invention are: the existing power grid short-circuit parameter measurement method relies on large disturbance events, affecting system stability; the test equipment is expensive and the implementation is complex; the data processing and optimization algorithms are not perfect, resulting in low measurement accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance, comprising:

[0007] Use multi-point synchronized phasor measurement technology to collect voltage and current phasor data;

[0008] Perform data processing and analysis;

[0009] Construct a dynamic simulation model of the power system and analyze the dynamic characteristics of the power grid under non-fault disturbances;

[0010] Using an optimization algorithm, short-circuit parameters are accurately estimated.

[0011] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance described in the present invention, wherein: the multi-point synchronous phasor measurement includes analyzing the power grid topology structure, identifying substations, power plants and key nodes of main transmission lines;

[0012] Considering the weights of different nodes in the power grid, we define the node weight w i ; The power flow between nodes is introduced, and the power grid topology is represented by a weighted directed graph;

[0013] Using the PageRank algorithm, calculate the comprehensive importance score of each node:

[0014]

[0015] Where, d represents the damping factor; M(v i ) indicates pointing to node v i All node sets of L(v j ) represents node v j Out-degree; PR(v i ) represents node v i PageRank score;

[0016] Calculate the power transfer capability of each node:

[0017]

[0018] Among them, P ij Represents the slave node v i To node v j The transmission power of E represents the transmission line;

[0019] Calculate the comprehensive importance index for each node:

[0020] I(v i )=α·PR(v i )+β·C(v i )

[0021] Among them, α and β represent weight coefficients, reflecting the weights of node PageRank and power transmission capacity in the comprehensive importance index;

[0022] Evaluate historical operating data to identify nodes with large load variations and high failure frequencies;

[0023] Considering the volatility of load changes in time series, the standard deviation is used to measure the fluctuation amplitude, and the formula is expressed as:

[0024]

[0025] Among them, ΔP(t)=P(t)-P(t-1), P(t) represents the load at time t; μ P represents the average value of load change rate, T represents the length of time series;

[0026] The weighted moving average method is used to smooth the load change data and eliminate the influence of seasonal and cyclical factors. The formula is expressed as:

[0027]

[0028] Among them, w k represents the weight coefficient, usually Gaussian distribution is selected; ΔP s (t) represents the load change rate after smoothing at time t; n is the window size in the smoothing calculation;

[0029] Considering the severity and recovery time of the fault, the weighted fault frequency is defined, and the formula is expressed as:

[0030]

[0031] Among them, S i represents the severity weight of the ith fault, T i represents the fault duration, N represents the number of faults; TOP represents the total observation period; PMUs are installed at the identified key nodes, and time synchronization of all measurement points is ensured through time synchronization technology.

[0032] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance described in the present invention, the data processing and analysis includes real-time acquisition of voltage and current phasor data, processing through filtering and fusion algorithms, performing discrete wavelet transform on the data of each measurement point, and extracting multi-scale features; performing wavelet decomposition on the voltage v(t) and current i(t) signals respectively, and the formula is expressed as follows:

[0033]

[0034] Among them, W v (j,k) represents the j-th layer and k-th wavelet coefficient of the voltage signal; W i (j,k) represents the j-th layer and k-th wavelet coefficient of the current signal; ψ j,k (n) represents the wavelet basis function;

[0035] The wavelet coefficients are reconstructed into the original signal, and the denoised signal is obtained through inverse wavelet transform:

[0036]

[0037] The data is standardized, and the standardized data of multiple measurement points are weighted and fused to obtain a comprehensive signal, which is then analyzed.

[0038] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance described in the present invention, the dynamic simulation model includes using differential equations to describe the dynamic behavior of the generator; establishing a second-order synchronous generator model, the formula is expressed as:

[0039]

[0040] Among them, δ represents the rotor angle, ω represents the speed of the generator, and ω s represents synchronous speed, M represents inertia constant, P m Represents mechanical input power; P e represents the electrical output power; D represents the damping coefficient;

[0041] The transmission line is represented by a π-type equivalent circuit, and the dynamic behavior is described by an algebraic equation, which is expressed as follows:

[0042]

[0043] Among them, I ij represents the current from node i to node j, V i and V j Represent the voltages of nodes i and j respectively, Z ij It represents the impedance of the transmission line;

[0044] The dynamic behavior of the load is represented by an exponential model:

[0045]

[0046] Among them, P L represents load power; P0 represents reference load power; V represents voltage; V0 represents reference voltage; k represents exponent.

