Parameter identification method and system of transformer main insulation equivalent model, and medium

Abstract

The embodiment of the invention discloses a parameter identification method and system for a transformer main insulation equivalent model and a medium. The method comprises the following steps: determining a fitting value of a complex capacitance real part and a fitting value of a complex capacitance imaginary part of transformer main insulation according to admittance of an oil gap part, a supporting strip part and a partition plate part in the transformer main insulation equivalent model; and determining a target function of the vector to be solved according to the fitting value of the complex capacitance real part and the fitting value of the complex capacitance imaginary part of the main insulation of the transformer. And defining the population scale and the maximum number of iterations. Updating the positions of elite individuals with fitness smaller than a preset threshold value in the population until the maximum number of iterations is reached, selecting a to-be-solved vector with the minimum target function after the positions are updated, and enabling each element in the vector to correspond to a to-be-solved parameter in the transformer main insulation equivalent model to obtain a to-be-solved parameter; and parameter identification of the transformer main insulation equivalent model is completed, so that the states of the three parts are independently evaluated and diagnosed.

Description

Technical Field

[0001] The invention relates to the technical field of power transformer state monitoring, and in particular to a parameter identification method, system and medium of a transformer main insulation equivalent model. Background Art

[0002] Transformers are an important part of power grid operation. The insulation performance of transformers is closely related to the stable operation of power grids. Therefore, it is necessary to detect the insulation status of transformers. The main insulation of transformers is located inside the transformer, which mainly includes three main parts: insulating oil gap, support bar and partition. Among them, the main material of support bar and partition is cellulose insulation paperboard. Therefore, the main insulation of transformers is a composite oil-paper insulation structure.

[0003] At present, the dielectric response method can be used to realize non-destructive diagnosis of the state of oil-paper insulation (moisture condition, aging degree). The main means is to establish an equivalent model of dielectric response, and quantitatively reflect the change of insulation state through the change of model parameters of the equivalent model. However, the existing traditional dielectric response models such as extended Debye model and Cole-Cole model regard the main insulation as a black box, and cannot distinguish the dielectric properties of the three parts of the insulation oil gap, support bar and partition separately. There is a lack of corresponding equivalent model parameter identification algorithm, so it is impossible to perform separate diagnosis and evaluation of the insulation state of the above parts, which limits the precise inspection and maintenance of insulation faults in various parts of the transformer main insulation. Summary of the invention

[0004] Based on this, it is necessary to propose a parameter identification method, system and medium for the transformer main insulation equivalent model to address the above problems.

[0005] A method for identifying parameters of a transformer main insulation equivalent model, the method comprising:

[0006] The fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation are determined according to the admittance of the oil gap part, the support bar part and the partition part in the transformer main insulation equivalent model.

[0007] The objective function of the vector to be solved is determined according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the main insulation of the transformer.

[0008] Define the population size and the maximum number of iterations.

[0009] The positions of elite individuals in the population whose fitness is less than a preset threshold are updated until the maximum number of iterations is reached. Then, the vector to be solved with the smallest objective function after the position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0010] The step of determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the strut part and the partition part in the transformer main insulation equivalent model specifically includes:

[0011] according to Determine the admittance of the oil gap in the transformer main insulation equivalent model, where Z o is the impedance of the oil gap part, C0 is the capacitance parameter of the oil gap part, R0 is the resistance parameter of the oil gap part, and ω is the angular frequency.

[0012] according to Determine the admittance of the brace part in the transformer main insulation equivalent model, where Z S is the impedance of the brace part, C s0 is the capacitance parameter of the support part, R s0 is the resistance parameter of the support part, R si is the resistance parameter of the support part of the i-th polarization branch, C si is the capacitance parameter of the support part of the i-th polarization branch, ω is the angular frequency, n is the maximum number of polarization branches, and i is the current number of polarization branches.

[0013] according to Determine the admittance of the partition part in the transformer main insulation equivalent model, where Z B is the impedance of the partition, C B0 is the capacitance parameter of the partition part, R B0 is the resistance parameter of the partition part, i is the number of current polarization branches, R Bi is the resistance parameter of the partition part of the i-th polarization branch, C Bi is the capacitance parameter of the partition part of the i-th polarization branch, ω is the angular frequency, and n is the number of branches.

[0014] The total circuit admittance is determined according to the admittances of the oil gap part, the support bar part and the partition part in the transformer main insulation equivalent model.

[0015] The fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation are determined according to the total admittance of the circuit.

