Power system load model parameter identification method and device

Abstract

The present invention provides a method and device for identifying parameters of a power system load model, comprising: for any individual in a contemporary population, comparing the fitness of the individual before and after the mutation operation of the previous generation, and adjusting the mutation operator and the crossover operator according to the comparison result; wherein the fitness of the individual is the distance between the predicted power and the measured power of the power system load model; performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether the result of the mutation operation is accepted by the individual according to the adjusted crossover operator; performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and using the individual after the selection operation as the individual of the next generation until a preset termination condition is met; using the individual with the smallest fitness in the last generation population as the optimal parameter to be identified of the power system load model. The present invention accelerates the convergence speed of the algorithm and improves the accuracy of parameter identification.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and device for identifying parameters of a load model of a power system. Background Art

[0002] Synchronous phasor measurement technology is widely used in power grids, providing a rich and accurate data source for power system load modeling research. Common power system load models include comprehensive load models and static load models. The load model in the existing simulation system mainly adopts the "ZIP load model + third-order electromechanical transient motor model", which has many dominant parameters. The basic optimization algorithm is prone to premature identification, resulting in inaccurate model parameters.

[0003] At present, the methods for determining the parameters of the power system load model include fault fitting method, overall measurement method and statistical synthesis method. Among them, the fault fitting method compares the measured data of the fault disturbance with the transient response curve of the model, and finally obtains the optimal parameters of the model by continuously adjusting the parameters. However, there are usually few faults in the power system, and the fault time is short, so the amount of fault disturbance data obtained is small. When fitting these fault disturbance data, it is easy to have multiple solutions.

[0004] Although the overall identification method can obtain a large amount of measured data, which greatly reduces the situation of multiple solutions in the fitting process. However, this identification method belongs to a relatively complex set of nonlinear equations, which can easily fall into the local optimal solution, making the parameter identification inaccurate. In addition, some relevant scholars have used the basic differential evolution algorithm to identify the parameters of the power system load model, but the basic differential evolution algorithm is prone to convergence to the local optimal solution or algorithm stagnation as the number of evolutionary generations increases, and the convergence speed is slow. Summary of the invention

[0005] The present invention provides a method and device for identifying parameters of a load model of an electric power system, which are used to solve the defects of inaccurate parameter identification and slow algorithm convergence in the prior art, thereby accelerating the algorithm convergence speed and improving the accuracy of parameter identification.

[0006] The present invention provides a method for identifying parameters of a power system load model, comprising:

[0007] For any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and crossover operator are adjusted according to the comparison results;

[0008] The individual is a parameter to be identified of the power system load model, the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified;

[0009] Perform a mutation operation on the individual according to the adjusted mutation operator, and determine whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator;

[0010] According to the fitness of the individual before the mutation operation and after the crossover operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation until the preset termination condition is met;

[0011] The individual with the smallest fitness in the last generation population is used as the optimal parameter to be identified of the power system load model.

[0012] According to a method for identifying parameters of a load model of a power system provided by the present invention, for any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and the crossover operator are adjusted according to the comparison result, including:

[0013] If the fitness of the individual after the previous generation mutation operation is greater than the fitness before the mutation operation, the mutation operator is adjusted so that the mutation operator changes randomly within a first preset range;

[0014] If the fitness of the individual after the previous generation mutation operation is less than or equal to the fitness before the mutation operation, the crossover operator is adjusted so that the crossover operator changes randomly within a second preset range.

[0015] According to a method for identifying parameters of a power system load model provided by the present invention, a mutation operator is adjusted by the following formula so that the mutation operator changes randomly within a first preset range:

[0016]

[0017] Among them, F i (G+1) is the mutation operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ1 is the first preset value.

[0018] According to a method for identifying parameters of a power system load model provided by the present invention, a hybrid operator is adjusted by the following formula so that the hybrid operator changes randomly within a second preset range:

[0019]

[0020] in, is the crossover operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ2 is the second preset value.

[0021] According to a method for identifying parameters of a power system load model provided by the present invention, the selection operation is performed on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, comprising:

[0022] Calculating the predicted power according to the to-be-identified parameter of the individual before the mutation operation, and obtaining the fitness of the individual according to the Euclidean distance between the predicted power and the measured power obtained in advance;

[0023] Adding the fitness of all individuals in the contemporary population to obtain the total fitness of the contemporary population;

[0024] Add the fitness of all individuals in the previous generation population of the current population before the mutation operation to obtain the total fitness of the previous generation population;

[0025] If the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than a first preset threshold, the value of the first counter is cumulatively increased by 1;

[0026] If the value of the first counter is greater than a second preset threshold, the fitness of the individual before the mutation operation and after the crossover operation is calculated based on the Mahalanobis distance, and a selection operation is performed on the individual.

[0027] According to a method for identifying parameters of a power system load model provided by the present invention, performing a mutation operation on the individual according to an adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to an adjusted crossover operator, comprises:

[0028] If, during the crossover operation on the individual, the adjusted crossover operator satisfies the preset conditions, the result of the mutation operation on the individual is accepted, and all the parameters to be identified of the individual after the mutation operation are taken as the result of the crossover operation on the individual;

[0029] Accordingly, performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator, further includes:

[0030] If the adjusted hybridization operator does not meet the preset conditions during the hybridization operation on the individual, all the parameters to be identified of the individual before the mutation operation are taken as the result of the hybridization operation on the individual.

