Conductor structure parameter identification method and device, electronic equipment and storage medium

Abstract

The invention discloses a conductor structure parameter identification method and device, electronic equipment and a storage medium, which are used for solving the technical problem that a model error is difficult to correct due to the fact that a winding structure cannot be directly observed in the operation process of a transformer. The method comprises the following steps: constructing a transformer magnetic flux leakage analytical calculation model; collecting the input current of each conductor in the transformer, and recording the actually measured magnetic field value at the preset measuring point under the same time step length; substituting the input current into the transformer magnetic flux leakage analytical calculation model, and predicting a preset measurement point magnetic field within the sampling time to obtain a predicted magnetic field value; constructing a cost function by taking a mean square error of the actually measured magnetic field value and the predicted magnetic field value as an optimization target; setting three types of rigid constraints, and solving a cost function by adopting a genetic algorithm to obtain an initial structure parameter approximate solution of the conductor; and taking the approximate solution of the initial structure parameter as an initial point, and solving a cost function by adopting a gradient constraint optimization algorithm based on an SQP method to obtain an optimal solution of the conductor structure parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of conductor structure, and particularly relates to a conductor structure parameter identification method and device, electronic equipment and storage medium. BACKGROUND

[0002] With the increasing requirements of intelligentization and high reliability operation of power systems, simulation modeling and operation state perception of key electrical equipment such as transformers have become the research focus. Among them, the finite element method has been widely used for fine modeling and response prediction of transformer electromagnetic field, thermal field and force field. However, in actual engineering, the finite element simulation model often has a certain degree of deviation from the actual equipment. Traditional finite element modeling is mostly based on design drawings or measurement methods to construct the internal winding structure of the transformer, but these methods have two key problems: first, the winding structure will evolve slowly after the equipment is running under the action of thermal-electric-force multi-field coupling, such as small displacement under the action of electric force, winding settlement, thermal expansion or local deformation caused by inter-turn short circuit; second, some key structures are difficult to accurately measure by external means after the equipment is packaged, which leads to the initial structure model relied on by simulation cannot represent the current real state of the equipment, affecting the calculation accuracy. Therefore, an identification method combining operation process data and inverting conductor structure parameters is urgently needed to realize dynamic reconstruction and accuracy correction of the simulation model.

[0003] In addition, in recent years, with the upgrading of magnetic measurement sensors, some new relay protection strategies have begun to introduce magnetic field quantities as criterion quantities for research, such as using magnetic flux leakage jump, magnetic field symmetry distribution characteristics, etc. to assist in identifying internal fault location or type. However, the application of magnetic characteristic quantities in protection algorithms still has problems such as difficulty in setting threshold value and unclear response sensitivity, and the fundamental reason is that the magnetic flux leakage distribution is highly dependent on the conductor structure form and arrangement. When the model parameters are inaccurate, the feature extraction and setting accuracy of the protection algorithm will be significantly affected. Therefore, accurately identifying the winding structure parameters and constructing a magnetic field analysis model matching the actual working condition are of great significance for improving the reliability and practicality of the magnetic quantity protection algorithm.

[0004] In recent years, transformer digital twin model construction and evolution prediction has gradually become an important direction of transformer state monitoring research. The transformer digital twin usually includes three key modules of geometric structure modeling, electromagnetic physical modeling, state estimation and simulation prediction, and the core goal is to realize the high-fidelity mapping and collaborative evolution of the virtual model to the real equipment, so as to realize the real-time perception of the equipment running state, the trend prediction of the potential fault and the optimization adjustment of the operation strategy. In this system, in order to improve the description ability of the twin model to the real state of the equipment, the research in recent years has gradually focused on model correction and structure reconstruction. Existing researches propose to perform parameter inversion through data-driven methods such as end measurement of electrical quantities, and then correct the equivalent circuit model or finite element model; there are also researches to identify the distributed parameters of the transformer by injecting high-frequency signals in the running state to realize dynamic updating of the model; in addition, using neural network and other deep learning methods to directly fit the magnetic field or temperature distribution from the measured data is also a frontier direction in current digital twin modeling. These researches are all committed to model parameter inversion and structure reconstruction based on various measurement data, realizing the fusion and optimization of data-driven and physical modeling.