[0016] The step of determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the total admittance of the circuit specifically includes:

[0017] according to Determine the complex capacitance of the transformer main insulation, where C*() is the complex capacitance, Z is the total circuit impedance, ω is the angular frequency, and j is the imaginary unit;

[0018] According to C' 拟合(ω)=real(C * ) Determine the fitted value of the real part of the complex capacitance of the transformer main insulation, where C' 拟合 () is the fitted value of the real part of the complex capacitance, C* is the complex capacitance;

[0019] According to C'' 拟合 (ω)=-Imag(C * ) Determine the fitting value of the imaginary part of the complex capacitance of the transformer main insulation, where C'' 拟合 () is the fitted value of the imaginary part of the complex capacitance, and C* is the complex capacitance.

[0020] The step of determining the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the main insulation of the transformer specifically includes:

[0021]

[0022] Wherein, C'measured() is the measured value of the real part of the complex capacitance, C"measured() is the measured value of the imaginary part of the complex capacitance, m is the maximum number of candidate solutions, i is the current number of candidate solutions for the vector to be solved, X is the vector to be solved, C'fit() is the fitted value of the real part of the complex capacitance, and C"fit() is the fitted value of the imaginary part of the complex capacitance.

[0023] Among them, the elite individuals in the population whose fitness is less than a preset threshold are updated in position until the maximum number of iterations is reached, and the vector to be solved with the smallest objective function after the position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, and the parameter identification of the transformer main insulation equivalent model is completed, which specifically includes:

[0024] The elite individuals in the population whose fitness is less than the preset threshold are updated for the first time as candidate solutions to obtain the candidate solutions after elite update.

[0025] The candidate solution after the elite update is added to the candidate solution of the elite individual greater than a preset threshold, and the position of the elements in the candidate solution of the added elite individual is updated for the second time to determine the update value of the candidate solution element.

[0026] The position of the candidate solution element update value and the candidate solution corresponding to the candidate solution element update value is updated for the third time.

[0027] The candidate solution element update value after the third position update and the candidate solution corresponding to the candidate solution element update value after the third position update are updated for the fourth time.

[0028] Determine whether the current number of iterations has reached the maximum number of iterations.

[0029] If the current number of iterations reaches the maximum number of iterations, the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, wherein the parameters to be solved include capacitance parameters and resistance parameters, and the parameter identification of the transformer main insulation equivalent model is completed.

[0030] The determining whether the current number of iterations reaches the maximum number of iterations further specifically includes:

[0031] If the current number of iterations does not reach the maximum number of iterations, the positions of the elite individuals in the population whose fitness is less than the preset threshold are updated again.

[0032] The first position update of the candidate solution for the elite individuals in the population whose fitness is less than a preset threshold to obtain the candidate solution after the elite update specifically includes:

[0033] according to The elite individuals in the population whose fitness is less than the preset threshold are updated for the first time as candidate solutions to obtain the candidate solutions after elite update, where: is the candidate solution after elite update, is the i-th candidate solution of the vector to be solved after the first position update, X i is the i-th candidate solution of the vector to be solved, F i is the fitness of the i-th candidate solution, F i * is the fitness of the i-th candidate solution after the first position update.

[0034] according to is the i-th candidate solution of the vector to be solved after the first position update, X i is the i-th candidate solution of the vector to be solved, δ is the step size, s is the walking step size, and β is the parameter.

[0035] according to Among them, μ and v are direction vectors, and β is a parameter.

[0036] The candidate solution after the elite update is added to the candidate solution of the elite individual greater than the preset threshold, and the elements in the candidate solution of the added elite individual are updated for the second time to determine the update value of the candidate solution element, which specifically includes:

[0037] according to The elements in the candidate solution of the added elite individuals are updated for the second time to determine the update value of the candidate solution element, where R1 is the random step length of the population distance to the optimal solution; R2 is the random step length of the population distance to the worst solution, x i,jis the jth element in the i-th candidate solution of the added elite individual, and is a random number in [0,1], is the optimal solution in the population, is the worst solution in the population, Update the values ​​for the candidate solution elements.

[0038] The third position update of the candidate solution element update value and the corresponding candidate solution specifically includes:

[0039] according to Perform a third position update on the candidate solution element update value, where: is the updated value of the candidate solution element after the third position is updated, rand is a random number in [0,1], p j is the value of the jth element of the random elite individual, α is a random number of 1 or 2, F p is the fitness of the random elite individual, F i is the fitness of the i-th elite individual.

