A method, system, device and medium for detecting reactor winding parameters

Abstract

The present invention discloses a method, system, device and medium for detecting the winding parameters of a reactor, relating to the technical field of reactors. The detection method obtains the operation data and structural data of the reactor, constructs a model with the operation data and structural data to generate an initial ideal circuit model corresponding to the reactor; performs structural constraints on the initial ideal circuit model based on the structural data to generate a target ideal circuit model; performs parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor; sets structural constraint conditions according to the reactor structural data to improve the convergence efficiency and optimization accuracy of parameter optimization. By gradually and deeply optimizing the model winding parameters of the target ideal circuit model, the present invention can accurately obtain the optimal winding parameters of the reactor, which is beneficial to timely obtaining the working state of the reactor winding, mastering the change of winding parameters, and improving the control ability of the power system over important power equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of reactors, and in particular to a method, system, device and medium for detecting reactor winding parameters. Background Art

[0002] As a key device for realizing reactive power compensation and stabilizing power quality in the power system, the reactor plays a crucial role in the power system. The winding state of the reactor is the key to determining its safe and stable operation. Therefore, how to quickly and sensitively obtain the reactor winding parameters is of great significance for the monitoring of the device operation state and the management and maintenance of the device.

[0003] Currently, the traditional methods for optimizing reactor winding parameters mainly include the least squares method, ant colony algorithm, particle swarm algorithm, etc. Among them, the least squares method has defects such as large computational amount and poor real-time performance, and it is unable to obtain the winding parameters in time. The ant colony algorithm and particle swarm algorithm both have problems such as large computational amount, long running time, and easy to fall into local optimum. Therefore, the existing methods for optimizing reactor winding parameters cannot quickly, sensitively and accurately obtain the reactor winding parameters, resulting in poor parameter detection effect. Summary of the Invention

[0004] The present invention provides a method, system, device and medium for detecting reactor winding parameters, which solves the technical problem that the existing methods for optimizing reactor winding parameters cannot quickly, sensitively and accurately obtain the reactor winding parameters, resulting in poor parameter detection effect.

[0005] A method for detecting reactor winding parameters provided by the present invention includes:

[0006] Obtain the operation data and structure data of the reactor, and perform model construction on the operation data and the structure data to generate an initial ideal circuit model corresponding to the reactor;

[0007] Perform structure constraint on the initial ideal circuit model based on the structure data to generate a target ideal circuit model;

[0008] Perform parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor.

[0009] Optionally, the step of performing model construction on the operation data and the structure data to generate an initial ideal circuit model corresponding to the reactor includes:

[0010] Use the rated parameters of the reactor in the structure data for model construction to generate an initial circuit model;

[0011] Select model parameters within a preset range, and update the initial circuit model with the model parameters to generate a target circuit model;

[0012] Input the port voltage in the operation data into the target circuit model to calculate the total current and generate a total current calculation value;

[0013] Construct a fitness function using the difference between the total current in the operation data and the total current calculation value;

[0014] Update the target circuit model with the fitness function to generate the initial ideal circuit model corresponding to the reactor.

[0015] Optionally, the step of performing structural constraints on the initial ideal circuit model based on the structure data to generate a target ideal circuit model includes:

[0016] Construct structural constraint conditions using the coil data in the structure data;

[0017] The structural constraint conditions are:

[0018]

[0019] where M ij is the mutual inductance between the i-th layer coil and the j-th layer coil; M nk is the mutual inductance between the n-th layer coil and the k-th layer coil; L i is the self-inductance of the i-th layer coil; L j is the self-inductance of the j-th layer coil;

[0020] Update the initial ideal circuit model with the structural constraint conditions to generate a target ideal circuit model.

[0021] Optionally, the step of performing parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor includes:

[0022] Perform population initialization using the model winding parameters of the target ideal circuit model to generate an initial population;

[0023] Calculate the fitness values of each initial individual in the initial population respectively to generate multiple fitness values;

[0024] Select the initial individuals whose fitness values meet the preset threshold using a selection operator to construct an intermediate population;

[0025] Perform genetic operations on each intermediate individual in the intermediate population using a preset crossover operator, a preset mutation operator, and a preset probability formula to construct a target population;

[0026] Select the target individual corresponding to the maximum fitness value in the target population as the elite individual and count the number of iterations;

[0027] Determine whether the number of iterations is equal to the preset maximum number of iterations;

[0028] If so, use the model winding parameters corresponding to the elite individual as the optimal winding parameters corresponding to the reactor;

[0029] If not, use the target population as the new initial population, increment the iteration count by 1, and jump to execute the step of calculating the fitness values of each initial individual in the initial population to generate multiple fitness values.