[0047] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance described in the present invention, wherein: the dynamic characteristics of the power grid under non-fault disturbance include simulating non-fault disturbance events and setting the amplitude and duration of the disturbance;

[0048] Train the dynamic prediction model and select the machine learning gated recurrent unit model for dynamic prediction; use historical data to train the model and optimize the model parameters to ensure that the model can accurately predict the dynamic behavior of the power grid;

[0049] Calibrate the physical model, combine the physical model with the data-driven model, and use the data-driven model to supplement and correct the physical model; set the simulation scenario, set the simulation time step and total time, and determine the type and range of disturbances that need to be analyzed; simulate the dynamic response of the power grid under disturbances, and record changes in voltage, current, and frequency; perform data analysis on the simulation results and extract the dynamic characteristics of the power grid.

[0050] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance described in the present invention, wherein: the optimization algorithm includes initializing the population

[0051] Each individual represents a possible short-circuit parameter combination, including resistance R and reactance X parameters; the individual coding adopts real number coding, and each individual can be represented as a vector x = [R, X, ...];

[0052] Randomly generate N individuals to form the initial population P(0), ensuring that the parameter values ​​are within a reasonable range:

[0053]

[0054] Parameter values ​​are randomly generated within a preset range:

[0055] R i ∈[R min ,R max] ,X i ∈[X min ,X max ]

[0056] The fitness function measures the quality of each individual and is calculated based on the error between the simulation results and the actual measurement data; the fitness function f(x) is expressed as:

[0057]

[0058] Error function:

[0059]

[0060] Where M represents the number of measurement points. and Indicates the actual measured voltage and current, and Represents the voltage and current obtained by simulation; ∈ represents a small positive number to prevent division by zero;

[0061] Using the roulette wheel selection strategy, individuals are selected for reproduction based on fitness; the probability calculation formula for roulette wheel selection is expressed as:

[0062]

[0063] Among them, the probability of the i-th individual being selected;

[0064] Use simulated binary crossover SBX to perform crossover operations to generate new individuals. The formula of SBX is expressed as:

[0065] x child1 =0.5((1+β q )x1+(1-β q )x2)

[0066] x child2 =0.5((1-β q )x1+(1+β q )x2)

[0067] Among them, β q represents a distribution index generated by random numbers, x child1 represents the first offspring individual generated by the crossover operation; x child2 represents the second offspring individual generated by the crossover operation; x1, x2 represent the two selected parent individuals; β q It represents the distribution index of simulated binary crossover. The specific calculation formula is expressed as:

[0068]

[0069] Among them, u represents a uniformly distributed random number, and q represents the distribution index of SBX;

[0070] Use multi-point mutation operations to increase population diversity; the Gaussian mutation formula is expressed as:

[0071]

[0072] Among them, σ is the mutation intensity, It is a Gaussian distribution with a mean of 0 and a standard deviation of 1;

[0073] Dynamically adjust the mutation intensity according to fitness to balance exploration and development:

[0074]

[0075] Among them, σ0 represents the initial mutation intensity, t represents the current generation, T max represents the maximum algebra;

[0076] The new individuals generated by selection, crossover and mutation operations form the next generation population P(t+1); set the convergence threshold of the fitness function ∈ stop , the iteration stops when the convergence condition is met.

[0077] As a preferred solution of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbances described in the present invention, the accurate estimation of short-circuit parameters includes, after the iteration, selecting the individual with the largest fitness function value as the optimal solution, that is, the estimated optimal short-circuit parameter, which is expressed as follows:

[0078]

[0079] After each iteration of the genetic algorithm, the optimal individual and overall fitness values ​​of the current population are analyzed to adjust the population parameters and improve the estimation accuracy.