[0040] according to The candidate solution corresponding to the updated value of the candidate solution element is updated for the third time, where: is the value of the third position update of the i-th candidate solution, F i P1 is the fitness of the i-th candidate solution after the third position update.

[0041] The fourth position update of the candidate solution element update value and the corresponding candidate solution after the third position update specifically includes:

[0042] according to The candidate solution element update value after the third position update is updated for the fourth time, where: is the value of the jth element in the i-th candidate solution after the fourth position update, R is a constant, N iter is the current iteration number, is the maximum number of iterations.

[0043] according to The candidate solution corresponding to the updated value of the candidate solution element after the third position update is updated for the fourth time, where: is the value of the fourth position update of the i-th candidate solution, F i P2 is the fitness of the i-th candidate solution after the fourth position update.

[0044] A parameter identification system for a transformer main insulation equivalent model, the system comprising:

[0045] The module for determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance is used to determine the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the support bar part and the partition part in the transformer main insulation equivalent model.

[0046] The objective function determination module of the vector to be solved is used to determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the main insulation of the transformer.

[0047] Definition module, used to define the population size and the maximum number of iterations.

[0048] The parameter identification module is used to update the position of the elite individuals in the population whose fitness is before the preset threshold until the maximum number of iterations is reached, and then select the vector to be solved with the smallest objective function after the position update, and correspond each element in the vector to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0049] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the method described above.

[0050] The embodiments of the present invention have the following beneficial effects:

[0051] The present invention is applicable to a dielectric response equivalent model combining an XY model and an extended Debye model for transformer main insulation. By accurately measuring and calculating the admittance of the oil gap part, the stay part and the partition part in the transformer main insulation equivalent model, the actual electrical characteristics of the transformer main insulation can be more accurately reflected. The objective function is defined by the calculated admittance of the oil gap part, the stay part and the partition part to clarify the goal of the optimization algorithm, that is, to find a vector to be solved that minimizes the objective function. Furthermore, the elite individuals whose fitness is less than a preset threshold are updated in position, which helps to retain excellent individuals in the population and accelerate the convergence process. After reaching the maximum number of iterations, the vector to be solved with the smallest objective function after the position update is selected as the optimal solution, and each element therein is respectively corresponded to the parameters to be solved in the transformer main insulation equivalent model, and the parameter values ​​of each circuit element in the model can be obtained by inverse solution. By comparing the changing trends of the component parameter values, the states of the above three parts can be evaluated and diagnosed separately, thereby determining the maintenance direction of the transformer more quickly. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] in:

[0054] Figure 1 A flow chart of an embodiment of a method for identifying parameters of a transformer main insulation equivalent model provided by the present invention;

[0055] Figure 2 A schematic structural diagram of an embodiment of a transformer main insulation equivalent model provided by the present invention;

[0056] Figure 3 A flow chart of another embodiment of a method for identifying parameters of a transformer main insulation equivalent model provided by the present invention;

[0057] Figure 4 A schematic structural diagram of an embodiment of a parameter identification system for a transformer main insulation equivalent model provided by the present invention;

[0058] Figure 5 A schematic structural diagram of an embodiment of the medium provided by the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0060] like Figure 1 As shown, Figure 1 A flow chart of an embodiment of a method for identifying parameters of a transformer main insulation equivalent model provided by the present invention. A method for identifying parameters of a transformer main insulation equivalent model, the method comprising:

[0061] S101: Determine a fitting value of a real part of complex capacitance and a fitting value of an imaginary part of complex capacitance of a transformer main insulation according to the admittance of an oil gap part, a stay part and a partition part in an equivalent model of the transformer main insulation.

[0062] For example, referring to Figure 2 , Figure 2The schematic diagram of the structure of an embodiment of the transformer main insulation equivalent model provided by the present invention. The admittance of the oil gap part in the transformer main insulation equivalent model is determined according to the following formula:

[0063]

[0064] Among them, Z o is the impedance of the oil gap part, C0 is the capacitance parameter of the oil gap part, R0 is the resistance parameter of the oil gap part, and ω is the angular frequency.

[0065] The admittance of the brace part in the transformer main insulation equivalent model is determined according to the following formula:

[0066]

[0067] Among them, Z S is the impedance of the brace part, C s0 is the capacitance parameter of the support part, R s0 is the resistance parameter of the support part, R si is the resistance parameter of the support part of the i-th polarization branch, C si is the capacitance parameter of the support part of the i-th polarization branch, ω is the angular frequency, n is the maximum number of polarization branches, and i is the current number of polarization branches.