[0030] Optionally, the step of performing genetic operations on each intermediate individual in the intermediate population using a preset crossover operator, a preset mutation operator, and a preset probability formula to construct a target population includes:

[0031] Perform overlapping recombination on the intermediate individuals in the intermediate population according to the crossover operator in the preset crossover operator and the crossover probability formula in the preset probability formula to generate multiple recombinant individuals;

[0032] The preset crossover operator is:

[0033]

[0034] where A i ′ is the first offspring gene after crossover; A j ′ is the second offspring gene after crossover; A i is the first parent gene before crossover; A j is the second parent gene before crossover; n is a random number in [-0.25, 1.25];

[0035] The crossover probability formula is:

[0036]

[0037] where P c is the crossover probability; t is the current iteration count; P c1 is the first variable parameter; P c2 is the second variable parameter, and P c2 = 0.6; max is the preset maximum number of iterations; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f′ is the fitness value of the parent gene with the largest fitness value in the crossover operation;

[0038] Perform mutation operation on the recombinant individual according to the preset mutation operator and the mutation probability formula in the preset probability formula to generate a mutant individual corresponding to the recombinant individual;

[0039] The preset mutation operator is:

[0040]

[0041] where x i is the gene before mutation; x i ′ is the gene after mutation, that is, the mutant individual; x min is the initial minimum value of the gene; x max is the initial maximum value of the gene; x newmin is the minimum value of the gene during the evolution process; x newmax are respectively the maximum value of the gene during the evolution process; G is the total number of generations of evolution; gen is the current generation of evolution; r is the first parameter, with a value of 0.2; b is the second parameter, with a value of 0.3; r1 is a random number in [0, 1];

[0042] The preset mutation probability formula is:

[0043]

[0044] where P m is the mutation probability; P m1 is the third variable parameter; P m2 is the fourth variable parameter, and P m2 = 0.02; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f is the fitness value of the recombinant individual; t is the current iteration number;

[0045] Construct a target population by using all the mutant individuals.

[0046] The present invention also provides a reactor winding parameter detection system, including:

[0047] An initial ideal circuit model generation module, configured to obtain the operation data and structure data of the reactor, perform model construction on the operation data and the structure data, and generate an initial ideal circuit model corresponding to the reactor;

[0048] A target ideal circuit model generation module, configured to perform structure constraint on the initial ideal circuit model based on the structure data to generate a target ideal circuit model;

[0049] An optimal winding parameter determination module, configured to perform parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor.

[0050] Optionally, the initial ideal circuit model generation module includes:

[0051] An initial circuit model generation module, configured to construct a model by using the rated parameters of the reactor in the structure data, and generate an initial circuit model;

[0052] A target circuit model generation module, configured to select model parameters within a preset range, and update the initial circuit model by using the model parameters to generate a target circuit model;

[0053] A total current calculated value generation module, configured to input the port voltage in the operation data into the target circuit model to calculate the total current, and generate a total current calculated value;

[0054] A fitness function construction module, configured to construct a fitness function by using the difference between the total current in the operation data and the total current calculated value;

[0055] An initial ideal circuit model generation sub-module, configured to update the target circuit model by using the fitness function to generate the initial ideal circuit model corresponding to the reactor.

[0056] Optionally, the target ideal circuit model generation module includes:

[0057] A structure constraint condition construction module, configured to construct structure constraint conditions by using the coil data in the structure data;

[0058] The structure constraint condition is:

[0059]

[0060] where M ij is the mutual inductance between the i-th layer coil and the j-th layer coil; M nk is the mutual inductance between the n-th layer coil and the k-th layer coil; L i is the self-inductance of the i-th layer coil; L j is the self-inductance of the j-th layer coil;

[0061] A target ideal circuit model generation sub-module, configured to update the initial ideal circuit model by using the structure constraint conditions to generate a target ideal circuit model.

[0062] The present invention also provides an electronic device, including a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor is caused to execute the steps of implementing the reactor winding parameter detection method as described in any one of the above.

[0063] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the method for detecting the winding parameters of a reactor as described in any one of the above is implemented.

[0064] As can be seen from the above technical solutions, the present invention has the following advantages:

[0065] The present invention improves the convergence efficiency and optimization accuracy of parameter optimization by setting structural constraint conditions based on the reactor structure data. By gradually and deeply optimizing the winding parameters of the model, the optimal winding parameters of the reactor can be accurately obtained, which is beneficial to timely obtaining the working state of the reactor winding, mastering the change of winding parameters, and improving the control ability of the power system for important power equipment. The present invention solves the technical problem that the existing method for optimizing the winding parameters of a reactor cannot quickly, sensitively, and accurately obtain the winding parameters of the reactor, resulting in poor parameter detection effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0067] Figure 1 It is a flowchart of the steps of a method for detecting the winding parameters of a reactor provided in Embodiment 1 of the present invention;

[0068] Figure 2 It is a flowchart of the steps of a method for detecting the winding parameters of a reactor provided in Embodiment 2 of the present invention;

[0069] Figure 3 It is a flowchart of a method for detecting the winding parameters of a reactor provided in Embodiment 2 of the present invention;

[0070] Figure 4 It is a flowchart of the adaptive genetic algorithm provided in Embodiment 2 of the present invention;

[0071] Figure 5 It is a block diagram of the structure of a system for detecting the winding parameters of a reactor provided in Embodiment 3 of the present invention;

[0072] Figure 6 It is a block diagram of the structure of a reactor network parameter system based on structural constraints and an adaptive genetic algorithm provided in Embodiment 3 of the present invention;

[0073] Figure 7 It is a block diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] An embodiment of the present invention provides a method, system, device and medium for detecting reactor winding parameters, which are used to solve the technical problem that the existing method for optimizing reactor winding parameters cannot quickly, sensitively and accurately obtain the reactor winding parameters, resulting in poor parameter detection effect.