[0080] A high-precision measurement system for short-circuit parameters of a power grid based on non-fault disturbances using any method as described in the present invention, wherein:

[0081] The data measurement module uses multi-point synchronous phasor measurement technology to collect voltage and current phasor data and ensure the time synchronization of all measurement points;

[0082] The data processing module filters and fuses the collected voltage and current phasor data to extract useful information;

[0083] Power system dynamic simulation module, which builds a dynamic simulation model of the power system and analyzes the dynamic characteristics of the power grid under non-fault disturbances;

[0084] The optimization estimation module uses a modified genetic algorithm for optimization to accurately estimate the short-circuit parameters of the power grid and improves the estimation accuracy through a feedback adjustment mechanism.

[0085] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0086] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0087] The beneficial effects of the present invention are as follows: by proposing a high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance, it has the advantages of improving measurement accuracy and reliability, realizing real-time monitoring and online analysis, reducing implementation costs and complexity, improving system stability and safety, and flexibly adapting to a variety of application scenarios. These beneficial effects provide a strong technical guarantee for the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0089] Figure 1 An overall flow chart of a method for high-precision measurement of power grid short-circuit parameters based on non-fault disturbance provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0090] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0091] Example 1

[0092] Reference Figure 1 , which is an embodiment of the present invention, provides a high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance, comprising:

[0093] S1: Use multi-point synchronized phasor measurement technology to collect voltage and current phasor data.

[0094] Furthermore, multi-point synchronized phasor measurements include analyzing the grid topology and identifying key nodes of substations, power plants and main transmission lines.

[0095] Furthermore, considering the weights of different nodes in the power grid, we define the node weight w i ; The power flow between nodes is introduced, and the power grid topology is represented by a weighted directed graph.

[0096] Furthermore, the PageRank algorithm is used to calculate the comprehensive importance score of each node:

[0097]

[0098] Where, d represents the damping factor; M(v i ) indicates pointing to node v i All node sets of L(v j ) represents node v j Out-degree; PR(v i) represents node v i PageRank score.

[0099] Going further, calculate the power transfer capacity of each node:

[0100]

[0101] Among them, P ij Represents the slave node v i To node v j transmission power; E represents the transmission line.

[0102] Furthermore, the comprehensive importance index of each node is calculated:

[0103] I(v i )=α·PR(v i )+β·C(v i )

[0104] Among them, α and β represent weight coefficients, reflecting the weights of node PageRank and power transmission capacity in the comprehensive importance index.

[0105] Furthermore, historical operating data is evaluated to identify nodes with large load variations and high failure frequencies.

[0106] Furthermore, considering the volatility of load changes in the time series, the standard deviation is used to measure the fluctuation amplitude, and the formula is expressed as:

[0107]

[0108] Among them, ΔP(t)=P(t)-P(t-1), P(t) represents the load at time t; μ P represents the average value of the load change rate, and T represents the length of the time series.

[0109] Furthermore, the weighted moving average method is used to smooth the load change data and eliminate the influence of seasonal and cyclical factors. The formula is expressed as:

[0110]

[0111] Among them, w k represents the weight coefficient, usually Gaussian distribution is selected; ΔP s (t) represents the load change rate after smoothing at time t; n is the window size in the smoothing calculation.

[0112] Furthermore, considering the severity and recovery time of the fault, a weighted fault frequency is defined, which is expressed as:

[0113]

[0114] Among them, S i represents the severity weight of the ith fault, T i represents the fault duration, N represents the number of faults; TOP represents the total observation period; PMUs are installed at the identified key nodes, and time synchronization of all measurement points is ensured through time synchronization technology.

[0115] It should be noted that by using multi-point synchronous phasor measurement technology (PMU) to collect phasor data of voltage and current, and using advanced data processing methods such as multi-source data fusion and wavelet transform, noise can be effectively filtered out and key information can be extracted. By constructing a dynamic simulation model of the power system, the real dynamic behavior of the power grid can be simulated under non-fault disturbances, and the short-circuit parameters can be accurately estimated by combining optimization algorithms. This method overcomes the limitations of traditional single-point measurement and reliance on fault disturbances, and greatly improves the accuracy and reliability of short-circuit parameter measurement.

[0116] S2: Perform data processing and analysis.

