Oil paper insulation Dissado-Hill model parameter identification method based on improved particle swarm optimization

Abstract

The invention relates to an oil paper insulation Dissado-Hill model parameter identification method based on an improved particle swarm algorithm. The method comprises the following steps: constructing a Dissado-Hill model to represent a dispersion relaxation process of oil paper insulation dielectric response; the method comprises the following steps: separating conductivity and relaxation characteristic information through a broadband dielectric response analysis method based on Kramers-Kronig transformation, and constructing an oil paper insulation broadband dielectric response D-H equivalent circuit model; preparing a sample, and performing frequency domain dielectric spectrum test to obtain a complex capacitance real part spectral line and a complex capacitance imaginary part spectral line corresponding to the oil paper insulation sample; constructing an optimization objective function; solving and optimizing the objective function by adopting an improved particle swarm algorithm, adding chaotic mapping on the basis of the original particle swarm algorithm, and generating a random number sequence by using the chaotic mapping to replace a pseudo random number sequence of the original particle swarm algorithm; according to frequency domain dielectric spectrum measured data, parameters are solved by combining Kramers-Kronig transformation and a D-H equivalent circuit model parameter identification method of an improved particle swarm algorithm, and a result is verified. The method can improve the accuracy and reliability of oil paper insulation aging state evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of transformer oil-paper insulation aging status assessment, and in particular to a Dissado-Hill model parameter identification method for oil-paper insulation based on an improved particle swarm algorithm. Background Art

[0002] Transformers are core equipment for power transmission and distribution, and their insulation performance directly impacts the safe operation of power systems. Transformer oil-paper insulation systems gradually age over years and due to factors such as temperature and moisture. Therefore, accurate assessment of the aging state of oil-paper insulation is crucial. Dielectric response analysis is a key method for evaluating the condition of oil-paper insulation, typically implemented using a dielectric response equivalent circuit model.

[0003] Currently, mainstream dielectric response equivalent models include the extended Debye model, the Cole-Cole model, and the Davidson-Cole model. These models primarily address a single relaxation process, with model parameters often being single or dual. Some parameters lack clear physical meaning, making them ineffective in reflecting the internal microstructure and charge motion characteristics of dielectrics. Their core flaw is the assumption that the microscopic particles involved in the relaxation process do not interact, which is inconsistent with reality and results in inaccurate descriptions of the motion characteristics of microscopic particles during polarization.

[0004] In the existing technology, such as the "Oil-paper Insulation Equivalent Circuit Model and Parameter Identification Method Based on Weighted Function" (Patent Publication No.: CN117313607A), an extended Debye model is used as the dielectric response equivalent model. Although it realizes broadband equivalent circuit parameter identification through the GA-LMA fusion optimization algorithm, it is limited by the inherent defects of the extended Debye model and is still unable to accurately characterize the interaction and motion characteristics of microscopic particles during the dielectric polarization process. Summary of the Invention

[0005] The purpose of the present invention is to provide a parameter identification method of the Dissado-Hill model of oil-paper insulation based on an improved particle swarm optimization algorithm, which can improve the accuracy and reliability of the aging status assessment of oil-paper insulation.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is: a parameter identification method of the Dissado-Hill model of oil-paper insulation based on an improved particle swarm optimization algorithm, comprising the following steps:

[0007] (1) A Dissado-Hill model is constructed to characterize the diffuse relaxation process of the dielectric response of oil-paper insulation. The model includes the DH loss peak model for the dipole-dominated relaxation peak dielectric response process and the DH QDC model for the carrier-dominated low-frequency dispersion phenomenon. The conductivity and relaxation characteristic information are separated by a broadband dielectric response analysis method based on Kramers-Kronig transform, and a DH equivalent circuit model for the broadband dielectric response of oil-paper insulation is constructed.

[0008] (2) Prepare samples by simulating the natural aging process of oil-paper insulation in a real oil tank environment, cut and process the insulation paperboard, dry it to remove the initial moisture, and then perform frequency domain dielectric spectrum testing to obtain the complex capacitance real part spectrum line and complex capacitance imaginary part spectrum line corresponding to the oil-paper insulation sample;

[0009] (3) Constructing an optimization objective function to transform the parameter identification problem of the DH equivalent circuit model into a nonlinear optimization problem;

[0010] (4) An improved particle swarm algorithm is used to solve the optimization objective function. Chaotic mapping is added to the original particle swarm algorithm, and the random number sequence generated by the chaotic mapping is used to replace the pseudo-random number sequence of the original particle swarm algorithm.