[0068] The admittance of the partition part in the transformer main insulation equivalent model is determined as follows:

[0069]

[0070] Among them, Z B is the impedance of the partition, C B0 is the capacitance parameter of the partition part, R B0 is the resistance parameter of the partition part, i is the number of current polarization branches, R Bi is the resistance parameter of the partition part of the i-th polarization branch, C Bi is the capacitance parameter of the partition part of the i-th polarization branch, ω is the angular frequency, and n is the number of branches.

[0071] Determine the total circuit admittance based on the admittance of the oil gap, brace and partition in the transformer main insulation equivalent model; Determine the complex capacitance of the transformer main insulation, where C*() is the complex capacitance, Z is the total circuit impedance, ω is the angular frequency, and j is the imaginary unit.

[0072] Further, the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance are determined according to the total admittance of the circuit. Specifically, the fitting value of the real part of the complex capacitance of the transformer main insulation is determined according to the following formula:

[0073] C' 拟合 (ω)=real(C * );

[0074] Among them, C' 拟合 () is the fitted value of the real part of the complex capacitance, and C* is the complex capacitance.

[0075] The fitted value of the imaginary part of the complex capacitance of the transformer main insulation is determined according to the following formula:

[0076] C'' 拟合 (ω)=-Imag(C * );

[0077] Among them, C'' 拟合 () is the fitted value of the imaginary part of the complex capacitance, and C* is the complex capacitance.

[0078] S102: Determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance.

[0079] For example,

[0080] Wherein, C'measured() is the measured value of the real part of the complex capacitance, C"measured() is the measured value of the imaginary part of the complex capacitance, m is the maximum number of candidate solutions, i is the current number of candidate solutions for the vector to be solved, X is the vector to be solved, C'fit() is the fitted value of the real part of the complex capacitance, and C"fit() is the fitted value of the imaginary part of the complex capacitance.

[0081] It should be noted that Figure 2 The polarization branches of the middle support bar and the partition board (i.e. Figure 2 The number n of "capacitor-resistor" series branches in the dotted boxes of the struts and partitions shown is set to 3. Then, under the given model circuit structure, there are a total of 18 capacitor and resistor parameter values ​​to be solved. All of them are combined into a vector to be solved X = [Co Ro CB0…]. There is no specific order requirement inside. You only need to keep the internal order unchanged during the execution of the following steps to participate in the calculation.

[0082] S103: Define the population size and maximum number of iterations.

[0083] Exemplarily, the Pelican optimization algorithm is used to solve the objective function of the vector to be solved. First, the population size and the maximum number of iterations of the algorithm are defined.

[0084] S104: Update the positions of elite individuals in the population whose fitness is less than a preset threshold until the maximum number of iterations is reached, then select the vector to be solved with the smallest objective function after the position update, and correspond each element in the vector to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0085] Exemplarily, the first position update of the candidate solution is performed on the elite individuals in the population whose fitness is less than a preset threshold, and the candidate solution after the elite update is obtained; the candidate solution after the elite update is added to the candidate solution of the elite individuals whose fitness is greater than the preset threshold, and the position of the elements in the candidate solution of the added elite individuals is updated for a second time to determine the candidate solution element update value; the candidate solution element update value and the candidate solution corresponding to the candidate solution element update value are updated for a third time; the candidate solution element update value after the third position update and the candidate solution corresponding to the candidate solution element update value after the third position update are updated for a fourth time.

[0086] Furthermore, it is determined whether the current number of iterations reaches the maximum number of iterations; if the current number of iterations reaches the maximum number of iterations, the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, and the parameters to be solved include capacitance parameters and resistance parameters, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0087] It can be seen from the above description that the present invention is applicable to the dielectric response equivalent model of the transformer main insulation, which is a combination of the XY model and the extended Debye model. By accurately measuring and calculating the admittance of the oil gap part, the brace part and the partition part in the transformer main insulation equivalent model, the actual electrical characteristics of the transformer main insulation can be more accurately reflected. The objective function is defined by the calculated admittance of the oil gap part, the brace part and the partition part to clarify the goal of the optimization algorithm, that is, to find the vector to be solved that minimizes the objective function. Furthermore, the elite individuals whose fitness is less than the preset threshold are updated in position, which helps to retain the excellent individuals in the population and accelerate the convergence process. After reaching the maximum number of iterations, the vector to be solved with the smallest objective function after the position update is selected as the optimal solution, and each element therein is respectively corresponded to the parameters to be solved in the transformer main insulation equivalent model, and the parameter values ​​of each circuit element in the model can be obtained by inverse solution. By comparing the changing trend of the component parameter values, the status of the above three parts can be evaluated and diagnosed separately, thereby determining the maintenance direction of the transformer more quickly.