[0117] Furthermore, the data processing and analysis includes real-time acquisition of voltage and current phasor data, processing through filtering and fusion algorithms, performing discrete wavelet transform on the data of each measurement point, and extracting multi-scale features; performing wavelet decomposition on the voltage v(t) and current i(t) signals respectively, and the formula is expressed as follows:

[0118]

[0119] Among them, W v (j,k) represents the j-th layer and k-th wavelet coefficient of the voltage signal; W i (j,k) represents the j-th layer and k-th wavelet coefficient of the current signal; ψ j,k (n) represents the wavelet basis function.

[0120] Furthermore, the wavelet coefficients are reconstructed into the original signal, and the denoised signal is obtained through inverse wavelet transform:

[0121]

[0122] Furthermore, the data is standardized, and the standardized data of multiple measurement points are weighted and fused to obtain a comprehensive signal, which is then analyzed.

[0123] It should be noted that traditional short-circuit parameter measurement methods usually require complex on-site testing and special equipment, with high implementation costs and cumbersome processes. Our invention can achieve high-precision short-circuit parameter measurement under non-fault disturbance conditions through existing PMU equipment and data processing technology, without the need for additional test equipment and intervention operations, significantly reducing implementation costs and complexity. At the same time, by optimizing algorithms and feedback adjustment mechanisms, the estimation accuracy is further improved and the operation process is simplified.

[0124] S3: Construct a dynamic simulation model of the power system and analyze the dynamic characteristics of the power grid under non-fault disturbances.

[0125] Furthermore, the dynamic simulation model includes using differential equations to describe the dynamic behavior of the generator; establishing a second-order synchronous generator model, the formula is expressed as:

[0126]

[0127] Among them, δ represents the rotor angle, ω represents the speed of the generator, and ω s represents synchronous speed, M represents inertia constant, P m Represents mechanical input power; P e represents the electrical output power; D represents the damping coefficient.

[0128] Furthermore, the transmission line is represented by a π-type equivalent circuit, and the dynamic behavior is described by an algebraic equation, which is expressed as follows:

[0129]

[0130] Among them, I ij represents the current from node i to node j, V i and V j Represent the voltages of nodes i and j respectively, Z ij Represents the impedance of the transmission line.

[0131] Furthermore, the dynamic behavior of the load is represented by an exponential model:

[0132]

[0133] Among them, P L represents load power; P0 represents reference load power; V represents voltage; V0 represents reference voltage; k represents exponent.

[0134] Furthermore, the dynamic characteristics of the power grid under non-fault disturbances include simulating non-fault disturbance events and setting the amplitude and duration of the disturbances.

[0135] Furthermore, a dynamic prediction model is trained and a machine learning gated recurrent unit model is selected for dynamic prediction. The model is trained using historical data and the model parameters are optimized to ensure that the model can accurately predict the dynamic behavior of the power grid.

[0136] Furthermore, the physical model is calibrated, the physical model and the data-driven model are combined, and the data-driven model is used to supplement and correct the physical model; the simulation scenario is set, the simulation time step and total time are set, and the type and range of disturbances that need to be analyzed are determined; the dynamic response of the power grid under disturbance is simulated, and the changes in voltage, current, and frequency are recorded; data analysis is performed on the simulation results to extract the dynamic characteristics of the power grid.

[0137] It should be noted that, thanks to the application of PMU and wide area measurement system (WAMS), our invention can realize real-time monitoring of the operation status of the power grid. Multi-point synchronous phasor measurement technology ensures high accuracy and time synchronization of data. Through online data processing and analysis, it can timely detect abnormal conditions in the operation of the power grid and estimate short-circuit parameters. This real-time monitoring and online analysis capability helps to improve the operational safety and stability of the power grid, take preventive measures in a timely manner, and reduce the risk of failure.

[0138] S4: Use optimization algorithms to accurately estimate short-circuit parameters.

[0139] Furthermore, the optimization algorithm includes initializing the population, each individual represents a possible short-circuit parameter combination, including resistance R and reactance X parameters; the individual encoding adopts real number encoding, and each individual can be represented as a vector x=[R,X,…].