[0005] Although the existing transformer digital twin modeling technology has achieved certain results, there are still many limitations in structure identification and model reconstruction. First, the method of parameter inversion based on end voltage or current is mostly only applicable to macro equivalent circuit model, and it is difficult to accurately represent the changes of conductor space structure and local parameters, and lacks the identification ability of real geometric shape. Second, the method relying on high-frequency injection signal for identification has high requirements for the application environment, has problems of additional signal interference, poor compatibility with power system, and complex injection signal design, which is difficult to popularize to routine detection under various operating states. In addition, data-driven methods such as neural network have strong non-linear fitting ability, but the model has poor interpretability and limited generalization ability, especially when the training data is insufficient or lacks physical boundary constraints, which can easily lead to distorted identification results or physical unreasonable results. At the same time, such methods have difficulty in meeting the requirements of engineering application in terms of identification accuracy and stability when facing scenes with slow structure evolution and weak feature difference. SUMMARY

[0006] The application provides a conductor structure parameter identification method and device, electronic equipment and storage medium, which are used to solve the technical problem that the winding structure cannot be directly observed during the operation of the transformer, resulting in difficulty in correcting the model error.

[0007] The application provides a conductor structure parameter identification method, which comprises the following steps:

[0008] A transformer leakage magnetic analytical calculation model is constructed;

[0009] An input current of each conductor in the transformer is collected, and a measured magnetic field value at a preset measuring point under a same time step is recorded;

[0010] The input current is substituted into the transformer leakage magnetic field analytical calculation model, a preset measuring point magnetic field is predicted within a sampling time, and a predicted magnetic field value is obtained;

[0011] A cost function is constructed with a mean square error of the measured magnetic field value and the predicted magnetic field value as an optimization target;

[0012] Three types of rigid constraints are set, and a genetic algorithm is used to solve the cost function, so that an initial structure parameter approximate solution of the conductor is obtained;

[0013] The initial structure parameter approximate solution is taken as an initial point, a gradient type constraint optimization algorithm based on an SQP method is used to solve the cost function, and an optimal solution of a conductor structure parameter is obtained.

[0014] Optionally, the step of constructing the transformer leakage magnetic field analytical calculation model comprises:

[0015] An instantaneous current of each conductor is obtained;

[0016] A coupling coefficient between a conductor current and a magnetic field is calculated;

[0017] The transformer leakage magnetic field analytical calculation model is constructed by using the instantaneous current and the coupling coefficient.

[0018] Optionally, the cost function is:

[0019]

[0020] wherein, the measured magnetic field value is, the predicted magnetic field value is, a sequence length of an input current of each conductor is, a total parameter vector of an unknown position parameter of the conductor is, a k-th sampling time is.

[0021] Optionally, the step of solving the cost function by using the genetic algorithm to obtain the initial structure parameter approximate solution of the conductor comprises:

[0022] An initial population is generated, and the initial population comprises a plurality of individuals;

[0023] A fitness function is generated;

[0024] A fitness of each individual is calculated according to the fitness function;

[0025] The optimal individual is selected according to the fitness for the next iteration until the variation of the best fitness in continuous preset iteration times is within a preset range, and the current optimal individual is output as an initial structure parameter approximate solution of the conductor.

[0026] The application further provides a conductor structure parameter identification device, comprising:

[0027] A transformer leakage magnetic field analytical calculation model construction module is configured to construct a transformer leakage magnetic field analytical calculation model.

[0028] A measured magnetic field value recording module is configured to collect input currents of each conductor in the transformer and record measured magnetic field values at preset measuring points under the same time step.

[0029] A predicted magnetic field value generation module is configured to substitute the input currents into the transformer leakage magnetic field analytical calculation model, predict magnetic fields at the preset measuring points within a sampling time, and obtain predicted magnetic field values.

[0030] A cost function construction module is configured to construct a cost function with the mean square error of the measured magnetic field values and the predicted magnetic field values as an optimization target.