[0088] like Figure 3 As shown, Figure 3 A flow chart of another embodiment of a method for identifying parameters of a transformer main insulation equivalent model provided by the present invention. A method for identifying parameters of a transformer main insulation equivalent model, the method comprising:

[0089] S201: Determine the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the strut part and the partition part in the transformer main insulation equivalent model.

[0090] S202: Determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance.

[0091] For example, steps S201-S202 are performed in Figure 1 This has been discussed in detail in the implementation scenario shown and will not be repeated here.

[0092] S203: Performing a first position update of the candidate solution for the elite individuals in the population whose fitness is less than a preset threshold, and obtaining the candidate solution after the elite update.

[0093] For example, first, define the population size, the maximum number of iterations of the algorithm, set the upper and lower limits of the parameters, and initialize the population position. Further, calculate the fitness of all individuals in the population, and update the first position of the candidate solution for the elite individuals with fitness less than 10% in the population according to the following formula to obtain the candidate solution after the elite update:

[0094]

[0095] in, is the candidate solution after elite update, is the i-th candidate solution of the vector to be solved after the first position update, X i is the i-th candidate solution of the vector to be solved, F i is the fitness of the i-th candidate solution, F i * is the fitness of the i-th candidate solution after the first position update.

[0096] The i-th candidate solution of the vector to be solved after the first position update is determined according to the following formula:

[0097]

[0098] Among them, X i is the i-th candidate solution of the vector to be solved, δ is the step size, s is the walking step size, and β is a parameter of 1.5.

[0099] The walking step length is determined according to the following formula:

[0100]

[0101] Among them, β is a parameter, μ and v are direction vectors, and they obey the normal distribution: Its standard deviation is:

[0102]

[0103] Among them, Γ is the gamma function, and its expression is:

[0104] S204: The candidate solution after the elite update is added to the candidate solution of the elite individual greater than a preset threshold, and the position of the elements in the candidate solution of the elite individual after the addition is updated for the second time to determine the update value of the candidate solution element.

[0105] Exemplarily, the position of the elements in the candidate solution of the added elite individuals is updated for the second time according to the following formula to determine the update value of the candidate solution element:

[0106]

[0107] Among them, R1 is the random step length of the population distance to the optimal solution; R2 is the random step length of the population distance to the worst solution, x i,j is the jth element in the i-th candidate solution of the added elite individual, and is a random number in [0,1], is the optimal solution in the population, is the worst solution in the population, Update the values ​​for the candidate solution elements.

[0108] S205: Perform a third position update on the candidate solution element update value and the candidate solution corresponding to the candidate solution element update value.

[0109] Exemplarily, the third position update is performed on the candidate solution element update value according to the following formula:

[0110]

[0111] in, is the updated value of the candidate solution element after the third position is updated, rand is a random number in [0,1], p j is the value of the jth element of the random elite individual, α is a random number of 1 or 2, F p is the fitness of the random elite individual, F i is the fitness of the i-th elite individual;

[0112] According to the above formula, the candidate solution element update value corresponds to the candidate solution Its corresponding fitness F i P1 If the condition is improved, the current candidate solution is updated. Specifically, the position of the candidate solution corresponding to the updated value of the candidate solution element is updated for the third time according to the following formula:

[0113]

[0114] in, is the value of the third position update of the i-th candidate solution, F i P1 is the fitness of the i-th candidate solution after the third position update.

[0115] S206: Perform a fourth position update on the candidate solution element update value after the third position update and the candidate solution corresponding to the candidate solution element update value after the third position update.

[0116] Exemplarily, the candidate solution element update value after the third position update is updated for the fourth time according to the following formula:

[0117]

[0118] in, is the value of the jth element in the i-th candidate solution after the fourth position update, R is a constant, N iter is the current iteration number, is the maximum number of iterations;

[0119] according to The candidate solution corresponding to the updated value of the candidate solution element after the third position update is updated for the fourth time, where: is the value of the fourth position update of the i-th candidate solution, F i P2 is the fitness of the i-th candidate solution after the fourth position update.

[0120] S207: Determine whether the current number of iterations reaches the maximum number of iterations.

[0121] S2071: If the current number of iterations reaches the maximum number of iterations, then the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model. The parameters to be solved include capacitance parameters and resistance parameters, and the parameter identification of the transformer main insulation equivalent model is completed.