[0140] Furthermore, N individuals are randomly generated to form the initial population P(0), ensuring that the parameter values ​​are within a reasonable range:

[0141] x i =[R i ,X i ,…],i=1,2,…,N

[0142] Furthermore, parameter values ​​are randomly generated within a preset range:

[0143] R i ∈[R min ,R max ],X i ∈[X min ,X max ]

[0144] Furthermore, the fitness function measures the quality of each individual and is calculated based on the error between the simulation results and the actual measurement data; the fitness function f(x) is expressed as:

[0145]

[0146] Furthermore, the error function:

[0147]

[0148] Where M represents the number of measurement points. and Indicates the actual measured voltage and current, and Represents the voltage and current obtained by simulation; ∈ represents a small positive number to prevent division by zero.

[0149] Furthermore, a roulette wheel selection strategy is used to select individuals for reproduction based on fitness; the probability calculation formula for roulette wheel selection is expressed as:

[0150]

[0151] Among them, the probability of the i-th individual being selected.

[0152] Furthermore, simulated binary crossover SBX is used to perform crossover operations to generate new individuals. The formula of SBX is expressed as:

[0153] x child1 =0.5((1+β q )x1+(1-β q )x2)

[0154] x child2 =0.5((1-β q )x1+(1+β q )x2)

[0155] Among them, β q represents a distribution index generated by random numbers, x child1 represents the first offspring individual generated by the crossover operation; x child2 represents the second offspring individual generated by the crossover operation; x1, x2 represent the two selected parent individuals; β q It represents the distribution index of simulated binary crossover. The specific calculation formula is expressed as:

[0156]

[0157] Among them, u represents a uniformly distributed random number, and q represents the distribution index of SBX.

[0158] Furthermore, multi-point mutation operations are used to increase population diversity; the Gaussian mutation formula is expressed as:

[0159]

[0160] Among them, σ is the mutation intensity, is a Gaussian distribution with mean 0 and standard deviation 1.

[0161] Furthermore, the mutation intensity is dynamically adjusted according to the fitness to balance exploration and development:

[0162]

[0163] Among them, σ0 represents the initial mutation intensity, t represents the current generation, T max Represents the maximum number of generations.

[0164] Furthermore, the new individuals generated by selection, crossover and mutation operations constitute the next generation population P(t+1); the convergence threshold of the fitness function is set ∈ stop , the iteration stops when the convergence condition is met.

[0165] Furthermore, the short-circuit parameter is accurately estimated, and after the iteration, the individual with the largest fitness function value is selected as the optimal solution, that is, the estimated optimal short-circuit parameter, which is expressed as follows:

[0166]

[0167] Furthermore, after each iteration of the genetic algorithm, the population parameters are adjusted by analyzing the optimal individual and overall fitness values ​​of the current population to improve the estimation accuracy.

[0168] It should be noted that the method invented by us is applicable to various power grid structures and operating environments. Whether it is a large power grid, a regional power grid or a distributed power grid, it can achieve high-precision short-circuit parameter measurement through corresponding configuration and adjustment. Through adaptive optimization algorithms and dynamic simulation models, it can flexibly respond to different types of power systems and provide personalized solutions, which has wide applicability and promotion value.

[0169] On the other hand, this embodiment also provides a high-precision measurement system for power grid short-circuit parameters based on non-fault disturbance, which includes:

[0170] The data measurement module uses multi-point synchronous phasor measurement technology to collect the phasor data of voltage and current and ensure the time synchronization of all measurement points.

[0171] The data processing module filters and fuses the collected voltage and current phase data to extract useful information.

[0172] The power system dynamic simulation module builds a dynamic simulation model of the power system and analyzes the dynamic characteristics of the power grid under non-fault disturbances.

[0173] The optimization estimation module uses a modified genetic algorithm for optimization to accurately estimate the short-circuit parameters of the power grid and improves the estimation accuracy through a feedback adjustment mechanism.

[0174] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0175] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0176] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0177] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0178] Example 2

[0179] The following is an embodiment of the present invention, which provides a high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0180] In order to verify the effectiveness and superiority of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance, a detailed experimental study was conducted. The experiment was carried out in a simulated power grid environment, and the system included a power plant, three substations and two main transmission lines. The test objects were the key nodes of the power grid, and the voltage and current phasor data of these nodes were collected through multi-point synchronous phasor measurement technology (PMU). The PMU equipment was time synchronized through GPS to ensure high accuracy and synchronization of the data.