[0031] An initial structure parameter approximate solution generation module is configured to set three types of rigid constraints and solve the cost function by using a genetic algorithm to obtain an initial structure parameter approximate solution of the conductor.

[0032] A conductor structure parameter optimal solution solving module is configured to take the initial structure parameter approximate solution as an initial point, solve the cost function by using a gradient-type constraint optimization algorithm based on an SQP method, and obtain a conductor structure parameter optimal solution.

[0033] Optionally, the transformer leakage magnetic field analytical calculation model construction module comprises:

[0034] A transient current acquisition submodule is configured to acquire transient currents of the conductors.

[0035] A coupling coefficient calculation submodule is configured to calculate coupling coefficients between conductor currents and magnetic fields.

[0036] A transformer leakage magnetic field analytical calculation model construction submodule is configured to construct a transformer leakage magnetic field analytical calculation model by using the transient currents and the coupling coefficients.

[0037] Optionally, the cost function is:

[0038]

[0039] wherein, the measured magnetic field value is, the predicted magnetic field value is, the sequence length of the input current of each conductor is, a total parameter vector of unknown position parameters of the conductor, is the kth sampling time.

[0040] Optionally, the initial structure parameter approximate solution generation module comprises:

[0041] An initial population generation submodule is configured to generate an initial population, wherein the initial population comprises a plurality of individuals.

[0042] An adaptability function generation submodule is configured to generate an adaptability function.

[0043] An adaptability calculation submodule is configured to calculate the adaptability of each individual according to the adaptability function.

[0044] An initial structure parameter approximate solution output submodule is configured to select the optimal individual according to the adaptability for the next iteration until the variation of the optimal adaptability in the continuous preset number of iterations is within the preset range, and output the current optimal individual as the initial structure parameter approximate solution of the conductor.

[0045] The application further provides an electronic device, which comprises a processor and a memory:

[0046] The memory is configured to store program code and transmit the program code to the processor.

[0047] The processor is configured to execute the conductor structure parameter identification method according to the instructions in the program code.

[0048] The application further provides a computer readable storage medium, which is configured to store program code, and the program code is configured to execute the conductor structure parameter identification method.

[0049] As can be seen from the above technical solutions, the application has the following advantages: the application provides a conductor structure parameter identification method, and specifically discloses the following: a transformer leakage magnetic field analytical calculation model is constructed; the input current of each conductor in the transformer is collected, and the measured magnetic field value at the same time step is recorded at a preset measuring point; the input current is substituted into the transformer leakage magnetic field analytical calculation model, the magnetic field at the preset measuring point is predicted within the sampling time, and the predicted magnetic field value is obtained; the mean square error of the measured magnetic field value and the predicted magnetic field value is taken as an optimization target, and a cost function is constructed; three types of rigid constraints are set, and a genetic algorithm is used to solve the cost function, so that the initial structure parameter approximate solution of the conductor is obtained; the initial structure parameter approximate solution is taken as an initial point, a gradient type constraint optimization algorithm based on the SQP method is used to solve the cost function, and the optimal solution of the conductor structure parameter is obtained.

[0050] The application adopts a magnetic leakage field analytical expression to accurately establish a nonlinear mapping relationship between a conductor boundary parameter and a magnetic field, under the condition of known current and magnetic field measurement data, a two-dimensional boundary structure parameter of the conductor is taken as an optimization variable, a full-time domain objective function is constructed, and high-precision inversion modeling of the conductor geometric parameter is realized. Then, the genetic algorithm is used to realize global coarse search of the parameters, avoid local minimum trap, and then introduce a soft penalty mechanism and a time weighting mechanism, combine SQP to realize local fine optimization, and enhance the convergence ability and precision through Gaussian disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0052] Figure 1 A step flow chart of a conductor structure parameter identification method provided by the embodiment of the present application is provided.

[0053] Figure 2 A schematic diagram of a transformer magnetic leakage analytical calculation model construction process is provided.

[0054] Figure 3 A schematic diagram of a conductor initial structure parameter approximate solution calculation process is provided.

[0055] Figure 4 A schematic diagram of a process of local fine identification based on a gradient type constraint optimization algorithm of an SQP method is provided.

