A method, device and medium for optimizing parameters of transformer core vibration model

Abstract

The present invention discloses a method, device, and medium for optimizing transformer core vibration model parameters, belonging to the technical field of electrical equipment fault diagnosis. The method first analyzes the vibration principle of the core under normal operating conditions, and establishes a core vibration acceleration model in combination with Faraday's law of electromagnetic induction. To facilitate optimization and ensure the accuracy of the optimization, sensitivity analysis is used to select optimization parameters, and F and R tests are performed to determine the accuracy of the parameters selected by the sensitivity analysis. After the optimization parameters are selected, a particle swarm algorithm is used to optimize the core vibration acceleration model with the core vibration acceleration model as the objective function, supplemented by optimization design conditions. This provides a design reference for the transformer core design parameters, ensures the optimality of the core vibration acceleration, and reduces the degree of damage to the transformer core.

Description

Technical Field

[0001] The present invention relates to a fault diagnosis technology for electrical equipment, and more particularly to a method, device and medium for optimizing parameters of a transformer core vibration model. Background Art

[0002] Power transformers are a crucial component of power transformation systems, ensuring daily life and even industrial electricity. Failures can disrupt social order and cause significant inconvenience. Vibration in the transformer core causes the core's silicon steel laminations to expand and contract, generating acceleration. This expansion and contraction can damage the core and affect transformer performance. Summary of the Invention

[0003] In view of this, in order to reduce the damage degree of the transformer core, the present invention proposes a method, device and medium for optimizing the parameters of the transformer core vibration model.

[0004] The present invention provides a method, device and medium for optimizing parameters of a transformer core vibration model, comprising: establishing a core vibration acceleration model; selecting optimization parameters of the core vibration acceleration model; verifying the accuracy of the optimization parameters; and optimizing the core vibration acceleration model according to the optimization parameters and preset constraints.

[0005] Preferably, the establishment of the iron core vibration acceleration model includes: obtaining the iron core magnetic induction intensity according to Faraday's law of electromagnetic induction; introducing the iron core magnetization intensity into the iron core magnetic induction intensity to obtain the magnetic field intensity; obtaining the magnetostriction rate of the iron core silicon steel sheet according to the expansion and contraction relationship of the iron core silicon steel sheet under the action of the magnetic field intensity; and obtaining the iron core vibration acceleration model by combining the magnetic field intensity and the magnetostriction rate of the iron core silicon steel sheet.

[0006] Preferably, the selecting of the optimization parameters of the core vibration acceleration model includes: performing a sensitivity analysis on the sensitive data of the core vibration acceleration model based on the collected core data information and its influencing factors to obtain a sensitivity matrix; and solving the sensitivity matrix to obtain the optimization parameters of the core vibration acceleration model.

[0007] Preferably, the checking of the accuracy of the optimization parameters includes: checking the degree of fit of the sensitivity matrix according to the F test method and the R test method.

[0008] Preferably, the R test method is used to test the correlation of the sensitivity matrix, and the F test method is used to test the significance of the sensitivity matrix; if H0:β1=0 is rejected, the regression is significant, it is believed that there is a relationship between the matrices, and the sensitivity matrix is ​​determined to be meaningful; if H0:β1=0 is accepted, the regression is not significant, and the sensitivity matrix is ​​determined to be meaningless.

[0009] Preferably, optimizing the core vibration acceleration model according to the optimization parameters and preset constraints includes: improving a hybrid particle swarm algorithm according to a standard PSO algorithm; and importing the optimization parameters, the preset constraints and the core vibration acceleration model into the improved hybrid particle swarm algorithm for algorithm optimization.

[0010] Preferably, the calculation process of the improved particle swarm optimization algorithm includes:

[0011] Initialization: set algorithm parameters, input raw data, and randomly generate the initial population;

[0012] Calculate the particle fitness function value;

[0013] Particle low-probability crossover and mutation genetic operations;

[0014] Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return the calculated particle fitness function value to start the next iteration optimization;

[0015] Output the optimal solution.