[0075] In order to make the object, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0076] Embodiment 1

[0077] Please refer to Figure 1 , Figure 1 which is a flowchart of the steps of a method for detecting reactor winding parameters provided in Embodiment 1 of the present invention.

[0078] A method for detecting reactor winding parameters provided in Embodiment 1 of the present invention includes:

[0079] Step 101: Obtain the operation data and structure data of the reactor, construct a model with the operation data and structure data, and generate an initial ideal circuit model corresponding to the reactor.

[0080] In the embodiment of the present invention, the rated parameters of the reactor in the structure data are used to construct a model to generate an initial circuit model. Model parameters are selected within a preset range, and the initial circuit model is updated with the model parameters to generate a target circuit model. The port voltage in the operation data is input into the target circuit model to calculate the total current, and a total current calculation value is generated. The difference between the total current in the operation data and the total current calculation value is used to construct a fitness function. The target circuit model is updated with the fitness function to obtain the initial ideal circuit model corresponding to the reactor.

[0081] Step 102: Perform structure constraint on the initial ideal circuit model based on the structure data to generate a target ideal circuit model.

[0082] In the embodiment of the present invention, the coil data in the structure data is first used to construct a structure constraint condition. Then the initial ideal circuit model is updated with the structure constraint condition to obtain the target ideal circuit model.

[0083] Step 103: Perform parameter optimization solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor.

[0084] In the embodiment of the present invention, the model winding parameters of the target ideal circuit model are used for population initialization to generate an initial population. The fitness values of each initial individual in the initial population are calculated respectively to generate a plurality of fitness values. The initial individuals whose fitness values meet the preset threshold are selected by using a selection operator to construct an intermediate population. Genetic operations are respectively performed on each intermediate individual in the intermediate population by using a preset crossover operator, a preset mutation operator and a preset probability formula to construct a target population. The target individual corresponding to the maximum fitness value in the target population is selected as the elite individual and the iteration times are counted. It is judged whether the iteration times are equal to the preset maximum iteration times. If so, the model winding parameters corresponding to the elite individual are used as the optimal winding parameters corresponding to the reactor. If not, the target population is used as the new initial population, the updated iteration times are incremented by 1, and the step of calculating the fitness values of each initial individual in the initial population respectively to generate a plurality of fitness values is jumped to and executed.

[0085] In the embodiment of the present invention, by acquiring the operation data and structure data of the reactor, the operation data and structure data are used for model construction to generate an initial ideal circuit model corresponding to the reactor. Structural constraints are imposed on the initial ideal circuit model based on the structure data to generate a target ideal circuit model. Parameter optimization solution is performed based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor. Structural constraint conditions are set according to the reactor structure data to improve the convergence efficiency and optimization accuracy of the algorithm. At the same time, the parameter optimization algorithm for the model winding parameters adopts an adaptive genetic algorithm. Compared with the prior art, the adaptive genetic algorithm can significantly improve the stability and global search ability of the algorithm, increase the convergence speed, and ensure the diversity characteristics of the population. It effectively avoids the problem of falling into the local optimal solution, can quickly, sensitively and accurately obtain the winding parameters of each layer of the reactor, and has good parameter detection effect. It is beneficial to timely obtain the working state of the reactor winding, master the change of winding parameters, and improve the control ability of the power system for important power equipment. It solves the technical problem that the existing method for optimizing the winding parameters of the reactor cannot quickly, sensitively and accurately obtain the winding parameters of the reactor, resulting in poor parameter detection effect.

[0086] Embodiment 2

[0087] Please refer to Figure 2 , Figure 2 which is a step flowchart of a method for detecting the winding parameters of a reactor provided in Embodiment 2 of the present invention.

[0088] Another method for detecting the winding parameters of a reactor provided in the second embodiment of the present invention includes:

[0089] Step 201, acquire the operation data and structure data of the reactor, and use the rated parameters of the reactor in the structure data for model construction to generate an initial circuit model.

[0090] In the embodiment of the present invention, when the reactor is operating stably, operating data and structural data are collected. The operating data includes the port voltage and the total current and are saved in the system working area. The structural data includes the rated parameters of the reactor and the structural parameters of the reactor. The reactor is a dry-type air-core reactor.

[0091] In this example, a certain 35 kV reactor is taken as the experimental object, and its single-phase capacity S n = 2000 kV·A, rated voltage U = 35 kV, with a total of 20 layers of coils, equally divided into 5 packages. The port voltage and the total current data of the dry-type air-core reactor under stable operating conditions are collected by using voltage and current transformers.

[0092] An ideal circuit model is established based on the rated parameters of the reactor. Specifically, an initial ideal circuit model is established based on the rated parameters of the dry-type air-core reactor. In this example, the circuit model has a total of 20 layers of coils. Among them, the circuit model expression and the total model current expression corresponding to the initial ideal circuit model are as follows:

[0093]

[0094] Among them, R i , L i respectively represent the resistance and self-inductance parameters of the i-th layer of coil; M i,k represents the mutual inductance parameter between the i-th and k-th layers of coils; represents the port voltage of the i-th layer of coil; represents the branch current of the i-th layer of coil; i = 1, 2,..., 20.

[0095] The formula for calculating the total model current is:

[0096]

[0097] Among them, represents the calculated value of the total current, represents the branch current of the i-th layer of coil.