[0056] Figure 5 A structure block diagram of a conductor structure parameter identification device provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0057] The embodiment of the present application provides a conductor structure parameter identification method, device, electronic equipment and storage medium, which is used for solving the technical problem that the winding structure cannot be directly observed in the operation process of the transformer, and the model error is difficult to correct.

[0058] In order to make the application purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0059] Please refer to Figure 1 , Figure 1 A step flow chart of a conductor structure parameter identification method provided by the embodiment of the application.

[0060] The application provides a conductor structure parameter identification method, which comprises the following steps:

[0061] Step 101, constructing a transformer leakage magnetic field analytical calculation model;

[0062] In the embodiment of the application, the transformer leakage magnetic field analytical calculation model is used to characterize the radial leakage magnetic field at the measuring point. The step of constructing the transformer leakage magnetic field analytical calculation model can comprise the following steps:

[0063] S11, obtaining the instantaneous current of each conductor;

[0064] S12, calculating the coupling coefficient between the conductor current and the magnetic field;

[0065] S13, constructing the transformer leakage magnetic field analytical calculation model by using the instantaneous current and the coupling coefficient.

[0066] In the specific implementation, as shown in the following table, the transformer leakage magnetic field analytical calculation model is constructed as follows: Figure 2

[0067] Under the two-dimensional static magnetic approximation condition, the calculation domain is positioned in the rectangular core window region with a height of h and a width of b. The conductor extends along the z direction, and the cross section in the xy plane is rectangular. Each conductor i is determined by the left lower corner coordinate and the right upper corner coordinate The internal current density distribution is uniform. The conductor position parameters are unknown variables to be identified. The core permeability is , and the radial leakage magnetic field at the measuring point is the transformer leakage magnetic field analytical calculation model, which is expressed by the Fourier superposition contribution expression of all conductors as follows:

[0068] (1)

[0069] Wherein, is the instantaneous current of the i th conductor; q is the total number of conductors; is the coupling coefficient between the current and the magnetic field, which has the following structure:

[0070] (2)

[0071] Wherein, , , k takes 1, 2,..., ∞, j takes 0, 1, 2,..., ∞, ​The coefficient of the conductor current density function after double Fourier series decomposition corresponds to the following:

[0072] (3)

[0073] When j is 0, there is a denominator of 0 in the solving formula, which is processed according to the mathematical limit.

[0074] Step 102, the input current of each conductor in the transformer is collected, and the measured magnetic field value at the preset measuring point under the same time step is recorded;

[0075] In the embodiment of the application, the input current of each conductor can be collected , wherein, is the kth sampling time, and the sequence length is . At the same time, the measured magnetic field value at the fixed measuring point under the same time step is recorded.

[0076] Step 103, the input current is substituted into the transformer leakage magnetic field analytical calculation model, and the magnetic field at the preset measuring point is predicted within the sampling time to obtain the predicted magnetic field value;

[0077] In the embodiment of the application, it is assumed that the number of conductors is q, as shown in Figure 2 , and the position parameter set of each conductor is , then the total parameter vector composed of the unknown position parameters of the q conductors is :

[0078] (4)

[0079] The input current and the total parameter vector are input into formula (1), and the measuring point magnetic field is predicted within the sampling time, and the predicted magnetic field value :

[0080] (5)

[0081] Step 104, taking the mean square error of the measured magnetic field value and the predicted magnetic field value as the optimization target, a cost function is constructed;

[0082] In the embodiment of the application, the mean square error between the measured magnetic field sequence and the predicted magnetic field is taken as the optimization objective function, and the cost function constructed is as follows:

[0083] (6)

[0084] The optimization problem is to minimize the function under the constraint condition.

[0085] Step 105, setting three types of rigid constraints, and solving the cost function by using a genetic algorithm to obtain an initial structure parameter approximate solution of the conductor;

[0086] In the embodiment of the present application, in order to ensure physical realizability, the following three types of rigid constraints can be introduced:

[0087] 1) Conductor size constraint: 、 ;

[0088] 2) All conductors are located in the safe area in the domain to avoid edge sticking: 、 , is a very small constant value, which can be set by the user.