[0031] According to a method for identifying parameters of a power system load model provided by the present invention, the selection operation is performed on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, comprising:

[0032] If the contemporary population is not the first generation population, the individual is searched for from the individuals of the previous generation population of the contemporary population after the selection operation according to the index of the individual;

[0033] If the individual is found, the fitness of the individual after the selection operation of the previous generation is used as the fitness of the individual before the mutation operation of the current generation;

[0034] If the parameters to be identified of the individual after the previous generation selection operation are the same as the parameters to be identified of the individual after the current hybridization operation, the fitness of the individual after the previous generation selection operation is taken as the fitness of the individual after the current hybridization operation;

[0035] A selection operation is performed on the individual according to its fitness before the mutation operation and after the hybridization operation.

[0036] According to a method for identifying parameters of a power system load model provided by the present invention, for any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and the crossover operator are adjusted according to the comparison result, and the method also includes:

[0037] Counting the number of individuals after the mutation operation that were not selected in the selection operation of the previous generation population of the contemporary population, and saving the mutation operation results of the individuals after the mutation operation that were not selected;

[0038] If the cumulative value of the second calculator plus the number of individuals after the unselected mutation operation reaches a third preset threshold, for any individual in all the saved mutation operation results, the fitness of the individual after the previous generation of mutation operation and the fitness before the mutation operation are compared, and the mutation operator and the crossover operator are adjusted according to the comparison result;

[0039] Accordingly, according to the fitness of the individual before the mutation operation and after the crossover operation, a selection operation is performed on the individual, and the individual after the selection operation is used as the individual of the next generation, including:

[0040] Find the mutation operation result with the same index as the individual from all saved mutation operation results;

[0041] According to the fitness of the individual before the mutation operation and after the hybridization operation and the fitness of the mutation operation result after the hybridization operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation.

[0042] The present invention also provides a power system load model parameter identification device, comprising:

[0043] The adjustment module is used to compare the fitness of any individual in the contemporary population before and after the mutation operation of the previous generation, and adjust the mutation operator and the crossover operator according to the comparison result;

[0044] The individual is a parameter to be identified of the power system load model, the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified;

[0045] A hybridization mutation module is used to perform a mutation operation on the individual according to the adjusted mutation operator, and determine whether the individual accepts the result of the mutation operation according to the adjusted hybridization operator;

[0046] A selection module is used to perform a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and use the individual after the selection operation as the individual of the next generation until a preset termination condition is met;

[0047] The optimization module is used to take the individual with the smallest fitness in the last generation of the population as the optimal parameter to be identified of the power system load model.

[0048] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any of the above-mentioned methods for identifying parameters of a load model of a power system are implemented.

[0049] The power system load model parameter identification method and device provided by the present invention adaptively adjust the mutation operator and crossover operator of any individual through the fitness of the individual before and after the mutation operation of the previous generation, use the adjusted mutation operator and crossover operator to perform mutation operation and crossover operation on the individual, select the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and use the individual after the selection operation as the next generation of the individual, which can not only improve the diversity of the population and promote the population evolution in the early stage of population evolution, thereby improving the global search performance and avoiding falling into the local optimum; but also can converge the mutation operator and crossover operator of each individual in the population to a range suitable for its own fitness in the late stage of population evolution, retain the high-quality solution, accelerate the convergence speed, and improve the accuracy of parameter identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is one of the flow charts of the method for identifying parameters of a load model of a power system provided by the present invention;

[0052] Figure 2 This is the second flow chart of the method for identifying parameters of a load model of a power system provided by the present invention;

[0053] Figure 3 It is a structural schematic diagram of the power system load model parameter identification device provided by the present invention;

[0054] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Combine the following Figure 1 The method for identifying parameters of a power system load model of the present invention comprises: step 101, for any individual in a contemporary population, comparing the fitness of the individual before and after the mutation operation of the previous generation, and adjusting the mutation operator and the crossover operator according to the comparison result; wherein the individual is a parameter to be identified of the power system load model, the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified;

[0057] Among them, the dynamic load is based on the transient equation of the third-order induction motor, and the static load is defined based on the ZIP load model. The power system load model can be a combination of the ZIP load model and the third-order induction motor load model. This embodiment is not limited to the type of power system load model. The ZIP load model contains constant impedance, constant current and constant power loads. After ignoring the constant current and constant power components, the parameters to be identified can be the rotor open circuit reactance, rotor transient reactance, rotor open circuit time constant and static load resistance, etc. According to the value range and dimension of the parameters to be identified in the power system load model, the first generation population can be randomly generated. Among them, the value range of the parameters to be identified can be adjusted according to actual needs. For example, the parameters to be identified are {X,X′,T d ' o ,R j ,X j}, its lower limit can be set to [2 0.1 0.1 0.1 0.1], and its upper limit can be set to [8 2 103 4]. The formula for generating the first generation population is as follows:

[0058]

[0059] in, is the jth parameter to be identified for the i-th individual in the first generation population, and are the upper and lower limits of the jth parameter to be identified in the first generation population, rand(0,1) is a random number between (0,1), N and N p are the dimensions of the parameters to be identified and the number of individuals in the first generation population, respectively.

[0060] In the prior art, when performing mutation and crossover operations on any individual in the contemporary population, the mutation operator and crossover operator are both constants. Performing mutation and crossover operations on the individual in this way affects the diversity of the population. However, this embodiment compares the fitness of the individual before and after the mutation operation of the previous generation. Determine whether to adjust the mutation operator or crossover operator based on the comparison result. By adaptively adjusting the mutation operator or crossover operator of each individual in the population, the diversity of the population can be increased and the evolution of the population can be promoted.