[0011] (5) Based on the measured data of the frequency domain dielectric spectrum, the parameters are solved by combining the Kramers-Kronig transform with the improved particle swarm algorithm to identify the DH equivalent circuit model parameters, and the results are verified.

[0012] Furthermore, in step (1), the repolarization rate expression of the DH loss peak model is:

[0013]

[0014] Where, is the response function of the relaxation peak response; Γ(*) is the Gamma function, 2F1(a, b; c; z) is the Gaussian hypergeometric function; is the amplitude of the relaxation peak polarizability, reflecting the net density of dipoles inside the dielectric; ω represents the test frequency; ω p is the characteristic angular frequency of the loss peak, reflecting the migration rate of the dipole; i represents the imaginary part; m and n1 are parameters reflecting the motion characteristics of the dipole; among them, m reflects the inter-cluster motion characteristics and characterizes the strength of the interaction between dipoles in different clusters; n1 reflects the intra-cluster motion characteristics and characterizes the strength of the interaction between dipoles in the same cluster.

[0015] Furthermore, in step (1), the repolarizability expression of the DH QDC model is:

[0016]

[0017] Where, is the response function of the low-frequency diffuse response; is the amplitude of the low-frequency diffuse polarizability, reflecting the net density of carriers participating in the low-frequency diffuse response during the polarization process; ω represents the test frequency; ω c is the characteristic frequency of the low-frequency diffuse response, reflecting the recombination and dissociation rates of the charges within the cluster; p and n2 are the parameters that determine the slopes of the low-frequency diffuse dielectric response curve on the low-frequency side and the high-frequency side, respectively, and also reflect the motion characteristics of the carriers.

[0018] Furthermore, in step (1), the complex capacitance C of the oil-paper insulation broadband dielectric response DH equivalent circuit model is * (ω) is expressed as:

[0019]

[0020] Where, is the response function of low-frequency diffusion response and relaxation peak response; ε0 is the vacuum dielectric constant; S is the electrode surface area; d is the sample thickness; ω is the test frequency; C ∞ is the frequency-independent capacitance; G is the DC conductivity.

[0021] Furthermore, in step (2), the sample is prepared by:

[0022] Insulating paperboard with a thickness of 1 mm was cut to obtain several groups of circular paperboards with a diameter of 80 mm. The circular paperboards and 25# insulating oil were placed in a constant temperature vacuum drying oven at 110°C and 100 Pa for 72 hours to remove the initial moisture in the experimental samples. Frequency domain dielectric spectrum tests were performed in sequence to obtain the real part of the complex capacitance C′(ω) spectrum line and the imaginary part of the complex capacitance C″(ω) spectrum line corresponding to the oil-paper insulation sample.

[0023] Furthermore, in step (3), the expression of the optimization objective function is:

[0024]

[0025] Where n is the number of measurement frequency points; C′(ω i ) and C″(ω i ) are the frequency points ω i The measured values ​​of the real and imaginary parts of the complex capacitance are as follows; and They are the corresponding frequency points ω i Calculated values ​​of the real and imaginary parts of the complex capacitance.

[0026] Furthermore, in step (4), the improved particle swarm algorithm adds a chaotic map on the basis of the original particle swarm algorithm, that is, a random number sequence generated by the chaotic map is used to replace the pseudo-random number sequence used in the original particle swarm algorithm to increase the randomness and diversity of the algorithm; the calculation formula of the chaotic map is:

[0027] x n+1 =f(x n )

[0028] Where x n represents the value of the nth iteration, and f(x) represents the specific form of the chaotic map;

[0029] The calculation formula of chaotic random numbers is:

[0030]

[0031] Where [x] represents x rounded down.