[0098] Step 202: Select model parameters within a preset interval, and update the initial circuit model with the model parameters to generate a target circuit model.

[0099] In the embodiment of the present invention, the preset interval refers to random selection. The model parameters include resistance R, self-inductance L, and mutual inductance M. Randomly add values of R, L, and M to the impedance matrix as the initial parameters of the following adaptive genetic algorithm to obtain the model parameters. Update the initial circuit model with the model parameters to obtain the target circuit model.

[0100] Step 203: Input the port voltage in the operation data into the target circuit model to calculate the total current and generate a total current calculation value.

[0101] In the embodiment of the present invention, taking the port voltage as the same input condition for the actual model and the ideal model, input the port voltage in the operation data into the target circuit model, and calculate the total current of the model reactor, that is, the total current calculation value, in combination with the circuit model expression and the model total current expression.

[0102] Step 204: Construct a fitness function by using the difference between the total current in the operation data and the total current calculation value.

[0103] In the embodiment of the present invention, as Figure 3 shown, evaluate the output difference between the actual dry-type air-core reactor and the target ideal circuit model under the same input conditions by constructing a fitness function. First, establish the expression of the fitness function, then set the structural constraints, and then correct it through the genetic algorithm to minimize the fitness function, thereby expressing the similarity between the actual dry-type air-core reactor and the target ideal circuit model. The present invention uses the total current in the operation data and the total current calculation value to construct a fitness function for parameter optimization and solution result evaluation. The fitness function is as follows:

[0104]

[0105] where Y is the fitness value; is the total current; is the total current calculation value.

[0106] Step 205: Update the target circuit model by using the fitness function to generate an initial ideal circuit model corresponding to the reactor.

[0107] In the embodiment of the present invention, an initial ideal circuit model corresponding to the reactor is obtained by updating the target circuit model with the fitness function. So as to calculate the fitness value when subsequently optimizing the network parameters of the target ideal circuit model by using the adaptive genetic algorithm.

[0108] Step 206: Perform structural constraints on the initial ideal circuit model based on the structural data to generate a target ideal circuit model.

[0109] Further, step 206 may include the following sub-steps S11 - S12:

[0110] S11: Construct structural constraint conditions by using the coil data in the structural data.

[0111] S12: Update the initial ideal circuit model by using the structural constraint conditions to generate a target ideal circuit model.

[0112] In the embodiment of the present invention, in order to accurately obtain the parameters of each layer of the reactor coil and reduce the optimization error, this example constrains the resistance and inductance size relationships of each layer of the coil respectively according to the coil self-inductance calculation formula and the resistance calculation formula in combination with the reactor structure parameters. At the same time, some physically reasonable constraint relationships between mutual inductance and self-inductance are fully considered. The resistance and self-inductance calculation expressions are as follows:

[0113]

[0114] Among them, ρ is the resistivity of copper; l is the coil length; S is the cross-sectional area of the coil; μ is the magnetic permeability of the medium in the coil; N is the number of turns of the coil.

[0115] Based on the coil data in the structure data, the constructed structure constraint conditions are as follows:

[0116]

[0117] Among them, M ij is the mutual inductance between the i-th layer coil and the j-th layer coil; M nk is the mutual inductance between the n-th layer coil and the k-th layer coil; L i is the self-inductance of the i-th layer coil; L j is the self-inductance of the j-th layer coil.

[0118] By using the structure constraint conditions to update the initial ideal circuit model, the target ideal circuit model is obtained. Utilizing the structural characteristics of the reactor itself, constraint conditions are respectively set for the resistance, self-inductance and mutual inductance of each layer of the coil, so that the optimization algorithm can obtain the optimal result more stably and quickly, and improve the convergence efficiency of the algorithm.

[0119] Step 207: Perform parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor.

[0120] Further, step 207 may include the following sub-steps S21-S28:

[0121] S21: Initialize the population by using the model winding parameters of the target ideal circuit model to generate an initial population.

[0122] S22: Calculate the fitness values of each initial individual in the initial population respectively to generate multiple fitness values.

[0123] S23: Select the initial individuals whose fitness values meet the preset threshold by using the selection operator to construct an intermediate population.

[0124] S24: Perform genetic operations on each intermediate individual in the intermediate population respectively by using the preset crossover operator, preset mutation operator and preset probability formula to construct a target population.

[0125] S25. Select the target individual corresponding to the maximum fitness value in the target population as the elite individual and count the number of iterations.

[0126] S26. Determine whether the number of iterations is equal to the preset maximum number of iterations. If so, execute step S27; if not, execute step S28.

[0127] S27. Take the model winding parameters corresponding to the elite individual as the optimal winding parameters of the reactor.

[0128] S28. Take the target population as the new initial population, increment the iteration count by 1, and jump to execute the steps of calculating the fitness values of each initial individual in the initial population respectively to generate multiple fitness values.

[0129] Further, step S24 may include the following sub-steps S241 - S242:

[0130] S241. Recombine the intermediate individuals in the intermediate population respectively according to the preset crossover operator and the crossover probability formula in the preset probability formula to generate multiple recombined individuals.

[0131] S242. Mutate the recombined individuals according to the preset mutation operator and the mutation probability formula in the preset probability formula to generate mutant individuals corresponding to the recombined individuals.