[0061] Step 102, performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator;

[0062] Specifically, the adjusted mutation operator can be used to perform mutation operation on the individual, and the calculation formula of the mutation operation is:

[0063] v i (G) = x r1 (G)+Fi (G)(x r2 (G)-x r3 (G)),i≠r1≠r2≠r3;

[0064] Among them, i is the index of the individual, G is the number of generations of evolution, and v i (G) represents the mutation operation on the i-th individual in the G-th generation population, x r1 (G), x r2 (G) and x r3 (G) are three individuals randomly selected from the G generation population, F i (G) is the mutation operator of the i-th individual in the G-th generation population. After completing the mutation operation, it is also necessary to i (G) Perform a test to determine whether it exceeds the upper or lower limit of the initial population. If it exceeds, perform a mutation operation on the individual again.

[0065] In the crossover operation on the individual, it can be determined whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator. If it is determined to accept the result of the mutation operation on the individual, the result after the mutation operation on the individual is used as the result of the crossover operation on the individual. If it is determined not to accept the result of the mutation operation on the individual, the result before the mutation operation on the individual is used as the result of the crossover operation on the individual.

[0066] Step 103, performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and using the individual after the selection operation as the individual of the next generation, until a preset termination condition is met;

[0067] Specifically, when the selection operation is performed on the individual, the fitness of the individual after the hybridization operation and the fitness before the mutation operation are compared. If the fitness of the individual after the hybridization operation is less than or equal to the fitness before the mutation operation, the individual after the hybridization operation is used as the individual of the next generation; if the fitness of the individual after the hybridization operation is greater than the fitness before the mutation operation, the individual before the mutation operation is used as the individual of the next generation.

[0068] After all individuals in the current population are selected, the next generation population can be obtained. By performing selection operations on each individual in each generation population, each individual in the next generation population can be iteratively updated. Before each iterative update, it is necessary to determine whether it meets the preset termination condition. If the preset termination condition is met, the update is stopped; if the preset termination condition is not met, each individual in the next generation population continues to be updated until the preset termination condition is met. Among them, the preset termination condition can be a condition that the number of iterations reaches a preset number, etc.

[0069] Step 104: taking the individual with the smallest fitness in the last generation population as the optimal parameter to be identified of the power system load model.

[0070] Specifically, if the preset termination condition is met, the fitness of all individuals in the last generation of the population is compared, and the individual with the smallest fitness is determined, which is used as the optimal parameter to be identified for the power system load model. By inputting the optimal parameter to be identified into the power system load model, the optimal power system load model can be obtained. Through this method, not only can an accurate power system load model be established, but also the optimal values ​​of multiple parameters to be identified can be determined at the same time, and it is suitable for parameter identification of various power system load models, with good robustness.

[0071] This embodiment adaptively adjusts the mutation operator and crossover operator of any individual by the fitness of the individual before and after the mutation operation of the previous generation, uses the adjusted mutation operator and crossover operator to perform mutation operation and crossover operation on the individual, selects the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and uses the individual after the selection operation as the individual of the next generation. This can not only improve the diversity of the population and promote the evolution of the population in the early stage of population evolution, thereby improving the global search performance and avoiding falling into the local optimum; it can also converge the mutation operator and crossover operator of each individual in the population to a range suitable for its own fitness in the later stage of population evolution, retain high-quality solutions, accelerate the convergence speed, and improve the accuracy of parameter identification.

[0072] On the basis of the above embodiments, in this embodiment, for any individual in the contemporary population, the fitness of the individual before and after the previous generation mutation operation is compared, and the mutation operator and the crossover operator are adjusted according to the comparison result, including: if the fitness of the individual after the previous generation mutation operation is greater than the fitness before the mutation operation, the mutation operator is adjusted to make the mutation operator randomly change within a first preset range; if the fitness of the individual after the previous generation mutation operation is less than or equal to the fitness before the mutation operation, the crossover operator is adjusted to make the crossover operator randomly change within a second preset range.

[0073] Specifically, when the mutation operator and crossover operator of an individual are larger, the diversity of the entire population and the globality of the algorithm are stronger. However, in the actual algorithm evolution process, each individual in each generation of the population can be judged as a different situation according to the fitness of its subordinate, and its mutation operator and crossover operator need to be adjusted according to its own situation. On the one hand, it can enhance the diversity of the population in the early stage of evolution, thereby improving the global searchability of the algorithm; on the other hand, it can make the mutation operator and crossover operator of each individual converge to the optimal range of variation at the end of evolution, thereby improving the local convergence ability of the algorithm.

[0074] On the basis of the above embodiment, in this embodiment, the mutation operator is adjusted by the following formula so that the mutation operator changes randomly within the first preset range:

[0075]

[0076] Among them, F i (G+1) is the mutation operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ1 is the first preset value.

[0077] Among them, τ1 can be adjusted according to actual needs, for example, τ1 = 0.1. By setting τ1, the contingency in the mutation operation can be eliminated. By adjusting the mutation operator of any individual, the mutation operator can be randomly changed within the first preset range, which is conducive to promoting population evolution. Among them, the first preset range can be [0.5, 1].

[0078] On the basis of the above embodiment, in this embodiment, the crossover operator is adjusted by the following formula so that the crossover operator changes randomly within the second preset range:

[0079]

[0080] in, is the crossover operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ2 is the second preset value.

[0081] Among them, τ2 can be adjusted according to actual needs, for example, τ2 = 0.1. By setting τ2, the randomness in the mutation operation can be eliminated. By adjusting the crossover operator of any individual, the crossover operator can be randomly changed within the second preset range, such as [0.5, 1]. By continuously adjusting the crossover operator of any individual, the optimal crossover operator range belonging to the individual can be gradually found.