[0016] The present invention provides a device for optimizing parameters of a transformer core vibration model, comprising: a first unit for establishing a core vibration acceleration model; a second unit for selecting optimization parameters of the core vibration acceleration model; a third unit for verifying the accuracy of the optimization parameters; and a fourth unit for optimizing the core vibration acceleration model according to the optimization parameters and preset constraints.

[0017] The present invention provides a computer-readable storage medium, which includes at least one instruction, at least one program, code set or instruction set stored therein, wherein when the instruction, the program, the code set or the instruction set is executed, the device where the computer-readable storage medium is located is controlled to execute a transformer core vibration model parameter optimization method.

[0018] The present invention provides a computer device, comprising a processor, a memory, and at least one instruction, at least one program, a code set, or an instruction set stored in the memory and configured to be executed by the processor. When the processor executes the instruction, the program, the code set, or the instruction set, a method for optimizing the parameters of a transformer core vibration model is implemented.

[0019] Beneficial effects

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] (1) The method of the present invention takes into account the characteristics of the magnetic field source under actual conditions. The magnetic field strength and direction will change after the magnetic field passes through the iron core. Therefore, the iron core magnetization intensity is introduced to make the model more accurate.

[0022] (2) The method of the present invention uses sensitivity analysis to select optimization parameters, and performs F and R tests to determine the accuracy of the parameters selected by the sensitivity analysis, which makes the method more accurate and eliminates the uncertainty that may exist in the algorithm design parameters;

[0023] (3) The method of the present invention adopts an improved hybrid particle swarm algorithm, adopts asynchronous time-varying learning factors and inertia weights to enhance the global search capability of the algorithm, and jointly uses the speed and position adjustment formulas of the continuous and discrete PSO algorithms to solve the optimization problem containing both continuous and discrete variables, overcoming the disadvantage that the standard PSO is prone to falling into local solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of parameter optimization design in the present invention;

[0025] Figure 2 It is a design flow chart of the improved particle swarm algorithm in the present invention;

[0026] Figure 3 is a schematic diagram of a transformer core vibration model parameter optimization method of the present invention;

[0027] Figure 4 It is a schematic diagram of the transformer core vibration model parameter optimization device of the present invention.

[0028] Description of main reference numerals:

[0029] 1. Unit 1; 2. Unit 2; 3. Unit 3; 4. Unit 4. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0032] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0034] See also Figure 1-4 ,In order to reduce the damage degree of transformer core, a transformer core vibration model parameter optimization method is proposed, including:

[0035] S110, establishing an iron core vibration acceleration model;

[0036] S120, selecting optimization parameters of the iron core vibration acceleration model;

[0037] S130, checking the accuracy of the optimization parameters;

[0038] S140 , optimizing the core vibration acceleration model according to the optimization parameters and preset constraints.

[0039] The above step S110, establishing the core vibration acceleration model, specifically includes:

[0040] During the operation of the transformer, the alternating current generated by the alternating voltage flows through the winding, inducing magnetic flux in the iron core. According to the induction law, the magnetic flux generates a magnetic field force. Under the action of the magnetic field force, the silicon steel sheets of the iron core expand and contract, generating acceleration. This expansion and contraction deformation may cause damage to the iron core. Therefore, it is necessary to study the iron core vibration acceleration model and find the design parameters that minimize the iron core damage based on the model, that is, the design parameters that minimize the iron core vibration acceleration.

[0041] Assume that the number of winding turns is N, the alternating voltage on the winding is u=Usinωt, U is the finite value of the alternating voltage, ω is the angular frequency, the cross-sectional area of ​​the transformer core is S, and the magnetic flux Φ generated by the voltage is

[0042]

[0043] Therefore, the magnetic induction intensity B generated in the iron core is:

[0044]

[0045] Where B0 is the magnetic induction intensity under no-load conditions, then:

[0046]

[0047] The magnetic field intensity H can be calculated based on the magnetic induction intensity:

[0048]

[0049] Where u0 is the magnetic permeability in vacuum, u0=4π×10 -7 , M is the core magnetization intensity; in order to simplify the calculation, the traditional method assumes that the core is in a vacuum and does not take into account the actual situation. The method of the present invention takes into account the characteristics of the magnetic field source. The magnetic field intensity and direction will change after the magnetic field passes through the core. Therefore, the core magnetization intensity M is introduced to make the model more accurate.