[0122] S2072: If the current number of iterations has not reached the maximum number of iterations, the positions of the elite individuals in the population whose fitness is less than the preset threshold are updated again.

[0123] Exemplarily, it is determined whether the current number of iterations reaches the maximum number of iterations. If the current number of iterations reaches the maximum number of iterations, the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, and the parameters to be solved include capacitance parameters and resistance parameters, and the parameter identification of the transformer main insulation equivalent model is completed. If the current number of iterations does not reach the maximum number of iterations, return to step S203.

[0124] like Figure 4 As shown, Figure 4 A schematic diagram of a transformer main insulation equivalent model parameter identification system according to an embodiment of the present invention. A transformer main insulation equivalent model parameter identification system 10, the system comprising:

[0125] The fitting value determination module 11 of the real part of complex capacitance and the fitting value of the imaginary part of complex capacitance is used to determine the fitting value of the real part of complex capacitance and the fitting value of the imaginary part of complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the strut part and the partition part in the transformer main insulation equivalent model.

[0126] The objective function determination module 12 of the vector to be solved is used to determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation.

[0127] The definition module 13 is used to define the population size and the maximum number of iterations.

[0128] The parameter identification module 14 is used to update the position of the elite individuals in the population whose fitness is before the preset threshold until the maximum number of iterations is reached, and then select the vector to be solved with the smallest objective function after the position update, and correspond each element in the vector to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0129] Exemplarily, in the fitting value determination module 11 of the real part of the complex capacitance and the imaginary part of the complex capacitance, according to Determine the admittance of the oil gap part in the transformer main insulation equivalent model, where Zo is the impedance of the oil gap part, C0 is the capacitance parameter of the oil gap part, R0 is the resistance parameter of the oil gap part, and ω is the angular frequency; according to Determine the admittance of the brace part in the transformer main insulation equivalent model, where ZS is the impedance of the brace part, Cs0 is the capacitance parameter of the brace part, Rs0 is the resistance parameter of the brace part, Rsi is the resistance parameter of the brace part of the i-th polarization branch, Csi is the capacitance parameter of the brace part of the i-th polarization branch, is the angular frequency, n is the maximum number of polarization branches, and i is the current number of polarization branches; according to Determine the admittance of the partition part in the transformer main insulation equivalent model, where ZB is the impedance of the partition part, CB0 is the capacitance parameter of the partition part, RB0 is the resistance parameter of the partition part, i is the number of current polarization branches, RBi is the resistance parameter of the partition part of the i-th polarization branch, CBi is the capacitance parameter of the partition part of the i-th polarization branch, is the angular frequency, and n is the number of branches; determine the total circuit admittance according to the admittance of the oil gap part, the strut part and the partition part in the transformer main insulation equivalent model; determine the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance according to the total circuit admittance. Further, in the target function determination module 12 of the vector to be solved, the target function of the vector to be solved is determined according to the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance. Further, in the definition module 13, define the population size and the maximum number of iterations. Furthermore, in the parameter identification module 14, the elite individuals in the population whose fitness is less than a preset threshold are subjected to the first position update of the candidate solution to obtain the candidate solution after the elite update; the candidate solution after the elite update is added to the candidate solution of the elite individuals whose fitness is greater than the preset threshold, and the elements in the candidate solution of the added elite individuals are subjected to the second position update to determine the candidate solution element update value; the candidate solution element update value and the candidate solution corresponding to the candidate solution element update value are subjected to the third position update; the candidate solution element update value after the third position update and the candidate solution corresponding to the candidate solution element update value after the third position update are subjected to the fourth position update; it is determined whether the current number of iterations reaches the maximum number of iterations; if the current number of iterations reaches the maximum number of iterations, the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, and the parameters to be solved include capacitance parameters and resistance parameters, so as to complete the parameter identification of the transformer main insulation equivalent model.

[0130] like Figure 5 As shown, Figure 5 The structure diagram of an embodiment of the medium provided by the present invention is shown in FIG. The medium 20 stores at least one computer program 21, which is executed by a processor to implement the following Figure 1 and Figure 3 In one embodiment, the medium 30 may be a storage chip, a hard disk, a mobile hard disk, a USB flash drive, an optical disk, or other readable and writable storage tools, or a server, etc.

[0131] Additionally, the processes depicted in the figures do not necessarily have to be performed in the particular order shown or in sequential order to achieve desired results. In certain implementations, multitasking and parallel processing are also possible or may be advantageous.