[0032] Furthermore, in step (5), since the mathematical fitting check needs to comprehensively consider the fitting effect of the equivalent circuit model on the real and imaginary part measurements of the complex capacitance, the mean goodness of fit is adopted. As an indicator to judge the overall fitting effect of the model;

[0033]

[0034] The present invention also provides an oil-paper insulation Dissado-Hill model parameter identification system based on an improved particle swarm algorithm, comprising a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the above-mentioned method can be implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the equivalent circuit model adopted by the prior art is the extended Debye model. Since the model assumes that there is no interaction between the microscopic particles involved in the relaxation process, it is obviously inconsistent with the actual situation and therefore cannot accurately describe the motion characteristics of the microscopic particles during the polarization process. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm algorithm provided by the present invention adopts the Dissado-Hill model as the dielectric response equivalent model, fully considering the interaction between microscopic particles during the dielectric polarization process, and adds non-diffuse units to characterize the non-diffuse relaxation process of the dielectric response, and adds DC conductivity to characterize the energy loss generated during the dielectric polarization process. In the study of the microscopic dielectric properties of dielectrics, the Dissado-Hill model has more obvious theoretical advantages than the Debye model and its derivative models. At the same time, the improved particle swarm algorithm adopted adds chaotic mapping compared to the traditional particle swarm algorithm, which can avoid the problems of premature convergence and falling into local optimal solutions that occur in the traditional particle swarm algorithm to a certain extent, thereby improving the global search capability and optimization accuracy of the algorithm. Compared with general genetic algorithms, it has the advantages of easy implementation, high accuracy and fast convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flowchart of a method implementation of an embodiment of the present invention;

[0037] Figure 2 1 is an equivalent circuit model diagram of the oil-paper insulation DH in the embodiment of the present invention;

[0038] Figure 3 1 is a diagram of the real part spectrum of complex capacitance and the imaginary part spectrum of complex capacitance corresponding to the oil-paper insulation sample in an embodiment of the present invention;

[0039] Figure 4 It is the FDS measured spectrum and calculated spectrum diagram in the embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0042] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0043] like Figure 1 As shown, this embodiment provides a parameter identification method for the Dissado-Hill model of oil-paper insulation based on an improved particle swarm optimization algorithm, comprising the following steps:

[0044] Step 1: Construct a Dissado-Hill model to characterize the diffuse relaxation process of the dielectric response of oil-paper insulation. This model includes the DH loss peak model for the dipole-dominated relaxation peak dielectric response process and the DH QDC model for the carrier-dominated low-frequency dispersion phenomenon. Separate the conductivity and relaxation characteristic information using a broadband dielectric response analysis method based on the Kramers-Kronig transform, and construct a DH equivalent circuit model for the broadband dielectric response of oil-paper insulation.

[0045] Step 2: Prepare the sample by simulating the natural aging process of oil-paper insulation in a real oil tank environment, cut the insulation paperboard, dry it to remove the initial moisture, and then perform frequency domain dielectric spectrum testing to obtain the complex capacitance real part spectrum line and complex capacitance imaginary part spectrum line corresponding to the oil-paper insulation sample;

[0046] Step 3: Construct an optimization objective function to transform the parameter identification problem of the DH equivalent circuit model into a nonlinear optimization problem;

[0047] Step 4: Use the improved particle swarm algorithm to solve the optimization objective function. Add chaos mapping on the basis of the original particle swarm algorithm and use the chaos mapping to generate a random number sequence to replace the pseudo-random number sequence of the original particle swarm algorithm.

[0048] Step 5: Based on the measured data of the frequency domain dielectric spectrum, the parameters are solved by combining the Kramers-Kronig transform with the improved particle swarm algorithm to identify the DH equivalent circuit model parameters, and the results are verified.

[0049] Step 1: Dissado-Hill model construction

[0050] The present invention first introduces the Dissado-Hill (DH) model to characterize the diffuse relaxation process of the dielectric response of oil-paper insulation. The DH model contains two sub-models: one is the DH loss peak model for the dipole-dominated relaxation peak dielectric response process; the other is the DH QDC model for the carrier-dominated low-frequency dispersion phenomenon.

[0051] The microscopic implementation mechanism of the DH loss peak model is: under the excitation of an alternating electric field, a dipole affects the dielectric behavior of adjacent dipoles through multi-body interaction, thereby triggering a chain response that extends in time and space. The complex polarization rate expression of the DH loss peak model is:

[0052]

[0053] Where, is the response function of the relaxation peak response; Γ(*) is the Gamma function, 2F1(a, b; c; z) is the Gaussian hypergeometric function; is the amplitude of the relaxation peak polarizability, reflecting the net density of dipoles inside the dielectric; ω represents the test frequency; ω p is the characteristic angular frequency of the loss peak, reflecting the migration rate of the dipole; i represents the imaginary part; m and n1 are parameters reflecting the motion characteristics of the dipole; among them, m reflects the inter-cluster motion characteristics and characterizes the strength of the interaction between dipoles in different clusters; n1 reflects the intra-cluster motion characteristics and characterizes the strength of the interaction between dipoles in the same cluster.