[0132] In the embodiment of the present invention, the adaptive genetic algorithm is improved as a whole by improving the crossover, mutation operators and probability formula. The extended crossover operator is adopted to break through the feasible region of the parent generation and expand the search range of the algorithm; the adaptive mutation operator is adopted to continuously adjust and change the gene boundary with evolution to improve the convergence efficiency. The crossover and mutation probabilities are improved to ensure the diversity characteristics of the population and prevent the population from falling into local optima. Finally, accurate optimization calculation of the equivalent parameters of the reactor network is realized.

[0133] Improve the stability and global search ability of the algorithm, ensure the diversity characteristics of the population, avoid falling into local optima, and improve the crossover and mutation operators in the traditional genetic algorithm. The crossover operator plays a leading role in genetic evolution. A suitable crossover operator will directly affect the optimization effect of the entire algorithm. The traditional crossover operator uses single-point non-uniform crossover, and the search range of the offspring is limited, and the offspring is not necessarily better than the parent. Therefore, in this example, the extended arithmetic crossover operator is adopted. This algorithm makes the offspring after crossover not limited to the parent generation and has a wider search range. The formula of the extended arithmetic crossover operator, that is, the preset crossover operator, is:

[0134]

[0135] where A′ i is the first offspring gene after the crossover operation; A′ jIs the second offspring gene after crossover operation; A i Is the first parent gene before crossover operation; A j Is the second parent gene before crossover operation; n is a random number in [-0.25, 1.25].

[0136] The diversity of population individuals is a necessary condition to ensure that the genetic algorithm converges to the optimal solution. In the process of evolution, the roulette wheel method optimizes according to the fitness value. The larger the fitness value, the higher the probability that an individual is selected, resulting in a decrease in population diversity. The mutation operator can generate new individuals through mutation, thus ensuring population diversity. Therefore, the selection of the mutation operator is also very important. The mutation operator selected by the traditional method is the uniform mutation operator, which mutates the mutated gene using the boundary conditions of the gene. The upper and lower bounds of this operator remain unchanged during the evolution process, resulting in a large mutation scale in the later stage of evolution and poor convergence effect. Therefore, this example proposes a mutation operator with adaptive transformation, which continuously adjusts the gene upper and lower bounds during the evolution process. The improved mutation operator is as follows, that is, the preset mutation operator is:

[0137]

[0138] Among them, x i Is the gene before mutation; x' i Is the gene after mutation, that is, the mutated individual; x min Is the initial minimum value of the gene; x max Is the initial maximum value of the gene; x new min Is the minimum value of the gene during the evolution process; x new max Are the maximum values of the gene during the evolution process respectively; G is the total number of generations of evolution, and the total number of generations of evolution is equal to the preset maximum number of iterations. In this example, the total number of generations of evolution is 500; gen is the current generation of evolution; r is the first parameter, with a value of 0.2; b is the second parameter, with a value of 0.3; r1 is a random number in [0, 1].

[0139] In the crossover operation, the size of the crossover probability determines the number of new individuals that appear during the chromosome crossover process. In the mutation process, the size of the mutation probability also restricts the scale of the population. Therefore, in this example, to ensure the population evolution speed and population diversity and avoid falling into local optima, the expressions of the crossover probability and mutation probability are improved, and an adaptive crossover probability and mutation probability are proposed, and their expressions are as follows:

[0140] The crossover probability formula is:

[0141]

[0142] Among them, P c Is the crossover probability; t is the current number of iterations; P c1 Is the first variable parameter; P c2is the second variable parameter, and P c2 = 0.6; max is the preset maximum number of iterations. In this example, the maximum number of iterations is preset to be 500; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f' is the fitness value of the parent gene with the largest fitness value in the crossover operation

[0143] The preset mutation probability formula is:

[0144]

[0145] where, P m is the mutation probability; P m1 is the third variable parameter; P m2 is the fourth variable parameter, and P m2 = 0.02; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f is the fitness value of the recombinant individual; t is the current iteration number.

[0146] As Figure 4 shown, the specific process of winding parameter optimization in this example is as follows:

[0147] (1) Add random parameters as the initial population, that is, use the model winding parameters of the target ideal circuit model for population initialization to generate the initial population. The model winding parameters here refer to the above-mentioned randomly given resistance, self-inductance, and mutual inductance parameters, namely model parameters.

[0148] (2) Calculate the fitness values of each initial individual in the initial population through the fitness function in the target ideal circuit model to obtain multiple fitness values. And select the population individuals through the selection operator, and then re-form a new population for the selected individuals to obtain the intermediate population. Among them, the selection operator can be one of the many selection methods in the genetic algorithm, and the embodiments of the present invention do not limit this.

[0149] (3) Perform genetic operations on the selected new population to generate a new population, that is, use the preset crossover operator, preset mutation operator, and preset probability formula to perform genetic operations on each intermediate individual in the intermediate population to obtain the target population. Then adopt the elite retention strategy to select the target individual with the largest fitness value in the target population as the elite individual and count the number of iterations.

[0150] (4) Determine whether the number of iterations is equal to the preset maximum number of iterations. If the maximum number of iterations is reached, the loop ends, and the optimal solution is output. The model winding parameters corresponding to the elite individuals are used as the optimal winding parameters of the reactor. Otherwise, the target population is used as the new initial population, the iteration count is incremented by 1, and it jumps to step (2) to continue the iterative optimization.