[0082] On the basis of the above embodiments, the present embodiment describes the selection operation of the individual according to the fitness of the individual before the mutation operation and after the hybridization operation, including: calculating the predicted power according to the parameters to be identified of the individual before the mutation operation, and obtaining the fitness of the individual according to the Euclidean distance between the predicted power and the measured power obtained in advance; adding the fitness of all individuals in the contemporary population to obtain the total fitness of the contemporary population; adding the fitness of all individuals in the previous generation population of the contemporary population before the mutation operation to obtain the total fitness of the previous generation population; if the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than a first preset threshold, adding 1 to the value of the first counter; if the value of the first counter is greater than a second preset threshold, calculating the fitness of the individual before the mutation operation and after the hybridization operation based on the Mahalanobis distance, and performing a selection operation on the individual.

[0083] Specifically, the power system load model parameter identification method generally obtains the cost formula by calculating the Euclidean distance between the predicted value and the measured value. However, the Euclidean distance does not take into account the correlation between the parameters to be identified. For example, the parameters to be identified are {X, X′, T d ' o ,R j ,X j}, but the parameters to be identified may be correlated, for example, X and X′ are correlated, but the value ranges of X and X′ are not exactly the same. When the evolutionary generations are large and the cost formula is close to convergence, it is easy to fall into the local optimal search due to weak globality, resulting in inaccurate identification of the parameters to be identified.

[0084] In this embodiment, the Euclidean distance is used to calculate the fitness of any individual in the early stage of population evolution. If the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than the first preset threshold, the algorithm will perform a local optimal search, and it is difficult to determine the optimal parameter to be identified. When the value of the first counter is greater than the second preset threshold, the Euclidean distance can be switched to the Mahalanobis distance, and the Mahalanobis distance can be used to calculate the fitness of any individual in the population. This method can eliminate the differences in fitness caused by parameters of different sensitivities. Among them, the first preset threshold and the second preset threshold can be adjusted according to actual needs. For example, the first preset threshold can be e -5 , the second preset threshold may be 50*N, where N is the dimension of the parameter to be identified.

[0085] On the basis of the above embodiments, the present embodiment describes performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to the adjusted hybridization operator, including: if the adjusted hybridization operator meets the preset conditions during the hybridization operation on the individual, then accepting the result of the mutation operation on the individual, and taking all the parameters to be identified of the individual after the mutation operation as the result of the hybridization operation on the individual; correspondingly, performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to the adjusted hybridization operator, also includes: if the adjusted hybridization operator does not meet the preset conditions during the hybridization operation on the individual, then taking all the parameters to be identified of the individual before the mutation operation as the result of the hybridization operation on the individual.

[0086] Specifically, when the power system load model is constructed by nesting multiple motor transient formulas, in order to reflect the load characteristics over a period of time, the power system load model parameters can be identified based on noise-like data over a period of time, for example, 10S. If the sampling accuracy of PMU (Phasor Measurement Unit) or WAMS (Wide Area Measurement System) is 0.02s, 500-1=499 predicted active power and predicted reactive power need to be calculated. When this happens, if the basic differential evolution algorithm is used, the amount of calculation required to calculate the fitness of each individual in each generation of the population is relatively large, resulting in low parameter identification efficiency. The hybridization operation formula of the basic differential evolution algorithm is:

[0087]

[0088] in, is the jth parameter to be identified of the i-th individual after the crossover operation in the G-th generation population, is the jth parameter to be identified of the i-th individual after the mutation operation in the G-th generation population, The jth parameter to be identified of the i-th individual in the G-th generation population before the mutation operation, CR is the crossover operator, j=1,2,…,N is the index of the parameter to be identified of the individual, j rand is a random number generated in {1,2,…,N}.

[0089] In the existing differential evolution algorithm, if a hybridization operation is performed on any parameter to be identified of any individual, then the individual needs to undergo N hybridization operations. In order to reduce the amount of calculation and improve the efficiency of parameter identification, this embodiment treats any individual as a vector and performs mutation and hybridization operations on the individual as a whole. That is, each individual only needs to undergo one hybridization operation. The following formula is used to perform a hybridization operation on any individual:

[0090]

[0091] in, is the i-th individual in the G-th generation population after the crossover operation, is the i-th individual after the mutation operation in the G-th generation population, The i-th individual in the G-th generation population before the mutation operation, is the crossover operator of the i-th individual in the G-th generation population.

[0092] On the basis of the above embodiments, the selection operation is performed on the individual according to the fitness of the individual before the mutation operation and after the hybridization operation in this embodiment, including: if the contemporary population is not the first-generation population, the individual is searched from the individuals after the selection operation of the previous generation population of the contemporary population according to the index of the individual; if the individual is found, the fitness of the individual after the previous generation selection operation is used as the fitness of the individual before the contemporary mutation operation; if the parameters to be identified of the individual after the previous generation selection operation are the same as the parameters to be identified of the individual after the contemporary hybridization operation, the fitness of the individual after the previous generation selection operation is used as the fitness of the individual after the contemporary hybridization operation; the selection operation is performed on the individual according to the fitness of the individual before the mutation operation and after the hybridization operation.

[0093] Specifically, in order to further reduce the amount of calculation and speed up the parameter identification, in the selection operation, the process of calculating the fitness of the individual before the mutation operation and after the hybridization operation can be divided into two stages. Among them, the first stage is that the contemporary population is the first generation population. If the contemporary population is the first generation population, it is necessary to calculate the fitness of the individual before the mutation operation and after the hybridization operation. Among them, the formula for the selection operation of the individual is as follows:

[0094]

[0095] in, is the i-th individual in the second generation population, is the i-th individual in the first generation population after the crossover operation, is the ith individual in the first generation population before the mutation operation, and f(·) is the fitness, which is calculated based on the distance between the predicted power and the measured power of the power system load model.