[0089] 3) All conductors do not overlap each other constraint, that is, for any i≠l, the boundary rectangle of conductor i and l does not intersect: or or or .

[0090] All constraint forms can be converted into inequalities and connected into the optimizer for strict control.

[0091] After setting the three types of rigid constraints, the genetic algorithm can be used to solve the cost function to obtain an initial structure parameter approximate solution of the conductor. Specifically, it can include the following steps:

[0092] S51, generating an initial population, the initial population containing several individuals;

[0093] S52, generating a fitness function;

[0094] S53, calculating the fitness of each individual according to the fitness function;

[0095] S54, selecting the optimal individual according to the fitness for the next iteration until the change of the best fitness in the consecutive preset number of iterations is within the preset range, and inputting the current optimal individual as the initial structure parameter approximate solution of the conductor.

[0096] In a specific implementation, the process of solving the cost function by the genetic algorithm to obtain the initial structure parameter approximate solution of the conductor is as shown in Figure 3 , and specifically includes:

[0097] Coding mode setting: corresponding to the total parameter vector of the boundary of q conductors , using real number coding mode, each individual is vector, avoiding binary coding precision problem, adapting to floating point optimization.

[0098] Initial population generation: setting the population size When initializing each individual, the parameters of each conductor are generated according to the following rules:

[0099] lateral coordinate , where ;

[0100] longitudinal coordinate , where .

[0101] where, is the maximum minimum width of each conductor, is the maximum minimum height of each conductor.

[0102] The generation is repeated until all the constraints of no intersection and no edge sticking between conductors are satisfied. This step ensures that all the individuals initially satisfy the constraints, improving the convergence quality.

[0103] Fitness function definition: the fitness function adopts the negative value of the cost function , so that the higher the fitness, the smaller the error:

[0104] (7)

[0105] In each round of iteration, all individuals are substituted into equation (1) to calculate the predicted magnetic field value, and the error is calculated point by point to smooth the mean square error.

[0106] Then, based on the fitness value of each individual, the initial population is selected, crossed, and mutated:

[0107] Selection: tournament selection is adopted, a subset is randomly selected from the population, and the individual with the best fitness is reserved for crossing;

[0108] Crossing: simulated binary crossover is used, two offspring are generated for each pair of individuals, and the crossover probability is set to ;

[0109] Mutation: Gaussian mutation is adopted, a normal disturbance with a mean of 0 and a standard deviation of is added to each parameter, and the mutation probability .

[0110] Elite reservation and constraint repair: the top individuals with the best fitness in each generation are reserved for the next generation. If the individual generated by crossing or mutation does not satisfy the constraints (such as conductor overlap, edge sticking), projection repair is immediately performed to adjust the parameters to the nearest legal boundary, or the individual is discarded and a new individual is generated.

[0111] Convergence criterion and output: the algorithm is terminated when any of the following conditions is met: the best fitness is continuous No significant improvement in the middle; reach the maximum iteration number . Output the optimal individual parameters , i.e. the initial structure parameter approximation solution of the conductor as the initial point for the next stage optimization.

[0112] Step 106, using the gradient type constraint optimization algorithm based on SQP method to solve the cost function with the initial structure parameter approximation solution as the initial point, to obtain the optimal solution of the conductor structure parameter.

[0113] After obtaining the initial structure parameter approximation solution by genetic algorithm, in order to further improve the accuracy and feasibility of the conductor boundary parameter, the gradient type constraint optimization algorithm based on SQP method is used for local fine identification. This method has good convergence performance and can quickly approach the local minimum point under the continuous derivable objective function and nonlinear constraint. In this step, in order to improve the response ability of the model to the structure constraint and time sequence dynamic, a number of algorithm innovation strategies are introduced, such as the systematic improvement of the objective function design, variable disturbance control and error weight distribution, as shown in Figure 4 .