[0181] The specific steps are as follows:

[0182] PMU devices are installed at key nodes of the power grid (power plants, substation A, substation B, substation C and main transmission lines). These devices collect voltage and current phasor data of each node in real time. The sampling frequency of each PMU is set to 30 times per second to ensure that the dynamic changes of the power grid are captured.

[0183] Through GPS synchronized clock, the time synchronization accuracy of all PMUs reaches microsecond level, ensuring the timing consistency of phasor data.

[0184] The collected data is first preprocessed, including removing noise and outliers. Wavelet transform is used to denoise the voltage and current signals and extract the multi-scale features of the signals.

[0185] The phasor data of different nodes are standardized to eliminate the dimension difference. Then, the data of multiple measurement points are fused into a comprehensive signal through the weighted fusion method.

[0186] Use power system simulation software (such as PSCAD) to build a dynamic simulation model of the power grid. The model includes the dynamic equations of the generator, the electrical parameters of the transmission line, and the load model. Set initial conditions and non-fault disturbance events (such as load changes, wind power output fluctuations, etc.).

[0187] Run the simulation software to simulate the dynamic response of the power grid under different disturbances and obtain simulation data. The simulation data includes the change of voltage and current over time, which is used for subsequent parameter estimation.

[0188] An improved genetic algorithm is used to estimate short-circuit parameters. First, the population is initialized, and each individual represents a possible short-circuit parameter combination (resistance RRR, reactance XXX). Through selection, crossover and mutation operations, a new generation of population is evolved.

[0189] Define the fitness function and take the error between the simulation data and the actual measurement data as the optimization target. Use the feedback adjustment mechanism to dynamically adjust the crossover probability, mutation probability and mutation intensity to ensure the convergence and accuracy of the algorithm.

[0190] Iterate until the convergence condition (maximum number of iterations or fitness convergence threshold) is met, and output the optimal solution, that is, the estimated optimal short-circuit parameter.

[0191] Table 1 Test data record table

[0192]

[0193] Tabular data analysis

[0194] Through the analysis of experimental data, it can be seen that the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance has significant advantages in power grid short-circuit parameter estimation. First of all, from the data of collected voltage and current, it can be seen that at all key nodes, the actual measured values ​​are very close to the simulation values. For example, the collected voltage of the power plant is 115.2kV, while the simulated voltage is 115.0kV, and the collected current is 245A, while the simulated current is 242A. This shows that the simulation model can accurately reflect the actual operating status of the power grid.

[0195] Secondly, the short-circuit parameters estimated by the improved genetic algorithm show high consistency and accuracy. The data in the table show that the estimated values ​​of short-circuit resistance and reactance of each node fluctuate within a reasonable range. For example, the estimated value of short-circuit resistance of substation A is 0.48Ω, and the estimated value of short-circuit reactance is 0.33Ω, while the estimated value of short-circuit resistance of transmission line 2 is 0.46Ω, and the estimated value of short-circuit reactance is 0.31Ω. The deviation between the estimated value of each node and the expected value is small, indicating that the algorithm has high accuracy in estimating short-circuit parameters.

[0196] Further compared with the prior art, the traditional short-circuit parameter measurement method usually relies on large disturbance events, which not only affects the system stability, but may also cause secondary faults. The present invention is based on non-fault disturbance conditions, and estimates parameters through small disturbances under normal operating conditions, avoiding additional disturbances to the system and greatly improving the safety and stability of the system. In addition, the traditional method requires complex on-site testing and special equipment, which is costly and complex to implement, while the present invention only requires existing PMU equipment and data processing technology, significantly reducing the implementation cost and complexity.

[0197] In terms of data processing and analysis, the present invention uses wavelet transform and data fusion technology to effectively remove noise and extract multi-scale features, thereby improving data quality and measurement accuracy. However, traditional methods have limitations in data processing and it is difficult to extract high-precision phasor information from multi-point measurement data. In addition, by optimizing the algorithm and feedback adjustment mechanism, the algorithm parameters are dynamically adjusted, which improves the estimation accuracy and convergence speed, and avoids the problem of falling into the local optimal solution.

[0198] In summary, the experimental data fully demonstrate the innovation and superiority of the high-precision measurement method of power grid short-circuit parameters based on non-fault disturbance. In terms of measurement accuracy, real-time monitoring, safety, cost and implementation complexity, this method is superior to the existing technology, providing a strong technical guarantee for the safe and stable operation of the power grid, and has important practical application value and promotion prospects.