[0054] The microscopic implementation mechanism of the DH QDC model is as follows: when the frequency of the external electric field is lower than the characteristic frequency, weakly bound charge pairs separate and perform long-distance hopping as carriers along a restricted path. Because carrier hopping involves charge storage, release, and energy loss, as the frequency decreases, the spatial scale of carrier migration increases, and the repolarization rate increases exponentially, resulting in low-frequency dispersion. The repolarization rate expression of the DH QDC model is:

[0055]

[0056] Where, is the response function of the low-frequency diffuse response; is the amplitude of the low-frequency diffuse polarizability, reflecting the net density of carriers participating in the low-frequency diffuse response during the polarization process; ω represents the test frequency; ω c is the characteristic frequency of the low-frequency diffuse response, reflecting the recombination and dissociation rates of the charges within the cluster; p and n2 are the parameters that determine the slopes of the low-frequency diffuse dielectric response curve on the low-frequency side and the high-frequency side, respectively, and also reflect the motion characteristics of the carriers.

[0057] Secondly, considering that dielectrics with inhomogeneous material structures can produce conductance processes, non-diffusive polarization processes, and multiple relaxation response processes under alternating electric field stimulation, the present invention separates the conductance and relaxation characteristic information through a broadband dielectric response analysis method based on the Kramers-Kronig (KK) transform.

[0058] The dielectric response mechanism of dielectrics with inhomogeneous material structures shows that the real part of the complex capacitance C′(ω) measured by the FDS method includes the contribution of the non-diffusive polarization process, while the imaginary part of the complex capacitance C″(ω) includes the contribution of the conductivity loss. Therefore, the KK transformation can be used to extract independent spectra of the conductivity process, non-diffusive polarization process, and relaxation polarization process, and further analyze the type and number of relaxation polarization processes, clarify the microscopic structure of the spectrum, and realize the broadband dielectric response analysis of oil-paper insulation.

[0059] The oil-paper insulation DH equivalent circuit model is as follows Figure 2 Finally, the complex capacitance C of the DH equivalent circuit model of the oil-paper insulation broadband dielectric response is * (ω) is expressed as:

[0060]

[0061] Where, is the response function of low-frequency diffusion response and relaxation peak response; ε0 is the vacuum dielectric constant; S is the electrode surface area; d is the sample thickness; ω is the test frequency; C ∞ is the frequency-independent capacitance; G is the DC conductivity.

[0062] Step 2: Sample preparation

[0063] By simulating the natural aging process of oil-paper insulation in a real oil tank environment, the required samples were obtained and FDS tests were performed. The preparation process is as follows: 1. The insulating paperboard with a thickness of 1 mm was cut to obtain several groups of circular paperboards with a diameter of 80 mm; 2. The circular paperboards and 25# insulating oil were placed in a constant temperature vacuum drying oven at 110°C and 100 Pa for 72 hours to remove the initial moisture in the experimental samples; finally, frequency domain dielectric spectrum tests were performed in sequence to obtain the complex capacitance real part C′(ω) spectrum line and the complex capacitance imaginary part C″(ω) spectrum line corresponding to the oil-paper insulation sample, as shown in Figure 2. Figure 3 shown.

[0064] Step 3: Optimization objective function construction

[0065] Combining formulas (1), (2), and (3), we can see that formula 3 contains multiple substitutions and contains two nonlinear functions with very large computational complexity: the Gamma function and the Gaussian hypergeometric function. Therefore, the parameter identification of the DH equivalent circuit model is essentially a nonlinear optimization problem. The optimization objective function is constructed as follows:

[0066]

[0067] Where n is the number of measurement frequency points; C′(ω i ) and C″(ω i ) are the frequency points ωi The measured values ​​of the real and imaginary parts of the complex capacitance are as follows; and They are the corresponding frequency points ω i Calculated values ​​of the real and imaginary parts of the complex capacitance.

[0068] Step 4: Improve the particle swarm algorithm construction

[0069] In order to solve the objective function optimization problem proposed in step three, the present invention adopts an improved particle swarm algorithm to solve it.