[0114] Soft constraint penalty function construction: traditional optimization algorithm usually inputs the physical structure condition in the form of hard constraint to the optimizer, but due to the non-crossing condition between conductors is essentially a non-convex logical judgment, which is not conducive to gradient promotion. Therefore, the structure feasibility condition is introduced into the objective function body in the form of penalty function, which allows the temporarily infeasible solution to enter the search space, and at the same time uses the gradient mechanism to adaptively repair the structure error. The penalty objective function is constructed as follows:

[0115] (8)

[0116] Among them, is the weighted area sum of all conductor overlapping regions, is used to measure the penalty degree of the conductor near the boundary, , is the adjustment coefficient. The above expression can ensure that even if the current solution does not satisfy all the structure restrictions, it can still be guided away from the illegal area by increasing the cost.

[0117] Time weighted error design: in order to enhance the identification ability of the optimization to the dynamic behavior of the current excitation, the time weighted error mechanism based on the derivative of the current is introduced. According to experience, the magnetic field is more sensitive to the structure of the conductor at the place where the current changes sharply, so it should be given higher error weight. Define the time weighted factor:

[0118] (9)

[0119] Among them, The sum of the currents flowing through all the non-series conductors. Embed the timing weighting factor into the cost function:

[0120] (10)

[0121] Through this mechanism, the conductor structure resolution capability of the model at the key excitation moment can be improved.

[0122] Lagrangian function construction: initial point is the output result of genetic algorithm , set the iteration tolerance and the maximum step number . Construct the Lagrangian function:

[0123] (11)

[0124] where, is the wth constraint function, and the corresponding Lagrange multiplier.

[0125] Approximate the original problem (i.e. the penalty objective function) as a local quadratic programming problem, and the quadratic optimization subproblem at the current iteration point is:

[0126] (12)

[0127] Satisfy:

[0128] (13)

[0129] In the formula, d is the current iteration step size; is the first-order gradient of the penalty objective function at the current point; is the Hessian matrix approximation of the constructed Lagrangian function to the variable; is the first-order derivative of the constraint function, indicating the linearization direction at the current point.

[0130] Solve the current search direction , perform line search along the direction , perform step size control, select to make the objective function sufficiently descend, and update:

[0131] (14)

[0132] An adaptive perturbation mechanism is introduced to improve the fine-tuning ability. To solve the problem that some conductor boundary variables are not sensitive in the error function, the variable gradient response is dynamically evaluated in each iteration, and a small amplitude perturbation is applied to the low sensitivity variables:

[0133] (15)

[0134] wherein, According to the latest several rounds of target function change rate reverse setting, the response is low, and the disturbance is strong, which prevents the optimization from falling into a local flat area. The disturbance is inserted between gradient descent and line search, effectively improving global convergence and parameter identification resolution.

[0135] Convergence criterion and solution output. The iteration termination condition is set as: the target function drop amplitude in any round is lower than the threshold ; or the search direction norm is lower than ; or the maximum iteration number is reached. The final output is the optimal solution of the conductor structure parameter .

[0136] The nonlinear mapping relationship between the conductor boundary parameter and the magnetic field is accurately established by using the analytical expression of the magnetic leakage field. Under the condition of known current and magnetic field measurement data, the two-dimensional boundary structure parameters of the conductor are taken as optimization variables, a full-time domain objective function is constructed, and high-precision inversion modeling of the conductor geometric parameters is realized. Then, the genetic algorithm is used to realize global coarse search of the parameters, avoid local minimum trap, and then introduce a soft penalty mechanism and a time weighting mechanism, combine SQP to realize local fine optimization, and enhance the convergence ability and precision through Gaussian disturbance.

[0137] Please refer to Figure 5 , Figure 5 The structural block diagram of a conductor structure parameter identification device provided by the embodiment of the present application.

[0138] The embodiment of the present application provides a conductor structure parameter identification device, which comprises:

[0139] The transformer magnetic leakage analytical calculation model construction module 501 is used for constructing a transformer magnetic leakage analytical calculation model.

[0140] The measured magnetic field value recording module 502 is used for collecting the input current of each conductor in the transformer, and recording the measured magnetic field value under the same time step at the preset measurement point.

[0141] The predicted magnetic field value generation module 503 is used for substituting the input current into the transformer magnetic leakage analytical calculation model, predicting the magnetic field of the preset measurement point within the sampling time, and obtaining the predicted magnetic field value.