[0096] The second stage is when the contemporary population is the second generation population or the population after the second generation. If the contemporary population is the second generation population or the population after the second generation, the formula for selecting the individual is as follows:

[0097]

[0098] Among them, G>1, is the i-th individual in the G+1-th generation population, is the i-th individual in the G1 generation population after the crossover operation, is the i-th individual in the G-th generation population before the mutation operation, is the fitness of the i-th individual in the G-th generation population before the mutation operation, is the fitness of the i-th individual in the G-th generation population after the crossover operation.

[0099] Since the individual after the previous generation selection operation is the current individual, the fitness of the individual in the current population before the hybridization operation can be found in the fitness of the individual after the previous generation selection operation according to the index of the individual. It can be found in the fitness of the individual after the previous generation selection operation. And the individual after the hybridization operation in the contemporary population It may be the individual before the mutation operation It may also be that the individual after mutation Determine whether the parameters to be identified of the individual after the previous generation selection operation are the same as the parameters to be identified of the individual after the current hybridization operation. If they are the same, it indicates that the individual after the hybridization operation is the individual before the mutation operation The fitness of the individual after the selection operation in the previous generation is taken as the fitness of the individual after the hybridization operation in the contemporary population; if they are different, it means that the individual after the hybridization operation is the individual after the mutation operation The fitness of the individual after the crossover operation is obtained by calculation.

[0100] For the contemporary population, if its size is N P , the existing technology needs to calculate N P The fitness of individuals before mutation operation, after crossover operation and after selection operation, the calculation amount is N P *3. This embodiment avoids repeated calculation of the fitness of individuals in each generation of the population by searching for known fitness during the evolution process. It only needs to calculate the fitness of individuals after the mutation operation during the hybridization process. It is a probability to determine whether to accept the result of each individual mutation operation. Therefore, the amount of calculation of the fitness of each generation of the population is less than N P Compared with the prior art, the amount of calculation for calculating the fitness of all individuals in the contemporary population in this embodiment is greatly reduced, thereby increasing the speed of parameter identification.

[0101] On the basis of the above embodiments, the present embodiment compares the fitness of any individual in the contemporary population before and after the previous generation mutation operation, and adjusts the mutation operator and the crossover operator according to the comparison result, and also includes: counting the number of individuals after the mutation operation that were not selected in the selection operation of the previous generation population of the contemporary population, and saving the mutation operation results of the individuals after the mutation operation that were not selected; if the value of the second calculator plus the number of individuals after the mutation operation that were not selected reaches a third preset threshold, then for any individual in all the saved mutation operation results, the fitness of the individual in the previous generation is calculated. The fitness after the mutation operation is compared with the fitness before the mutation operation, and the mutation operator and the crossover operator are adjusted according to the comparison result; accordingly, according to the fitness of the individual before the mutation operation and after the crossover operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation, including: searching for a mutation operation result with the same index as the individual from all saved mutation operation results; according to the fitness of the individual before the mutation operation and after the crossover operation and the fitness after the crossover operation of the found mutation operation result, the individual is selected and the individual after the selection operation is used as the individual of the next generation.

[0102] Specifically, there may be a large number of unselected individuals after mutation in the selection operation of the previous generation of population. Although the fitness of the unselected individuals after mutation is high and they are most likely distributed in some locations far away from the optimal solution area, the mutation operation of the differential evolution algorithm is to perform differential operations on individuals in three different positions. Therefore, if the unselected individuals after mutation are mutated, these unselected individuals after mutation may mutate into better individuals after further mutation, making them fall into the optimal solution area.

[0103] like Figure 2 As shown, in this embodiment, the individuals after the mutation operation that are not selected in the selection operation of the previous generation population are saved, and they can be saved in a matrix, which can be called an auxiliary population. A second counter is used to cumulatively count the number of individuals after the mutation operation that are not selected. If the value of the second counter reaches the third preset threshold or the matrix is ​​full rank, any individual after the mutation operation that is not selected is further subjected to mutation operation and hybridization operation. Among them, any individual after the mutation operation that is not selected is subjected to mutation operation and hybridization operation in parallel with any individual of the contemporary population.

[0104] When performing a selection operation on the individual in the contemporary population, the fitness of the individual before the mutation operation and after the hybridization operation can be compared with the fitness of the mutation operation result matching the index of the individual after further hybridization operation to determine the individual in the next generation population. The calculation formula for the selection operation is as follows:

[0105]

[0106] in, is the crossover operation of the i-th individual after the saved unselected mutation operation.

[0107] The power system load model parameter identification device provided by the present invention is described below. The power system load model parameter identification device described below and the power system load model parameter identification method described above can be referenced to each other.

[0108] like Figure 3 As shown, this embodiment provides a power system load model parameter identification device, which includes: an adjustment module 301, a hybridization and mutation module 302, a selection module 303 and an optimization module 304, wherein:

[0109] The adjustment module 301 is used to compare the fitness of any individual in the contemporary population before and after the previous generation mutation operation, and adjust the mutation operator and the crossover operator according to the comparison result; wherein the individual is a parameter to be identified of the power system load model, and the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified;

[0110] Among them, the dynamic load is based on the transient equation of the third-order induction motor, and the static load is defined based on the ZIP load model. The power system load model can be a combination of the ZIP load model and the third-order IM load model. This embodiment is not limited to the type of power system load model. The ZIP load model contains constant impedance, constant current and constant power loads. After ignoring the constant current and constant power components, the parameters to be identified can be the rotor open circuit reactance, rotor transient reactance, rotor open circuit time constant and static load resistance, etc. According to the value range and dimension of the parameters to be identified in the power system load model, the first generation population can be randomly generated. Among them, the value range of the parameters to be identified can be adjusted according to actual needs. Among them, the formula for generating the first generation population is as follows:

[0111]

[0112] in, is the jth parameter to be identified for the i-th individual in the first generation population, and are the upper and lower limits of the jth parameter to be identified in the first generation population, rand(0,1) is a random number between (0,1), N and N p are the dimensions of the parameters to be identified and the number of individuals in the first generation population, respectively.