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A high-precision measurement method for short-circuit parameters of a power grid based on non-fault disturbance, characterized in that: include: Use multi-point synchronized phasor measurement technology to collect voltage and current phasor data; Perform data processing and analysis; Construct a dynamic simulation model of the power system and analyze the dynamic characteristics of the power grid under non-fault disturbances; Using an optimization algorithm, short-circuit parameters are accurately estimated.

2. The high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance according to claim 1, characterized in that: The multi-point synchronized phasor measurement includes analyzing the topology of the power grid and identifying key nodes of substations, power plants and main transmission lines; Considering the weights of different nodes in the power grid, we define the node weight w i ; The power flow between nodes is introduced, and the power grid topology is represented by a weighted directed graph; Using the PageRank algorithm, calculate the comprehensive importance score of each node: Where, d represents the damping factor; M(v i ) indicates pointing to node v i All node sets of L(v j ) represents the out-degree of node vj; PR(v i ) represents node v i PageRank score; Calculate the power transfer capability of each node: Among them, P ij Represents the slave node v i To node v j The transmission power of E represents the transmission line; Calculate the comprehensive importance index for each node: I(v i )=α PR(v i )+β C(v i , Among them, α and β represent weight coefficients, reflecting the weights of node PageRank and power transmission capacity in the comprehensive importance index; Evaluate historical operating data to identify nodes with large load variations and high failure frequencies; Considering the volatility of load changes in time series, the standard deviation is used to measure the fluctuation amplitude, and the formula is expressed as: Among them, ΔP(t)=P(t)-P(t-1), P(t) represents the load at time t; μ P represents the average value of load change rate, T represents the length of time series; The weighted moving average method is used to smooth the load change data and eliminate the influence of seasonal and cyclical factors. The formula is expressed as: Among them, W k represents the weight coefficient, usually Gaussian distribution is selected; ΔP s (t) represents the load change rate after smoothing at time t; n is the window size in the smoothing calculation; Considering the severity and recovery time of the fault, the weighted fault frequency is defined, and the formula is expressed as: Among them, S i represents the severity weight of the ith fault, T i represents the fault duration, N represents the number of faults; TOP represents the total observation period; PMUs are installed at the identified key nodes, and time synchronization of all measurement points is ensured through time synchronization technology.

3. The high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance according to claim 2, characterized in that: The data processing and analysis includes real-time acquisition of voltage and current phasor data, processing through filtering and fusion algorithms, performing discrete wavelet transform on the data of each measurement point, and extracting multi-scale features; The voltage v(t) and current i(t) signals are decomposed by wavelet respectively, and the formula is expressed as follows: Among them, W v (j, k) represents the j-th layer and k-th wavelet coefficient of the voltage signal; W i (j, k) represents the j-th layer and k-th wavelet coefficient of the current signal; ψ j，k (n) represents the wavelet basis function; The wavelet coefficients are reconstructed into the original signal, and the denoised signal is obtained through inverse wavelet transform: The data is standardized, and the standardized data of multiple measurement points are weighted and fused to obtain a comprehensive signal, which is then analyzed.

4. The high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance according to claim 3, characterized in that: The dynamic simulation model includes using differential equations to describe the dynamic behavior of the generator; establishing a second-order synchronous generator model, which is expressed as follows: Among them, δ represents the rotor angle, ω represents the speed of the generator, and ω s represents synchronous speed, M represents inertia constant, P m Represents mechanical input power; P e represents the electrical output power; D represents the damping coefficient; The transmission line is represented by a π-type equivalent circuit, and the dynamic behavior is described by an algebraic equation, which is expressed as follows: Among them, I ij represents the current from node i to node j, V i and V j Represent the voltages of nodes i and j respectively, Z ij It represents the impedance of the transmission line; The dynamic behavior of the load is represented by an exponential model: Among them, P L represents load power; P0 represents reference load power; V represents voltage; V0 represents reference voltage; k represents exponent.