[0142] The cost function construction module 504 is used for taking the mean square error of the measured magnetic field value and the predicted magnetic field value as an optimization target, and constructing a cost function.

[0143] The initial structure parameter approximate solution generation module 505 is used for setting three types of rigid constraints, and solving the cost function by using a genetic algorithm to obtain an initial structure parameter approximate solution of the conductor.

[0144] The conductor structure parameter optimal solution solving module 506 is configured to take the initial structure parameter approximate solution as an initial point, and solve the cost function by using a gradient type constraint optimization algorithm based on an SQP method to obtain the conductor structure parameter optimal solution.

[0145] In the embodiment of the present application, the transformer leakage magnetic field analytical calculation model construction module 501 comprises:

[0146] The instantaneous current acquisition submodule is configured to acquire the instantaneous current of each conductor.

[0147] The coupling coefficient calculation submodule is configured to calculate the coupling coefficient between the conductor current and the magnetic field.

[0148] The transformer leakage magnetic field analytical calculation model construction submodule is configured to construct the transformer leakage magnetic field analytical calculation model by using the instantaneous current and the coupling coefficient.

[0149] In the embodiment of the present application, the cost function is as follows:

[0150]

[0151] wherein, is the measured magnetic field value, is the predicted magnetic field value, is the sequence length of the input current of each conductor, is the total parameter vector of the unknown position parameters of the conductor, is the kth sampling time.

[0152] In the embodiment of the present application, the initial structure parameter approximate solution generation module 505 comprises:

[0153] The initial population generation submodule is configured to generate an initial population, and the initial population comprises a plurality of individuals.

[0154] The fitness function generation submodule is configured to generate a fitness function.

[0155] The fitness calculation submodule is configured to calculate the fitness of each individual according to the fitness function.

[0156] The initial structure parameter approximate solution output submodule is configured to select the optimal individual according to the fitness for the next iteration, until the change of the best fitness in the continuous preset number of iterations is within a preset range, and output the current optimal individual as the initial structure parameter approximate solution of the conductor.

[0157] The embodiment of the present application further provides an electronic device, which comprises a processor and a memory:

[0158] The memory is configured to store program code and transmit the program code to the processor.

[0159] The processor is configured to execute the conductor structure parameter identification method according to the instructions in the program code.

[0160] The embodiment of the present application also provides a computer readable storage medium, which is used for storing program code, and the program code is used for executing the conductor structure parameter identification method.

[0161] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-mentioned system, device and unit can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0162] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts between each embodiment can be referred to each other.

[0163] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, device or computer program product. Therefore, the embodiments of the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application 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 codes.

[0164] The embodiments of the present application are described with reference to flowcharts and / or block diagrams according to the method, terminal device (system) and computer program product of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be realized by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing terminal device to produce a machine, so that the instructions executed by the computer or other programmable data processing terminal device produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0165] These computer program instructions can also be stored in a computer readable storage medium which can guide the computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks. Figure 1 The device for implementing the functions specified in one or more flows and / or blocks.

[0166] These computer program instructions can also be loaded into computer or other programmable data processing devices to cause a series of operational steps to be performed on the computer or other programmable devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable devices provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0167] Although the preferred embodiments of the present application have been described, those skilled in the art will understand that there are many modifications and alterations to the described embodiments. Accordingly, the appended claims are intended to encompass all such modifications and alterations as falling within the true spirit and scope of the present application.

[0168] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0169] Finally, it should be noted that in this document, relational terms such as first and second and the like can only be used to distinguish one entity or action from another entity or action, without necessarily requiring or implying that these entities or actions are in any way actually related or ordered in time. The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element preceded by "comprises a" does not, without more constraints, foreclose the existence of additional identical elements in the process, method, article, or apparatus that comprises the recited element.