[0151] By improving the crossover operator and mutation operator, the local convergence effect of the algorithm is enhanced. The improved crossover and mutation probability formulas solve the drawback that the population stops evolving when the crossover and mutation probabilities are zero. By adding structural constraints, the optimization accuracy and efficiency are improved, which has important theoretical value and engineering significance.

[0152] In the embodiment of the present invention, the fitness functions of the actual model and the ideal model are constructed. By using the structural characteristics of the reactor itself, structural constraint conditions are respectively set for the self-inductance and mutual inductance of each layer of coils, enabling the optimization algorithm to obtain the optimal result more stably and quickly, and improving the convergence efficiency of the algorithm. An extended crossover operator is adopted to expand the search range of the offspring, enhance the stability and global search ability, and the mutation operator is improved by using the method of adaptive transformation to ensure the population diversity. In the crossover operation, the crossover probability and mutation probability expressions are improved to enhance the population evolution speed and reduce the possibility of falling into the local optimal solution.

[0153] The method for detecting the winding parameters of a reactor based on structural constraints and the adaptive genetic algorithm has advantages over existing intelligent optimization algorithms such as the ant colony algorithm and particle swarm algorithm in making up for problems such as large computational amount, long running time, and easy to fall into local optimality. By improving the crossover operator, mutation operator, crossover probability, and mutation probability of the traditional genetic algorithm, the winding parameters of the reactor including inductance, resistance, and mutual inductance are accurately obtained, which is beneficial to timely obtaining the working state of the reactor winding and mastering the change of winding parameters, and improving the control ability of the power system over important power equipment.

[0154] Embodiment III

[0155] Please refer to Figure 5 , Figure 5 which is the structural block diagram of a system for detecting the winding parameters of a reactor provided in Embodiment III of the present invention.

[0156] A system for detecting the winding parameters of a reactor provided in Embodiment III of the present invention includes:

[0157] An initial ideal circuit model generation module 501, configured to obtain the operation data and structural data of the reactor, construct a model with the operation data and structural data, and generate an initial ideal circuit model corresponding to the reactor.

[0158] The target ideal circuit model generation module 502 is configured to generate a target ideal circuit model by performing structural constraints on the initial ideal circuit model based on the structural data.

[0159] The optimal winding parameter determination module 503 is configured to perform parameter optimization and solution based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor.

[0160] Optionally, the initial ideal circuit model generation module 501 includes:

[0161] The initial circuit model generation module is configured to construct a model using the rated parameters of the reactor in the structural data to generate an initial circuit model.

[0162] The target circuit model generation module is configured to select model parameters within a preset range and update the initial circuit model using the model parameters to generate a target circuit model.

[0163] The total current calculated value generation module is configured to input the port voltage in the operating data into the target circuit model for total current calculation to generate a total current calculated value.

[0164] The fitness function construction module is configured to construct a fitness function using the difference between the total current in the operating data and the total current calculated value.

[0165] The initial ideal circuit model generation sub-module is configured to update the target circuit model using the fitness function to generate an initial ideal circuit model corresponding to the reactor.

[0166] Optionally, the target ideal circuit model generation module 502 includes:

[0167] The structural constraint condition construction module is configured to construct structural constraint conditions using the coil data in the structural data.

[0168] The structural constraint condition is:

[0169]

[0170] Where M ij is the mutual inductance between the i-th layer coil and the j-th layer coil; M nk is the mutual inductance between the n-th layer coil and the k-th layer coil; L i is the self-inductance of the i-th layer coil; L j is the self-inductance of the j-th layer coil.

[0171] The target ideal circuit model generation sub-module is configured to update the initial ideal circuit model using the structural constraint conditions to generate a target ideal circuit model.

[0172] Optionally, the optimal winding parameter determination module 503 includes:

[0173] An initial population generation module, which is used to initialize the population by using the model winding parameters of the target ideal circuit model to generate an initial population.

[0174] A fitness value generation module, which is used to calculate the fitness values of each initial individual in the initial population respectively to generate multiple fitness values.

[0175] An intermediate population construction module, which is used to select the initial individuals whose fitness values meet the preset threshold by using a selection operator to construct an intermediate population.

[0176] A target population construction module, which is used to perform genetic operations on each intermediate individual in the intermediate population respectively by using a preset crossover operator, a preset mutation operator and a preset probability formula to construct a target population.

[0177] An elite individual and iteration number determination module, which is used to select the target individual corresponding to the maximum fitness value in the target population as the elite individual and count the iteration number.

[0178] An iteration number judgment module, which is used to judge whether the iteration number is equal to the preset maximum iteration number.

[0179] A winding parameter determination sub-module, which is used to, if so, use the model winding parameters corresponding to the elite individual as the optimal winding parameters corresponding to the reactor.

[0180] A jump module, which is used to use the target population as the new initial population, update the iteration number by adding 1, and jump to execute the step of calculating the fitness values of each initial individual in the initial population respectively to generate multiple fitness values.