[0113] In the prior art, when performing mutation and crossover operations on any individual in the contemporary population, the mutation operator and crossover operator are both constants. Performing mutation and crossover operations on the individual in this way affects the diversity of the population. However, this embodiment compares the fitness of the individual before and after the mutation operation of the previous generation. Determine whether to adjust the mutation operator or crossover operator based on the comparison result. By adaptively adjusting the mutation operator or crossover operator of each individual in the population, the diversity of the population can be increased and the evolution of the population can be promoted.

[0114] The hybridization mutation module 302 is used to perform a mutation operation on the individual according to the adjusted mutation operator, and determine whether to accept the result of the mutation operation on the individual according to the adjusted hybridization operator;

[0115] Specifically, the adjusted mutation operator can be used to perform mutation operation on the individual, and the calculation formula of the mutation operation is:

[0116] v i (G) = x r1 (G)+F i (G)(x r2 (G)-x r3 (G)),i≠r1≠r2≠r3;

[0117] Among them, i is the index of the individual, G is the number of generations of evolution, and v i (G) represents the mutation operation on the i-th individual in the G-th generation population, x r1 (G), x r2 (G) and x r3 (G) are three individuals randomly selected from the G generation population, F i (G) is the mutation operator of the i-th individual in the G-th generation population. After completing the mutation operation, it is also necessary to i (G) Perform a test to determine whether it exceeds the upper or lower limit of the initial population. If it exceeds, perform a mutation operation on the individual again.

[0118] In the crossover operation on the individual, it can be determined whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator. If it is determined to accept the result of the mutation operation on the individual, the result after the mutation operation on the individual is used as the result of the crossover operation on the individual. If it is determined not to accept the result of the mutation operation on the individual, the result before the mutation operation on the individual is used as the result of the crossover operation on the individual.

[0119] The selection module 303 is used to perform a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and use the individual after the selection operation as the individual of the next generation until a preset termination condition is met;

[0120] Specifically, when the selection operation is performed on the individual, the fitness of the individual after the hybridization operation and the fitness before the mutation operation are compared. If the fitness of the individual after the hybridization operation is less than or equal to the fitness before the mutation operation, the individual after the hybridization operation is used as the individual of the next generation; if the fitness of the individual after the hybridization operation is greater than the fitness before the mutation operation, the individual before the mutation operation is used as the individual of the next generation.

[0121] After all individuals in the current population are selected, the next generation population can be obtained. By performing selection operations on each individual in each generation population, each individual in the next generation population can be iteratively updated. Before each iterative update, it is necessary to determine whether it meets the preset termination condition. If the preset termination condition is met, the update is stopped; if the preset termination condition is not met, each individual in the next generation population continues to be updated until the preset termination condition is met. Among them, the preset termination condition can be a condition that the number of iterations reaches a preset number, etc.

[0122] The optimization module 304 is used to use the individual with the smallest fitness in the last generation population as the optimal parameter to be identified of the power system load model.

[0123] Specifically, if the preset termination condition is met, the fitness of all individuals in the last generation of the population is compared, and the individual with the smallest fitness is determined, which is used as the optimal parameter to be identified for the power system load model. By inputting the optimal parameter to be identified into the power system load model, the optimal power system load model can be obtained. Through this method, not only can an accurate power system load model be established, but also the optimal values ​​of multiple parameters to be identified can be determined at the same time, and it is suitable for parameter identification of various power system load models, with good robustness.

[0124] This embodiment adaptively adjusts the mutation operator and crossover operator of any individual by the fitness of the individual before and after the mutation operation of the previous generation, uses the adjusted mutation operator and crossover operator to perform mutation operation and crossover operation on the individual, selects the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and uses the individual after the selection operation as the individual of the next generation. This can not only improve the diversity of the population and promote the evolution of the population in the early stage of population evolution, thereby improving the global search performance and avoiding falling into the local optimum; it can also converge the mutation operator and crossover operator of each individual in the population to a range suitable for its own fitness in the later stage of population evolution, retain high-quality solutions, accelerate the convergence speed, and improve the accuracy of parameter identification.

[0125] On the basis of the above embodiments, the adjustment module in this embodiment is specifically used for: if the fitness of the individual after the previous generation of mutation operation is greater than the fitness before the mutation operation, then adjusting the mutation operator so that the mutation operator changes randomly within a first preset range; if the fitness of the individual after the previous generation of mutation operation is less than or equal to the fitness before the mutation operation, then adjusting the crossover operator so that the crossover operator changes randomly within a second preset range.

[0126] On the basis of the above embodiment, in this embodiment, the mutation operator is adjusted by the following formula so that the mutation operator changes randomly within the first preset range:

[0127]

[0128] Among them, F i (G+1) is the mutation operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ1 is the first preset value.

[0129] On the basis of the above embodiment, in this embodiment, the crossover operator is adjusted by the following formula so that the crossover operator changes randomly within the second preset range:

[0130]

[0131] in, is the crossover operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ2 is the second preset value.

[0132] On the basis of the above embodiments, the selection module in this embodiment is specifically used for: calculating the predicted power according to the parameters to be identified of the individual before the mutation operation, and obtaining the fitness of the individual according to the Euclidean distance between the predicted power and the measured power obtained in advance; adding the fitness of all individuals in the contemporary population to obtain the total fitness of the contemporary population; adding the fitness of all individuals in the previous generation population of the contemporary population before the mutation operation to obtain the total fitness of the previous generation population; if the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than a first preset threshold, adding 1 to the value of the first counter; if the value of the first counter is greater than a second preset threshold, calculating the fitness of the individual before the mutation operation and after the hybridization operation based on the Mahalanobis distance, and performing a selection operation on the individual.