[0050] Under the action of magnetic field, the expansion and contraction relationship of the core silicon steel sheet is:

[0051]

[0052] Among them, x is the magnetic path length of the iron core, λ1 is the saturation magnetostriction of the iron core silicon steel sheet, H c is the coercive force; then the magnetostriction coefficient λ of the core silicon steel sheet is:

[0053]

[0054] Substituting formula (4) into formula (6) yields:

[0055]

[0056] According to formula (7), the core vibration acceleration can be derived. The core vibration acceleration model is shown in the following formula:

[0057]

[0058] To simplify calculations, traditional methods assume the core is in a vacuum and fail to consider the actual situation. The method proposed in this paper considers the characteristics of the magnetic field source. The magnetic field strength and direction change after passing through the core. Therefore, the core magnetization intensity M is introduced, making the model more accurate.

[0059] Step S120, selecting the optimization parameters of the core vibration acceleration model, specifically including:

[0060] Sensitivity analysis can screen out the main influencing factors from a large number of uncertain factors. The sensitivity of the main influencing factors is proportional to the impact of the results. The higher the sensitivity, the greater the impact, and the more important the influencing factor. Based on the required data information and its influencing factors, sensitivity analysis is performed to calculate the sensitive data of the core vibration acceleration model. The sensitivity matrix is ​​calculated as follows:

[0061]

[0062] In formula (9), F 1i 、F 2i 、F 3i 、F 4i 、F 5i S, N, V, H respectively c The sensitivity of the i-th factor of the five design parameters, λ, and λ1, to the total magnetic flux leakage loss model of the circuit breaker. Each content factor will have a unit minimum change, and the content change matrix is ​​shown in the formula:

[0063]

[0064] ΔR 1i , ΔR 2i , ΔR 3i , ΔR 4i , ΔR 5i S, N, V, H c The change value of the content of the i-th factor within the minimum unit change of the five design parameters λ1, λ2 and λ3. Then the sensitivity matrix solution can be seen as follows:

[0065]

[0066] In formula (11), S1 is the current content change value of all influencing factors, V i is the current value of the influencing factor of the i-th design parameter content, ΔV i is the change value of the influencing factor of the i-th design parameter.

[0067] Step S130, checking the accuracy of the optimization parameters, specifically includes:

[0068] The F and R test methods are used to verify the degree of fit of the sensitivity matrix. The R test method mainly tests the correlation of the sensitivity matrix. The F test method is a significance test of the matrix to assist in verifying the effect. It boils down to testing the hypothesis H0:β1=0; H1:β1≠0. If the hypothesis H0:β1=0 is rejected, the regression is significant, and it is believed that there is a relationship between the matrices, and the sensitivity matrix sought is meaningful; otherwise, the regression is not significant, and the sensitivity matrix is ​​meaningless. Therefore, the F and R tests are usually used as auxiliary test methods to verify the sensitivity matrix.

[0069] The principle of F test is as follows

[0070] When H0 holds true,

[0071]

[0072] Among them, F 1-α (1,n-2) is the variance analysis value, U is the regression sum of squares, Q eis the residual sum of squares, and n is the sample degrees of freedom. The regression sum of squares U can be obtained by the following formula:

[0073]

[0074] is the i-th dependent variable and the mean value of the dependent variable

[0075] Therefore, F>F 1-α (1,n-2), reject H0, otherwise accept H0,

[0076] When H0 holds true,

[0077]

[0078] In the above formula, T is the normality value, W=W core +W W +W i +W c is the variance, is the ratio of sample variances, t(n-2) is the normal degree of freedom, Partial regression coefficient components;

[0079] Therefore Reject H0, otherwise accept H0, where:

[0080]

[0081] Among them, x i 、 is the i-th independent variable and the average value of the independent variable.