[0132] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0133] The apparatus, device, non-volatile computer-readable storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, device, and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the corresponding apparatus, device, and non-volatile computer storage medium will not be repeated here.

[0134] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0135] For the convenience of description, the above device is described by being divided into various units according to their functions and described separately. Of course, when implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware. It should be understood by those skilled in the art that this specification embodiment can be provided as a method, system, or computer program product. Therefore, this specification embodiment can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification embodiment can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This specification is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0139] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0140] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0141] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0142] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0143] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0144] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0145] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A parameter identification method for a transformer main insulation equivalent model, characterized in that: The method comprises: Determine the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the stay part and the partition part in the transformer main insulation equivalent model; Determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the main insulation of the transformer; Define the population size and maximum number of iterations; The positions of elite individuals in the population whose fitness is less than a preset threshold are updated until the maximum number of iterations is reached. Then, the vector to be solved with the smallest objective function after the position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

2. The parameter identification method of a transformer main insulation equivalent model according to claim 1 is characterized in that: The step of determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the strut part and the partition part in the transformer main insulation equivalent model specifically includes: according to Determine the admittance of the oil gap in the transformer main insulation equivalent model, where Z o is the impedance of the oil gap, C0 is the capacitance parameter of the oil gap, R0 is the resistance parameter of the oil gap, and ω is the angular frequency; according to Determine the admittance of the brace part in the transformer main insulation equivalent model, where Z S is the impedance of the brace part, C s0 is the capacitance parameter of the support part, R s0 is the resistance parameter of the support part, R si is the resistance parameter of the support part of the i-th polarization branch, C si is the capacitance parameter of the strut part of the i-th polarization branch, ω is the angular frequency, n is the maximum number of polarization branches, and i is the current number of polarization branches; according to Determine the admittance of the partition part in the transformer main insulation equivalent model, where Z B is the impedance of the partition, C B0 is the capacitance parameter of the partition part, R B0 is the resistance parameter of the partition part, i is the number of current polarization branches, R Bi is the resistance parameter of the partition part of the i-th polarization branch, C Bi is the capacitance parameter of the partition part of the i-th polarization branch, ω is the angular frequency, and n is the number of branches; Determine the total circuit admittance according to the admittance of the oil gap part, the stay part and the partition part in the transformer main insulation equivalent model; The fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation are determined according to the total admittance of the circuit.

3. The parameter identification method of a transformer main insulation equivalent model according to claim 1 is characterized in that: The step of determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the total admittance of the circuit specifically includes: according to Determine the complex capacitance of the transformer main insulation, where C*() is the complex capacitance, Z is the total circuit impedance, ω is the angular frequency, and j is the imaginary unit; According to C' 拟合 (ω)=real(C * ) Determine the fitted value of the real part of the complex capacitance of the transformer main insulation, where C' 拟合 () is the fitted value of the real part of the complex capacitance, C* is the complex capacitance; According to C'' 拟合 (ω)=-Imag(C * ) Determine the fitting value of the imaginary part of the complex capacitance of the transformer main insulation, where C'' 拟合 () is the fitted value of the imaginary part of the complex capacitance, and C* is the complex capacitance.

4. The parameter identification method of a transformer main insulation equivalent model according to claim 3 is characterized in that: The step of determining the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance of the transformer main insulation and the fitting value of the imaginary part of the complex capacitance specifically includes: Where C'measured() is the real part of the complex capacitance, C"measured() is the imaginary part of the complex capacitance, m is the maximum number of candidate solutions, i is the current number of candidate solutions for the vector to be solved, X is the vector to be solved, C' 拟合 () is the fitting value of the real part of the complex capacitance, C" 拟合 () is the fitted value of the imaginary part of the complex capacitance.

5. The parameter identification method of a transformer main insulation equivalent model according to claim 4 is characterized in that: The elite individuals in the population whose fitness is less than a preset threshold are updated in position until the maximum number of iterations is reached, and the vector to be solved with the smallest objective function after the position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, and the parameter identification of the transformer main insulation equivalent model is completed, specifically including: Perform the first position update of the candidate solution for the elite individuals in the population whose fitness is less than the preset threshold, and obtain the candidate solution after the elite update; The candidate solution after the elite update is added to the candidate solution of the elite individual greater than a preset threshold, and the position of the elements in the candidate solution of the elite individual after the addition is updated for the second time to determine the update value of the candidate solution element; Performing a third position update on the candidate solution element update value and the candidate solution corresponding to the candidate solution element update value; Performing a fourth position update on the candidate solution element update value after the third position update and the candidate solution corresponding to the candidate solution element update value after the third position update; Determine whether the current number of iterations has reached the maximum number of iterations; If the current number of iterations reaches the maximum number of iterations, the vector to be solved with the smallest objective function after the fourth position update is selected, and each element in the vector corresponds to the parameters to be solved in the transformer main insulation equivalent model, wherein the parameters to be solved include capacitance parameters and resistance parameters, and the parameter identification of the transformer main insulation equivalent model is completed.