[0181] Optionally, the target population generation module may perform the following steps:

[0182] Perform superposition recombination on the intermediate individuals in the intermediate population respectively according to the crossover probability formula in the preset crossover operator and the preset probability formula to generate multiple recombinant individuals;

[0183] The preset crossover operator is:

[0184]

[0185] Among them, A′ i is the first offspring gene after the crossover operation; A′ j is the second offspring gene after the crossover operation; A i is the first parent gene before the crossover operation; A j is the second parent gene before the crossover operation; n is a random number in [-0.25, 1.25];

[0186] The crossover probability formula is:

[0187]

[0188] Among them, P c is the crossover probability; t is the current iteration number; P c1 is the first variable parameter; P c2 is the second variable parameter, and P c2 = 0.6; max is the preset maximum number of iterations; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f' is the fitness value of the parent gene with the largest fitness value in the crossover operation;

[0189] Perform a mutation operation on the recombinant individual according to the preset mutation operator and the mutation probability formula in the preset probability formula to generate a mutant individual corresponding to the recombinant individual;

[0190] The preset mutation operator is:

[0191]

[0192] Among them, x i is the gene before mutation; x' i is the gene after mutation, that is, the mutant individual; x min is the initial minimum value of the gene; x max is the initial maximum value of the gene; x newmin is the minimum value of the gene during the evolution process; x newmax are the maximum values of the gene during the evolution process respectively; G is the total number of generations of evolution; gen is the current generation of evolution; r is the first parameter, with a value of 0.2; b is the second parameter, with a value of 0.3; r1 is a random number in [0, 1];

[0193] The preset mutation probability formula is:

[0194]

[0195]

[0196] Among them, P m is the mutation probability; P m1 is the third variable parameter; P m2 is the fourth variable parameter, and P m2 = 0.02; f max is the maximum fitness value in the intermediate population; f a is the average fitness value corresponding to the intermediate population; f is the fitness value of the recombinant individual; t is the current iteration number.

[0197] Construct the target population using all mutant individuals.

[0198] In the embodiment of the present invention, as Figure 6As shown, the system can also be a reactor network parameter optimization system based on structural constraints and an adaptive genetic algorithm. Here, the network parameters refer to the winding parameters of the reactor. The system includes a function setting module, an optimization result module, and a workspace. The function setting module includes parameter setting, model setting, constraint setting, and data storage. The system workspace stores the port voltage and total current of the actual operation of the reactor, as well as the calculated total current value and port voltage calculated value obtained from the model. The results output by the optimization result module include the resistance, inductance, and mutual inductance corresponding to each coil.

[0199] Embodiment 4

[0200] Please refer to Figure 7 , Figure 7 which is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention.

[0201] An electronic device according to an embodiment of the present invention includes a memory 701 and a processor 702. A computer program is stored in the memory 701. When the computer program is executed by the processor 702, the processor 702 is caused to execute the reactor winding parameter detection method according to any of the above embodiments.

[0202] The memory 701 can be an electronic memory such as a flash memory, EEPROM (electrically erasable programmable read-only memory), EPROM, hard disk, or ROM. The memory 701 has a storage space 703 for program code 713 for performing any method steps in the above methods. For example, the storage space 703 for program code can include respective program codes 713 for implementing various steps in the above methods. These program codes can be read out from or written into one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code can be compressed in an appropriate form, for example. When these codes are run by a computing processing device, the computing processing device is caused to execute the respective steps in the reactor winding parameter detection method described above.

[0203] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the reactor winding parameter detection method according to any of the above embodiments.

[0204] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described in detail here.

[0205] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0206] 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 can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, in each embodiment of the present invention, the functional units can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0208] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, and other media that can store program codes.

[0209] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A method for detecting parameters of a reactor winding, characterized in that: include: Acquiring operation data and structural data of the reactor, constructing a model based on the operation data and the structural data, and generating an initial ideal circuit model corresponding to the reactor; The specific steps include: using the rated parameters of the reactor in the structural data to construct a model and generate an initial circuit model; selecting model parameters within a preset interval, and using the model parameters to update the initial circuit model to generate a target circuit model; inputting the port voltage in the operating data into the target circuit model to calculate the total current and generate a total current calculation value; using the difference between the total current in the operating data and the total current calculation value to construct a fitness function; using the fitness function to update the target circuit model to generate an initial ideal circuit model corresponding to the reactor; Based on the structural data, the initial ideal circuit model is structurally constrained to generate a target ideal circuit model; the specific steps include: using the coil data in the structural data to construct structural constraint conditions; the structural constraint conditions are: Among them, M ij is the mutual inductance between the i-th coil and the j-th coil; M nk is the mutual inductance between the nth coil and the kth coil; L i is the self-inductance of the i-th coil; L j is the self-inductance of the j-th layer coil; the initial ideal circuit model is updated using the structural constraint condition to generate a target ideal circuit model; Parameter optimization is performed based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor; the specific steps include: using the model winding parameters of the target ideal circuit model to initialize the population and generate an initial population; calculating the fitness value of each initial individual in the initial population respectively to generate multiple fitness values; using a selection operator to select the initial individual whose fitness value meets a preset threshold to construct an intermediate population; using a preset crossover operator, a preset mutation operator and a preset probability formula to perform genetic operations on each intermediate individual in the intermediate population respectively to construct a target population; selecting the target individual corresponding to the maximum fitness value in the target population as an elite individual and counting the number of iterations; judging whether the number of iterations is equal to the preset maximum number of iterations; if so, using the model winding parameters corresponding to the elite individual as the optimal winding parameters corresponding to the reactor; if not, using the target population as a new initial population, updating the number of iterations plus 1, and jumping to execute the step of respectively calculating the fitness value of each initial individual in the initial population to generate multiple fitness values.