5. The high-precision measurement method for short-circuit parameters of a power grid based on non-fault disturbance according to claim 4, characterized in that: The dynamic characteristics of the power grid under non-fault disturbances include simulating non-fault disturbance events and setting the amplitude and duration of the disturbance; Train the dynamic prediction model and select the machine learning gated recurrent unit model for dynamic prediction; use historical data to train the model and optimize the model parameters to ensure that the model can accurately predict the dynamic behavior of the power grid; Calibrate the physical model, combine the physical model with the data-driven model, and use the data-driven model to supplement and correct the physical model; set the simulation scenario, set the simulation time step and total time, and determine the type and range of disturbances that need to be analyzed; simulate the dynamic response of the power grid under disturbances, and record changes in voltage, current, and frequency; perform data analysis on the simulation results and extract the dynamic characteristics of the power grid.

6. The high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance according to claim 5, characterized in that: The optimization algorithm includes initializing the population Each individual represents a possible short-circuit parameter combination, including resistance R and reactance X parameters; the individual coding adopts real number coding, and each individual can be represented as a vector x = [R, X, ...]; Randomly generate N individuals to form the initial population P(0), ensuring that the parameter values ​​are within a reasonable range: x i =[R i ,X i ,...],i=1,2,...,N Parameter values ​​are randomly generated within a preset range: R i ∈[R min ,R max ],X i ∈[X min ,X max ] The fitness function measures the quality of each individual and is calculated based on the error between the simulation results and the actual measurement data; the fitness function f(x) is expressed as: Error function: Where M represents the number of measurement points. and Indicates the actual measured voltage and current, and Represents the voltage and current obtained by simulation; ∈ represents a small positive number to prevent division by zero; Using the roulette wheel selection strategy, individuals are selected for reproduction based on fitness; the probability calculation formula for roulette wheel selection is expressed as: Among them, the probability of the i-th individual being selected; Use simulated binary crossover SBX to perform crossover operations to generate new individuals. The formula of SBX is expressed as: x child1 =0.5((1+β q )x1+(1-β q )x2) x child2 =0.5((1-β q )x1+(1+β q )x2) Among them, β q represents a distribution index generated by random numbers, x child1 represents the first offspring individual generated by the crossover operation; x child2 represents the second offspring individual generated by the crossover operation; x1, x2 represent the two selected parent individuals; β q It represents the distribution index of simulated binary crossover. The specific calculation formula is expressed as: Among them, u represents a uniformly distributed random number, and q represents the distribution index of SBX; Use multi-point mutation operations to increase population diversity; the Gaussian mutation formula is expressed as: Among them, σ is the mutation intensity, It is a Gaussian distribution with a mean of 0 and a standard deviation of 1; Dynamically adjust the mutation intensity according to fitness to balance exploration and development: Among them, σ0 represents the initial mutation intensity, t represents the current generation, T max represents the maximum algebra; The new individuals generated by selection, crossover and mutation operations form the next generation population P(t+1); set the convergence threshold ∈st of the fitness function op , the iteration is stopped when the convergence condition is met.

7. The high-precision measurement method for power grid short-circuit parameters based on non-fault disturbance according to claim 6, characterized in that: The accurate estimation of the short-circuit parameters includes, after the iteration, selecting the individual with the largest fitness function value as the optimal solution, that is, the estimated optimal short-circuit parameter, which is expressed as: After each iteration of the genetic algorithm, the optimal individual and overall fitness values ​​of the current population are analyzed to adjust the population parameters and improve the estimation accuracy.

8. A high-precision measurement system for power grid short-circuit parameters based on non-fault disturbances using the method according to any one of claims 1 to 7, characterized in that: The data measurement module uses multi-point synchronous phasor measurement technology to collect voltage and current phasor data and ensure the time synchronization of all measurement points; The data processing module filters and fuses the collected voltage and current phasor data to extract useful information; Power system dynamic simulation module, which builds a dynamic simulation model of the power system and analyzes the dynamic characteristics of the power grid under non-fault disturbances; The optimization estimation module uses a modified genetic algorithm for optimization to accurately estimate the short-circuit parameters of the power grid and improves the estimation accuracy through a feedback adjustment mechanism.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the processor implements the steps of a high-precision measurement method for power grid short-circuit parameters based on non-fault disturbances.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for high-precision measurement of power grid short-circuit parameters based on non-fault disturbance are implemented.

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