[0133] On the basis of the above embodiments, the hybridization mutation module in this embodiment is specifically used for: if during the hybridization operation on the individual, the adjusted hybridization operator meets the preset conditions, then accepting the result of the mutation operation on the individual, and taking all the parameters to be identified of the individual after the mutation operation as the result of the hybridization operation on the individual; correspondingly, the hybridization mutation module is also used for, if during the hybridization operation on the individual, the adjusted hybridization operator does not meet the preset conditions, then taking all the parameters to be identified of the individual before the mutation operation as the result of the hybridization operation on the individual.

[0134] On the basis of the above embodiments, the selection module in this embodiment is specifically used for: if the contemporary population is not the first generation population, then searching for the individual from the individuals after the selection operation of the previous generation population of the contemporary population according to the index of the individual; if the individual is found, then using the fitness of the individual after the previous generation selection operation as the fitness of the individual before the contemporary mutation operation; if the parameters to be identified of the individual after the previous generation selection operation are the same as the parameters to be identified of the individual after the contemporary hybridization operation, then using the fitness of the individual after the previous generation selection operation as the fitness of the individual after the contemporary hybridization operation; and performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the hybridization operation.

[0135] On the basis of the above embodiments, the adjustment module in this embodiment is specifically used for: counting the number of individuals after the mutation operation that are not selected in the selection operation of the previous generation population of the contemporary population, and saving the mutation operation results of the individuals after the mutation operation that are not selected; if the value of the second calculator plus the number of individuals after the mutation operation that are not selected reaches a third preset threshold, then for any individual in all the saved mutation operation results, the fitness of the individual after the previous generation mutation operation and the fitness before the mutation operation are compared, and the mutation operator and the crossover operator are adjusted according to the comparison result; accordingly, according to the fitness of the individual before the mutation operation and after the crossover operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation, including: searching for the mutation operation result with the same index as the individual from all the saved mutation operation results; according to the fitness of the individual before the mutation operation and after the crossover operation and the fitness of the found mutation operation result after the crossover operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation.

[0136] Figure 4 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor (processor) 401, a communication interface (Communications Interface) 402, a memory (memory) 403 and a communication bus 404, wherein the processor 401, the communication interface 402, and the memory 403 communicate with each other through the communication bus 404. The processor 401 can call the logic instructions in the memory 403 to execute the power system load model parameter identification method, which includes: for any individual in the contemporary population, comparing the fitness of the individual before and after the previous generation mutation operation, and adjusting the mutation operator and the crossover operator according to the comparison result; wherein the individual is the parameter to be identified of the power system load model, and the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified; performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether the result of the mutation operation is accepted for the individual according to the adjusted crossover operator; performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and using the individual after the selection operation as the next generation of the individual until the preset termination condition is met; using the individual with the smallest fitness in the last generation of the population as the optimal parameter to be identified of the power system load model.

[0137] In addition, the logic instructions in the above-mentioned memory 403 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0138] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the power system load model parameter identification method provided by the above methods, the method comprising: for any individual in the contemporary population, comparing the fitness of the individual before and after the mutation operation of the previous generation, and adjusting the mutation operator and the crossover operator according to the comparison result; wherein the individual is a parameter to be identified of the power system load model, and the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified; performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether the result of the mutation operation is accepted for the individual according to the adjusted crossover operator; performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and using the individual after the selection operation as the individual of the next generation until the preset termination condition is met; using the individual with the smallest fitness in the last generation population as the optimal parameter to be identified of the power system load model.

[0139] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented when executed by a processor to execute the above-mentioned power system load model parameter identification method, the method comprising: for any individual in the contemporary population, comparing the fitness of the individual before and after the mutation operation of the previous generation, and adjusting the mutation operator and the crossover operator according to the comparison result; wherein the individual is the parameter to be identified of the power system load model, and the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified; performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether the result of the mutation operation is accepted for the individual according to the adjusted crossover operator; performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and using the individual after the selection operation as the individual of the next generation until the preset termination condition is met; using the individual with the smallest fitness in the last generation population as the optimal parameter to be identified of the power system load model.

[0140] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0141] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0142] Finally, 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying parameters of a power system load model, characterized in that: include: For any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and crossover operator are adjusted according to the comparison results; The individual is a parameter to be identified of the power system load model, the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified; Perform a mutation operation on the individual according to the adjusted mutation operator, and determine whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator; According to the fitness of the individual before the mutation operation and after the crossover operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation until the preset termination condition is met; Taking the to-be-identified parameter corresponding to the individual with the smallest fitness in the last generation of the population as the optimal to-be-identified parameter of the power system load model; The mutation operator is adjusted by the following formula so that the mutation operator changes randomly within the first preset range: in, is the mutation operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ1 is the first preset value; The crossover operator is adjusted by the following formula so that the crossover operator changes randomly within the second preset range: in, is the crossover operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ2 is the second preset value; The step of performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation comprises: Calculating the predicted power according to the to-be-identified parameter of the individual before the mutation operation, and obtaining the fitness of the individual according to the Euclidean distance between the predicted power and the measured power obtained in advance; Adding the fitness of all individuals in the contemporary population to obtain the total fitness of the contemporary population; Add the fitness of all individuals in the previous generation population of the current population before the mutation operation to obtain the total fitness of the previous generation population; If the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than a first preset threshold, the value of the first counter is cumulatively increased by 1; If the value of the first counter is greater than a second preset threshold, the fitness of the individual before the mutation operation and after the crossover operation is calculated based on the Mahalanobis distance, and a selection operation is performed on the individual.