6. The parameter identification method of a transformer main insulation equivalent model according to claim 5 is characterized in that: The determining whether the current number of iterations reaches the maximum number of iterations further specifically includes: If the current number of iterations does not reach the maximum number of iterations, the positions of the elite individuals in the population whose fitness is less than the preset threshold are updated again.

7. The parameter identification method of a transformer main insulation equivalent model according to claim 5 is characterized in that: The first position update of the candidate solution for the elite individuals in the population whose fitness is less than a preset threshold to obtain the candidate solution after the elite update specifically includes: according to The elite individuals in the population whose fitness is less than the preset threshold are updated for the first time as candidate solutions to obtain the candidate solutions after elite update, where: is the candidate solution after elite update, is the i-th candidate solution of the vector to be solved after the first position update, X i is the i-th candidate solution of the vector to be solved, F i is the fitness of the i-th candidate solution, F i * is the fitness of the i-th candidate solution after the first position update; according to is the i-th candidate solution of the vector to be solved after the first position update, X i is the i-th candidate solution of the vector to be solved, δ is the step size, s is the walking step size, and β is the parameter; according to Among them, μ and v are direction vectors, and β is a parameter.

8. The parameter identification method of a transformer main insulation equivalent model according to claim 7 is characterized in that: The candidate solution after the elite update is added to the candidate solution of the elite individual greater than a preset threshold, and the elements in the candidate solution of the added elite individual are updated for the second time to determine the update value of the candidate solution element, specifically including: according to The elements in the candidate solution of the added elite individuals are updated for the second time to determine the update value of the candidate solution element, where R1 is the random step length of the population distance to the optimal solution; R2 is the random step length of the population distance to the worst solution, x i,j is the jth element in the i-th candidate solution of the added elite individual, and is a random number in [0,1], is the optimal solution in the population, is the worst solution in the population, Update the values ​​for the candidate solution elements.

9. The parameter identification method of a transformer main insulation equivalent model according to claim 8 is characterized in that: The third position update of the candidate solution element update value and the corresponding candidate solution specifically includes: according to Perform a third position update on the candidate solution element update value, where: is the updated value of the candidate solution element after the third position is updated, rand is a random number in [0,1], p j is the value of the jth element of the random elite individual, α is a random number of 1 or 2, F p is the fitness of the random elite individual, F i is the fitness of the i-th elite individual; according to The candidate solution corresponding to the updated value of the candidate solution element is updated for the third time, where: is the value of the third position update of the i-th candidate solution, F i P1 is the fitness of the i-th candidate solution after the third position update.

10. The method for identifying parameters of a transformer main insulation equivalent model according to claim 9, characterized in that: The fourth position updating of the candidate solution element update value after the third position update and the corresponding candidate solution specifically includes: according to The candidate solution element update value after the third position update is updated for the fourth time, where: is the value of the jth element in the i-th candidate solution after the fourth position update, R is a constant, N iter is the current iteration number, is the maximum number of iterations; according to The candidate solution corresponding to the updated value of the candidate solution element after the third position update is updated for the fourth time, where: is the value of the fourth position update of the i-th candidate solution, F i P2 is the fitness of the i-th candidate solution after the fourth position update.

11. A parameter identification system for a transformer main insulation equivalent model, characterized in that: The system comprises: A module for determining the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance, used to determine the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the transformer main insulation according to the admittance of the oil gap part, the stay part and the partition part in the transformer main insulation equivalent model; An objective function determination module for the vector to be solved, used to determine the objective function of the vector to be solved according to the fitting value of the real part of the complex capacitance and the fitting value of the imaginary part of the complex capacitance of the main insulation of the transformer; Definition module, used to define the population size and maximum number of iterations; The parameter identification module is used to update the position of the elite individuals in the population whose fitness is before the preset threshold until the maximum number of iterations is reached, and then select the vector to be solved with the smallest objective function after the position update, and correspond each element in the vector to the parameters to be solved in the transformer main insulation equivalent model, so as to complete the parameter identification of the transformer main insulation equivalent model.

12. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 10.

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