[0170] The above-described embodiments are merely intended to illustrate the technical solutions of the present application, but not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying conductor structure parameters, characterized in that: include: Construct a transformer leakage magnetic analysis calculation model; Collect the input current of each conductor in the transformer and record the measured magnetic field value at the same time step at the preset measuring point; Substituting the input current into the transformer magnetic leakage analytical calculation model, predicting the magnetic field at the preset measuring point within the sampling time to obtain a predicted magnetic field value; Constructing a cost function with the mean square error between the measured magnetic field value and the predicted magnetic field value as an optimization target; Three types of rigid constraints are set, and a genetic algorithm is used to solve a cost function to obtain an approximate solution of the initial structural parameters of the conductor; Taking the approximate solution of the initial structural parameters as the starting point, a gradient-type constrained optimization algorithm based on the SQP method is adopted to solve the cost function to obtain the optimal solution of the conductor structural parameters.

2. The method according to claim 1, characterized in that The step of constructing a transformer magnetic leakage analytical calculation model includes: Obtain the instantaneous current of each conductor; Calculate the coupling coefficient between the conductor current and the magnetic field; The instantaneous current and the coupling coefficient are used to construct a transformer leakage magnetic analysis calculation model.

3. The method according to claim 1, characterized in that The cost function is: in, is the measured magnetic field value, To predict the magnetic field value, Enter the sequence length of the current for each conductor, is the total parameter vector of the unknown position parameters of the conductor, is the kth sampling moment.

4. The method according to claim 1, wherein The step of using a genetic algorithm to solve the cost function to obtain an approximate solution for the initial structural parameters of the conductor includes: generating an initial population, wherein the initial population includes a plurality of individuals; Generate fitness function; Calculating the fitness of each individual according to the fitness function; The optimal individual is selected according to the fitness to perform the next iteration until the change of the optimal fitness in the consecutive preset number of iterations is within a preset range, and the current optimal individual is output as the initial structural parameter approximate solution of the conductor.

5. A conductor structure parameter identification device, characterized in that: include: Transformer magnetic leakage analytical calculation model construction module, used to construct transformer magnetic leakage analytical calculation model; The measured magnetic field value recording module is used to collect the input current of each conductor in the transformer and record the measured magnetic field value under the same time step at the preset measuring point; A predicted magnetic field value generation module is used to substitute the input current into the transformer leakage magnetic analysis calculation model, predict the magnetic field of the preset measuring point within the sampling time, and obtain a predicted magnetic field value; A cost function construction module is used to construct a cost function with the mean square error between the measured magnetic field value and the predicted magnetic field value as an optimization target; An initial structural parameter approximate solution generation module is used to set three types of rigid constraints and use a genetic algorithm to solve a cost function to obtain an initial structural parameter approximate solution of the conductor; The conductor structural parameter optimal solution solving module is used to solve the cost function using the initial structural parameter approximate solution as the starting point and adopt a gradient constrained optimization algorithm based on the SQP method to obtain the optimal solution of the conductor structural parameters.

6. The device according to claim 5, characterized in that The transformer magnetic leakage analytical calculation model construction module includes: The instantaneous current acquisition submodule is used to obtain the instantaneous current of each conductor; A coupling coefficient calculation submodule is used to calculate the coupling coefficient between the conductor current and the magnetic field; The transformer leakage magnetic analysis calculation model construction submodule is used to construct a transformer leakage magnetic analysis calculation model using the instantaneous current and the coupling coefficient.

7. The device according to claim 5, characterized in that The cost function is: in, is the measured magnetic field value, To predict the magnetic field value, Enter the sequence length of the current for each conductor, is the total parameter vector of the unknown position parameters of the conductor, is the kth sampling moment.

8. The device according to claim 5, characterized in that The initial structural parameter approximate solution generation module includes: An initial population generation submodule, used to generate an initial population, wherein the initial population includes a number of individuals; Fitness function generation submodule, used to generate fitness function; A fitness calculation submodule, used to calculate the fitness of each individual according to the fitness function; The initial structural parameter approximate solution output submodule is used to select the optimal individual according to the fitness for the next iteration until the change of the optimal fitness in the consecutive preset number of iterations is within a preset range, and output the current optimal individual as the initial structural parameter approximate solution of the conductor.

9. An electronic device, characterized in that: The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the conductor structure parameter identification method according to any one of claims 1 to 4 according to instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program codes, and the program codes are used to execute the conductor structure parameter identification method according to any one of claims 1 to 4.

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