2. The method for identifying parameters of a power system load model according to claim 1, characterized in that: For any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and the crossover operator are adjusted according to the comparison result, including: If the fitness of the individual after the previous generation mutation operation is greater than the fitness before the mutation operation, the mutation operator is adjusted so that the mutation operator changes randomly within a first preset range; If the fitness of the individual after the previous generation mutation operation is less than or equal to the fitness before the mutation operation, the crossover operator is adjusted so that the crossover operator changes randomly within a second preset range.

3. The method for identifying parameters of a power system load model according to any one of claims 1 to 2, characterized in that: The step of performing a mutation operation on the individual according to the adjusted mutation operator and determining whether the individual accepts the result of the mutation operation according to the adjusted crossover operator includes: If, during the crossover operation on the individual, the adjusted crossover operator satisfies the preset conditions, the result of the mutation operation on the individual is accepted, and all the parameters to be identified of the individual after the mutation operation are taken as the result of the crossover operation on the individual; Accordingly, performing a mutation operation on the individual according to the adjusted mutation operator, and determining whether to accept the result of the mutation operation on the individual according to the adjusted crossover operator, further includes: If the adjusted hybridization operator does not meet the preset conditions during the hybridization operation on the individual, all the parameters to be identified of the individual before the mutation operation are taken as the result of the hybridization operation on the individual.

4. The method for identifying parameters of a power system load model according to any one of claims 1 to 2, characterized in that: The step of performing a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation comprises: If the contemporary population is not the first generation population, the individual is searched for from the individuals of the previous generation population of the contemporary population after the selection operation according to the index of the individual; If the individual is found, the fitness of the individual after the selection operation of the previous generation is used as the fitness of the individual before the mutation operation of the current generation; If the parameters to be identified of the individual after the previous generation selection operation are the same as the parameters to be identified of the individual after the current hybridization operation, the fitness of the individual after the previous generation selection operation is taken as the fitness of the individual after the current hybridization operation; A selection operation is performed on the individual according to its fitness before the mutation operation and after the hybridization operation.

5. The method for identifying parameters of a power system load model according to any one of claims 1 to 2, characterized in that: For any individual in the contemporary population, the fitness of the individual before and after the mutation operation of the previous generation is compared, and the mutation operator and the crossover operator are adjusted according to the comparison result, and further includes: Counting the number of individuals after the mutation operation that were not selected in the selection operation of the previous generation population of the contemporary population, and saving the mutation operation results of the individuals after the mutation operation that were not selected; If the cumulative value of the second calculator plus the number of individuals after the unselected mutation operation reaches a third preset threshold, for any individual in all the saved mutation operation results, the fitness of the individual after the previous generation of mutation operation and the fitness before the mutation operation are compared, and the mutation operator and the crossover operator are adjusted according to the comparison result; Accordingly, according to the fitness of the individual before the mutation operation and after the crossover operation, a selection operation is performed on the individual, and the individual after the selection operation is used as the individual of the next generation, including: Find the mutation operation result with the same index as the individual from all saved mutation operation results; According to the fitness of the individual before the mutation operation and after the hybridization operation and the fitness of the mutation operation result after the hybridization operation, the individual is selected and the individual after the selection operation is used as the individual of the next generation.

6. A power system load model parameter identification device, characterized in that: include: The adjustment module is used to compare the fitness of any individual in the contemporary population before and after the mutation operation of the previous generation, and adjust the mutation operator and the crossover operator according to the comparison result; The individual is a parameter to be identified of the power system load model, the fitness of the individual is the distance between the predicted power and the measured power of the power system load model, and the predicted power is calculated and obtained according to the parameter to be identified; A hybridization mutation module is used to perform a mutation operation on the individual according to the adjusted mutation operator, and determine whether the individual accepts the result of the mutation operation according to the adjusted hybridization operator; A selection module is used to perform a selection operation on the individual according to the fitness of the individual before the mutation operation and after the crossover operation, and use the individual after the selection operation as the individual of the next generation until a preset termination condition is met; An optimization module, used for taking the to-be-identified parameter corresponding to the individual with the smallest fitness in the last generation of the population as the optimal to-be-identified parameter of the power system load model; The regulation module is specifically used for: The mutation operator is adjusted by the following formula so that the mutation operator changes randomly within the first preset range: in, is the mutation operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ1 is the first preset value; The crossover operator is adjusted by the following formula so that the crossover operator changes randomly within the second preset range: in, is the crossover operator of the i-th individual in the G+1-th generation population, is the fitness of the i-th individual in the G-th generation population after the mutation operation, The fitness of the i-th individual in the G-th generation population before the mutation operation, rand(0,1) is a random number between [0,1], and τ2 is the second preset value; The selection module is specifically used for: Calculating the predicted power according to the to-be-identified parameter of the individual before the mutation operation, and obtaining the fitness of the individual according to the Euclidean distance between the predicted power and the measured power obtained in advance; Adding the fitness of all individuals in the contemporary population to obtain the total fitness of the contemporary population; Add the fitness of all individuals in the previous generation population of the current population before the mutation operation to obtain the total fitness of the previous generation population; If the difference between the total fitness of the contemporary population and the total fitness of the previous generation population is less than a first preset threshold, the value of the first counter is cumulatively increased by 1; If the value of the first counter is greater than a second preset threshold, the fitness of the individual before the mutation operation and after the crossover operation is calculated based on the Mahalanobis distance, and a selection operation is performed on the individual.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the power system load model parameter identification method as described in any one of claims 1 to 5 are implemented.

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