[0070] PSO (Particle Swarm Optimization) is an optimization algorithm based on swarm intelligence. The algorithm simulates birds in a flock by designing a massless particle. The particle has only two properties: speed and position. Speed ​​represents the speed of movement, and position represents the direction of movement. The position of particle i in N-dimensional space is represented by a vector X1=(x1,x2,…,x N ), the flight speed is expressed as a vector V1=(v1,v1,…,v N ). Each particle has a fitness value determined by the objective function and knows its best position (pbest) found so far and its current position X i This can be thought of as the particle's own flight experience. Furthermore, each particle also knows the best position (gbest) found by all particles in the swarm so far. The particle uses these two values, pbest and gbest, to determine its next move.

[0071] In each iteration, the particle updates itself by tracking two "extreme values". After finding these two optimal values, the particle will update its own speed and position using the following formula.

[0072] v i+1 =v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i ) (5)

[0073] x i+1 =x i +v i (6)

[0074] Where: i = 1, 2, ... N, N is the total number of particles in this group; v i is the speed of the particle; rand() is a random number between 0 and 1; x iis the current position of the particle; c1 and c2 are learning factors.

[0075] Based on the above two formulas, the standard form of PSO is formed:

[0076] v i+1 =ω×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i ) (7)

[0077] Where: ω is the inertia factor, and its value is non-negative. When ω is large, the global optimization ability is strong and the local optimization ability is weak; when ω is small, the global optimization ability is weak and the local optimization ability is strong.

[0078] The most commonly used strategy at present is the Linear Decreasing Weight (LDW) strategy:

[0079]

[0080] Where: G k is the maximum number of iterations; ω ini is the initial inertia weight; ω end is the inertia weight when iterating to the maximum number of evolutionary iterations.

[0081] This new approach builds on the existing particle swarm optimization algorithm by adding a chaotic mapping step. This step replaces the pseudo-random number sequence used in traditional particle swarm optimization with a chaotic mapping-generated random number sequence, increasing the algorithm's randomness and diversity. This algorithm can, to a certain extent, avoid the problems of premature convergence and getting stuck in local optimal solutions that often occur in traditional particle swarm optimization algorithms, thereby improving the algorithm's global search capabilities and optimization accuracy.

[0082] The calculation formula of chaos mapping is:

[0083] x n+1 =f(x n ) (9)

[0084] Where x n Represents the value of the nth iteration, and f(x) represents the specific form of the chaotic map.

[0085] The calculation formula of chaotic random numbers is:

[0086]

[0087] Where: [x] means x rounded down.

[0088] Step 5: Result Verification

[0089] According to the FDS measured data in step 2, the parameters are solved by combining the equivalent model parameter identification method of KK transformation and improved particle swarm optimization. The results are shown in Table 1 below.

[0090] Table 1D-H equivalent circuit model parameters

[0091]

[0092] FDS measured and calculated spectral lines are as follows Figure 4 shown.

[0093] Since the mathematical fitting check needs to comprehensively consider the fitting effect of the equivalent circuit model on the real and imaginary part measurements of the complex capacitance, the mean fitting goodness of fit is used. As an indicator to judge the overall fitting effect of the model.

[0094]

[0095] The degree of fit of the model to the FDS measured spectrum is shown in Table 2.

[0096] Table 2 The degree of fit of the equivalent model to the FDS measured spectrum

[0097]

[0098] because The index reaches above 0.95, indicating that the oil-paper insulation DH equivalent circuit model can effectively fit the FDS measured spectrum.

[0099] The present invention also provides an oil-paper insulation Dissado-Hill model parameter identification system based on an improved particle swarm algorithm, comprising a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the above-mentioned method can be implemented.

[0100] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, 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 magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0101] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the 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 steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0102] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0103] 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 the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0104] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A parameter identification method for the Dissado-Hill model of oil-paper insulation based on an improved particle swarm optimization algorithm, characterized in that: The following steps are involved: (1) A Dissado-Hill model is constructed to characterize the diffuse relaxation process of the dielectric response of oil-paper insulation. The model includes the DH loss peak model for the dipole-dominated relaxation peak dielectric response process and the DH QDC model for the carrier-dominated low-frequency dispersion phenomenon. The conductivity and relaxation characteristic information are separated by a broadband dielectric response analysis method based on Kramers-Kronig transform, and a DH equivalent circuit model for the broadband dielectric response of oil-paper insulation is constructed. (2) Prepare samples by simulating the natural aging process of oil-paper insulation in a real oil tank environment, cut and process the insulation paperboard, dry it to remove the initial moisture, and then perform frequency domain dielectric spectrum testing to obtain the complex capacitance real part spectrum line and complex capacitance imaginary part spectrum line corresponding to the oil-paper insulation sample; (3) Constructing an optimization objective function to transform the parameter identification problem of the DH equivalent circuit model into a nonlinear optimization problem; (4) An improved particle swarm algorithm is used to solve the optimization objective function. Chaotic mapping is added to the original particle swarm algorithm, and the random number sequence generated by the chaotic mapping is used to replace the pseudo-random number sequence of the original particle swarm algorithm. (5) Based on the measured data of the frequency domain dielectric spectrum, the parameters are solved by combining the Kramers-Kronig transform with the improved particle swarm algorithm to identify the DH equivalent circuit model parameters, and the results are verified.

2. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (1), the repolarization rate expression of the DH loss peak model is: Where, is the response function of the relaxation peak response; Γ(*) is the Gamma function, 2F1(a, b; c; z) is the Gaussian hypergeometric function; is the amplitude of the relaxation peak polarizability, reflecting the net density of dipoles inside the dielectric; ω represents the test frequency; ω p is the characteristic angular frequency of the loss peak, reflecting the migration rate of the dipole; i represents the imaginary part; m and n1 are parameters reflecting the motion characteristics of the dipole; among them, m reflects the inter-cluster motion characteristics and characterizes the strength of the interaction between dipoles in different clusters; n1 reflects the intra-cluster motion characteristics and characterizes the strength of the interaction between dipoles in the same cluster.

3. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (1), the repolarizability expression of the DH QDC model is: Where, is the response function of the low-frequency diffuse response; is the amplitude of the low-frequency diffuse polarizability, reflecting the net density of carriers participating in the low-frequency diffuse response during the polarization process; ω represents the test frequency; ω c is the characteristic frequency of the low-frequency diffuse response, reflecting the recombination and dissociation rates of the charges within the cluster; p and n2 are the parameters that determine the slopes of the low-frequency diffuse dielectric response curve on the low-frequency side and the high-frequency side, respectively, and also reflect the motion characteristics of the carriers.

4. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (1), the complex capacitance C of the oil-paper insulation broadband dielectric response DH equivalent circuit model is * (ω) is expressed as: Where, is the response function of low-frequency diffusion response and relaxation peak response; ε0 is the vacuum dielectric constant; S is the electrode surface area; d is the sample thickness; ω is the test frequency; C ∞ is the frequency-independent capacitance; G is the DC conductivity.

5. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (2), the sample is prepared by: Insulating paperboard with a thickness of 1 mm was cut to obtain several groups of circular paperboards with a diameter of 80 mm. The circular paperboards and 25# insulating oil were placed in a constant temperature vacuum drying oven at 110°C and 100 Pa for 72 hours to remove the initial moisture in the experimental samples. Frequency domain dielectric spectrum tests were performed in sequence to obtain the real part of the complex capacitance C′(ω) spectrum line and the imaginary part of the complex capacitance C″(ω) spectrum line corresponding to the oil-paper insulation sample.

6. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (3), the expression of the optimization objective function is: Where n is the number of measurement frequency points; C′(ω i ) and C″(ω i ) are the frequency points ω i The measured values ​​of the real and imaginary parts of the complex capacitance are as follows; and They are the corresponding frequency points ω i Calculated values ​​of the real and imaginary parts of the complex capacitance.

7. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (4), the improved particle swarm algorithm adds a chaotic map on the basis of the original particle swarm algorithm, that is, a random number sequence generated by the chaotic map is used to replace the pseudo-random number sequence used in the original particle swarm algorithm to increase the randomness and diversity of the algorithm; the calculation formula of the chaotic map is: x n+1 =f(x n ) Where x n represents the value of the nth iteration, and f(x) represents the specific form of the chaotic map; The calculation formula of chaotic random numbers is: Where [x] represents x rounded down.

8. The parameter identification method of the oil-paper insulation Dissado-Hill model based on the improved particle swarm optimization algorithm according to claim 1 is characterized in that: In step (5), since the mathematical fitting check needs to comprehensively consider the fitting effect of the equivalent circuit model on the real and imaginary part measurements of the complex capacitance, the mean fitting goodness of fit is adopted. As an indicator to judge the overall fitting effect of the model; 9. A parameter identification system for the Dissado-Hill model of oil-paper insulation based on an improved particle swarm optimization algorithm, characterized in that: The method comprises a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method according to any one of claims 1 to 8 can be implemented.

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