2. The method for detecting reactor winding parameters according to claim 1, characterized in that: The step of using a preset crossover operator, a preset mutation operator and a preset probability formula to perform genetic operations on each intermediate individual in the intermediate population to construct a target population includes: According to a preset crossover operator and a crossover probability formula in a preset probability formula, the intermediate individuals in the intermediate population are respectively superimposed and recombined to generate a plurality of recombinant individuals; The preset crossover operator is: Among them, A i ' is the first offspring gene after the crossover operation; A j ' is the second offspring gene after the crossover operation; A i is the first parent gene before the crossover operation; A j is the second parent gene before the crossover operation; n is a random number between [-0.25, 1.25]; The crossover probability formula is: Among them, P c is the crossover probability; t is the current iteration number; P c1 is the first variable parameter; P c2 is the second variable parameter, and P c2 =0.6; max is the preset maximum number of iterations; f max is the maximum fitness value in the middle population; f a is the average fitness value corresponding to the intermediate population; f′ is the fitness value of the party with the largest parent gene fitness value in the crossover operation; Performing a mutation operation on the recombinant individual according to a preset mutation operator and a mutation probability formula in the preset probability formula to generate a mutant individual corresponding to the recombinant individual; The preset mutation operator is: Among them, x i is the gene before mutation; x i ' is the mutated gene, i.e. the mutated individual; x min is the initial minimum value of the gene; x max is the initial maximum value of the gene; x newmin is the minimum value of genes in the evolution process; x newmax are the maximum values ​​of genes in the evolution process; G is the total number of evolutionary generations; gen is the current number of evolutionary generations; r is the first parameter, with a value of 0.2; b is the second parameter, with a value of 0.3; r1 is a random number in [0, 1]; The preset mutation probability formula is: Among them, P m is the mutation probability; P m1 is the third variable parameter; P m2 is the fourth variable parameter, and P m2 =0.02; f max is the maximum fitness value in the middle population; f a is the average fitness value corresponding to the intermediate population; f is the fitness value of the recombinant individual; t is the current iteration number; All of the variant individuals are used to construct a target population.

3. A reactor winding parameter detection system, characterized in that: include: An initial ideal circuit model generation module is used to obtain the operation data and structure data of the reactor, construct a model with the operation data and the structure data, and generate an initial ideal circuit model corresponding to the reactor; the specific modules include: an initial circuit model generation module, which is used to construct a model using the rated parameters of the reactor in the structure data to generate an initial circuit model; a target circuit model generation module, which is used to select model parameters within a preset interval, and use the model parameters to update the initial circuit model to generate a target circuit model; a total current calculation value generation module, which is used to input the port voltage in the operation data into the target circuit model to calculate the total current and generate a total current calculation value; a fitness function construction module, which is used to construct a fitness function using the difference between the total current in the operation data and the total current calculation value; an initial ideal circuit model generation submodule, which is used to update the target circuit model using the fitness function to generate an initial ideal circuit model corresponding to the reactor; The target ideal circuit model generation module is used to perform structural constraints on the initial ideal circuit model based on the structural data to generate a target ideal circuit model; the specific modules include: a structural constraint condition construction module, which is used to use the coil data in the structural data to construct structural constraint conditions; the structural constraint conditions are: Among them, M ij is the mutual inductance between the i-th coil and the j-th coil; M nk is the mutual inductance between the nth coil and the kth coil; L i is the self-inductance of the i-th coil; L j is the self-inductance of the j-th layer coil; a target ideal circuit model generation submodule, used to update the initial ideal circuit model using the structural constraint condition to generate a target ideal circuit model; The optimal winding parameter determination module is used to perform parameter optimization based on the model winding parameters of the target ideal circuit model to determine the optimal winding parameters corresponding to the reactor; the specific modules include: an initial population generation module, which is used to use the model winding parameters of the target ideal circuit model to initialize the population and generate an initial population; a fitness value generation module, which is used to calculate the fitness value of each initial individual in the initial population and generate multiple fitness values; an intermediate population construction module, which is used to use a selection operator to select initial individuals whose fitness values ​​meet a preset threshold value to construct an intermediate population; a target population construction module, which is used to use a preset crossover operator, a preset mutation operator and a preset probability formula to generate a fitness value of each initial individual in the initial population; The formula performs genetic operations on each intermediate individual in the intermediate population to construct a target population; an elite individual and iteration number determination module is used to select the target individual corresponding to the maximum fitness value in the target population as the elite individual and count the iteration number; an iteration number judgment module is used to judge whether the iteration number is equal to the preset maximum iteration number; a winding parameter determination submodule is used to, if yes, use the model winding parameters corresponding to the elite individual as the optimal winding parameters corresponding to the reactor; if not, use the target population as the new initial population, update the iteration number plus 1, and jump to execute the step of respectively calculating the fitness value of each initial individual in the initial population to generate multiple fitness values.

4. An electronic device, characterized in that: It comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the reactor winding parameter detection method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the method for detecting reactor winding parameters according to any one of claims 1 to 2 is implemented.

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