[0082] The principle of R test is as follows

[0083]

[0084] In the above formula, r is the multiple correlation coefficient; r 1-α is the critical value of the multiple correlation coefficient, when |r|>r 1-α , reject H0; otherwise, accept H0, where:

[0085]

[0086] Traditional methods have low accuracy and there may be uncertainty in the algorithm design parameters. The method of the present invention uses sensitivity analysis to select optimization parameters and performs F and R tests to determine the accuracy of the parameters selected by sensitivity analysis.

[0087] Step S140, optimizing the core vibration acceleration model according to the optimization parameters and preset constraints, specifically includes:

[0088] The particle swarm algorithm is based on a bird foraging model and introduces the concepts of individual learning and cultural transmission to seek the optimal solution. The method of the present invention adopts an improved hybrid particle swarm algorithm, using asynchronous time-varying learning factors and inertia weights to enhance the algorithm's global search capability.

[0089] The improved hybrid particle swarm optimization algorithm makes the following improvements based on the standard PSO algorithm:

[0090] (1) Combined use of the speed and position adjustment formulas of the continuous and discrete PSO algorithms to solve the optimization problem with both continuous and discrete variables;

[0091] (2) During the iteration process, by comparing the fitness function value and the size of the particle crowding distance, the individual extreme value and the group extreme value are selected to guide the algorithm to find the best solution;

[0092] (3) A method combining asynchronous time-varying learning factors, nonlinear dynamic inertia weights, and small probability crossover and mutation of particles is adopted to overcome the disadvantage that standard PSO is prone to falling into local solutions, and to enhance the global optimization and local search capabilities of the algorithm.

[0093] See also Figure 2 , the calculation process of the improved particle swarm algorithm is as follows:

[0094] S710, Initialization: Set algorithm parameters, input original data, and randomly generate an initial population.

[0095] S720, calculate the particle fitness function value

[0096] S730, particle small probability crossover and mutation genetic operation, that is, updating parameters and particle speed and position according to fitness value.

[0097] S740: Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return to step S720 to start the next iteration optimization search.

[0098] S750: Output the optimal solution and end.

[0099] Traditional design algorithms are overly simplistic and prone to being stuck in local solutions. The proposed method uses an improved hybrid particle swarm optimization algorithm, employing asynchronous time-varying learning factors and inertia weights to enhance the algorithm's global search capabilities. It also combines the speed and position adjustment formulas of continuous and discrete PSO algorithms to solve optimization problems involving both continuous and discrete variables, overcoming the drawback of standard PSO, which is prone to being stuck in local solutions.

[0100] See also Figure 4This embodiment also provides a transformer core vibration model parameter optimization device, including: a first unit, used to establish a core vibration acceleration model; a second unit, used to select optimization parameters of the core vibration acceleration model; a third unit, used to verify the accuracy of the optimization parameters; and a fourth unit, used to optimize the core vibration acceleration model according to the optimization parameters and preset constraints.

[0101] This embodiment also provides a computer-readable storage medium, which includes at least one instruction, at least one program, code set or instruction set stored therein, wherein when the instruction, the program, the code set or the instruction set is executed, the device where the computer-readable storage medium is located is controlled to execute the transformer core vibration model parameter optimization method.

[0102] This embodiment also provides a computer device, including a processor, a memory, and at least one instruction, at least one program, a code set, or an instruction set stored in the memory and configured to be executed by the processor. When the processor executes the instruction, the program, the code set, or the instruction set, a method for optimizing the parameters of the transformer core vibration model is implemented.

[0103] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0104] In the embodiments provided in the present application, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for optimizing transformer core vibration model parameters, characterized in that: include: Establish the core vibration acceleration model; The optimization parameters of the core vibration acceleration model are selected, wherein the core vibration acceleration model is shown in the following formula: ； Where λ is the magnetostriction of the core silicon steel sheet, λ1 is the saturation magnetostriction of the core silicon steel sheet, u0 is the magnetic permeability in vacuum, M is the core magnetization intensity, ω is the angular frequency, and S is the cross-sectional area of ​​the transformer core; Checking the accuracy of the optimized parameters; Optimizing the core vibration acceleration model according to the optimization parameters and preset constraints; The establishing of the iron core vibration acceleration model comprises: According to Faraday's law of electromagnetic induction, the magnetic induction intensity of the iron core is obtained; Introducing the iron core magnetization intensity into the iron core magnetic induction intensity to obtain the magnetic field intensity; Obtaining a magnetostriction coefficient of the core silicon steel sheet according to a contraction relationship of the core silicon steel sheet under the action of the magnetic field strength; The core vibration acceleration model is obtained by combining the magnetic field strength and the magnetostriction coefficient of the core silicon steel sheet.

2. The method for optimizing transformer core vibration model parameters according to claim 1, wherein: The selecting of the optimization parameters of the core vibration acceleration model includes: Based on the collected core data information and its influencing factors, sensitivity analysis is performed on the sensitive data of the core vibration acceleration model to obtain a sensitivity matrix; The sensitivity matrix is ​​solved to obtain the optimized parameters of the iron core vibration acceleration model.

3. The method for optimizing transformer core vibration model parameters according to claim 2, wherein: The checking of the accuracy of the optimization parameters comprises: The degree of fit of the sensitivity matrix was tested using the F test and the R test.

4. The method for optimizing transformer core vibration model parameters according to claim 3, wherein: The R test method is to test the correlation of the sensitivity matrix, and the F test method is to test the significance of the sensitivity matrix; like If it is rejected, the regression is significant, and it is believed that there is a relationship between the matrices, and the sensitivity matrix is ​​considered meaningful; like If it is accepted, the regression is not significant and the sensitivity matrix is ​​deemed meaningless.

5. The method for optimizing transformer core vibration model parameters according to claim 4, wherein: Optimizing the core vibration acceleration model according to the optimization parameters and preset constraints includes: Improve the hybrid particle swarm optimization algorithm based on the standard PSO algorithm; The optimization parameters, the preset constraints and the core vibration acceleration model are introduced into the improved hybrid particle swarm algorithm for algorithm optimization.

6. The method for optimizing transformer core vibration model parameters according to claim 5, characterized in that: The improved calculation process of the particle swarm algorithm includes: Initialization: set algorithm parameters, input raw data, and randomly generate the initial population; Calculate the particle fitness function value; Particle low-probability crossover and mutation genetic operations; Determine whether the iteration termination condition is met. If the iteration termination condition is not met, return the calculated particle fitness function value to start the next iteration optimization; Output the optimal solution.

7. A transformer core vibration model parameter optimization device, applying the transformer core vibration model parameter optimization method according to any one of claims 1 to 6, characterized in that: include: The first unit is used to establish the core vibration acceleration model; The second unit is used to select the optimization parameters of the iron core vibration acceleration model; The third unit is used to test the accuracy of the optimization parameters; The fourth unit is used to optimize the core vibration acceleration model according to the optimization parameters and preset constraints.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes at least one instruction, at least one program, code set or instruction set stored therein, wherein when the instruction, the program, the code set or the instruction set is executed, the device where the computer-readable storage medium is located is controlled to execute the transformer core vibration model parameter optimization method according to any one of claims 1 to 6.

9. A computer device, characterized in that: The method comprises a processor, a memory, and at least one instruction, at least one program, a code set, or an instruction set stored in the memory and configured to be executed by the processor, wherein when the processor executes the instruction, the program, the code set, or the instruction set, the method for optimizing the transformer core vibration model parameters according to any one of claims 1 to 6 is implemented.

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