CN112287520A - Oil paper insulation expansion Debye model parameter identification method based on two-step optimization method - Google Patents
Oil paper insulation expansion Debye model parameter identification method based on two-step optimization method Download PDFInfo
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Abstract
The invention discloses an oil paper insulation extended Debye model parameter identification method based on a two-step optimization method, which belongs to the technical field of electrical equipment insulation aging state detection.
Description
Technical Field
The invention belongs to the technical field of detection of insulation aging states of electrical equipment, and particularly relates to a parameter identification method of an oil paper insulation expansion Debye model based on a two-step optimization method.
Background
The oil paper insulation system is an important insulation medium of power equipment such as a transformer in a power transmission and distribution system, and the good insulation performance of the oil paper insulation system is of great significance for ensuring the safe and reliable operation of the power equipment. Therefore, accurate and effective evaluation of the state information of the oil paper insulation is the key for guaranteeing the safe and stable operation of the oil paper insulation equipment.
At present, a dielectric response technology is widely applied to grease insulation state evaluation, and a polarization depolarization current method based on a dielectric response principle mainly quantifies a dielectric response function of a composite oilpaper insulation structure system in an extended time domain and reflects the insulation aging state of different parts in the oilpaper insulation system by processing and calculating parameters. The method has the advantages of low test voltage, short test time, portability of hardware equipment required by experiments and the like, and is gradually used for detecting the insulation state of power equipment such as transformers, cables and the like. The existing method for evaluating the aging state of the oil paper insulation equipment by applying a polarization depolarization current method is to provide new characteristic quantities by methods such as formula derivation and the like based on an equivalent model of an oil paper insulation system and combine with the traditional characteristic quantity comprehensive analysis.
The research method based on the equivalent model can more intuitively embody the relation between the characteristic quantity and the insulation structure. At present, equivalent models adopted for modeling and analyzing the oil paper insulation system mainly comprise a Cole-Cole model, an X-Y model and an expanded Debye model which is most widely used. To accurately reflect the aging state of the oiled paper insulation system, the parameter identification of the equivalent model is an extremely important precondition.
The common parameter identification methods include the following methods:
a first differential method: firstly, carrying out primary differentiation on a depolarized current curve to reveal relaxation response information contained in the depolarized current curve, wherein the research shows that the number of peak points of a depolarized current differential spectral line is the number of equivalent circuit relaxation branches; secondly, theoretically analyzing the relation between the function formula of the depolarized current first differential spectral line and equivalent circuit parameters, resolving the spectrum of the first differential spectral line and obtaining corresponding sub-spectral line parameters, and researching and finding that the relaxation branch element parameters can be further identified and obtained through the differential sub-spectral line parameters.
A second derivative method: a secondary time domain differential analysis method is provided on the basis of a primary differential method and used for decomposition of different relaxation processes, the method can accurately judge the number of polarization branches, and the uniqueness of medium response parameter identification is ensured by the extracted spectral line characteristic quantity.
Chicken flock optimization algorithm: and establishing an error function of the depolarized current, and calculating the error between the identified curve and the actual curve. By utilizing the characteristics of swarm optimization and classification optimization of the swarm optimization algorithm, the circuit parameters are adjusted to reduce the error with the actual curve.
In the prior art, such as a first differential method and a second differential method, a phenomenon that a strong relaxation spectral line covers a weak relaxation spectral line easily occurs in a first differential spectral line, so that a branch with a relatively close time constant distribution cannot be identified. The secondary differential time domain dielectric spectrum method can effectively reduce the half-height line width and enable the number of peak points of the differential spectral line to be highlighted, but branches with relatively close time constant distribution are still difficult to identify, and the solving process of the two methods is complex and tedious. An intelligent algorithm such as a chicken flock optimization algorithm has outstanding global search capability and can randomly generate an initial solution, but the local search efficiency is low, and the local optimal solution is easy to trap.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an oil paper insulation extended Debye model parameter identification method based on a two-step optimization method, wherein the two-step optimization method is a fusion optimization algorithm based on a whale optimization algorithm and an LM algorithm, and the extended Debye model parameter identification objective function is solved by combining the whale optimization algorithm and the LM algorithm, so that the problem of easily falling into a local optimal solution is solved, the solving efficiency and the solving precision are effectively improved, the accurate representation of the extended Debye model on the oil paper insulation time domain dielectric response is realized, and the problem of insufficient reliability existing in the equivalent model research of the existing oil paper insulation system by using equivalent model parameters can be effectively solved.
In order to achieve the purpose, the invention adopts the following technical scheme: the method for identifying the parameters of the oil paper insulation expansion Debye model based on the two-step optimization method is characterized by comprising the following steps of:
step S1: testing the transformer test product by adopting a polarization depolarization current method to obtain the polarization current i of the transformer test productpAnd depolarization current id；
Step S2: establishing a model equivalent polarization current i according to an extended Debye modelpAnd depolarization current idTo obtain a depolarization current i 'simulated according to the extended debye model'd；
Step S3: depolarization current i obtained through actual measurement in step S1dAnd the depolarization current i 'obtained by simulation according to the extended Debye model in step S2'dThe error comparison is carried out on the curve, and an error optimization function is constructed as follows:
the error optimization function is an extended Debye model parameter identification target function;
step S4: and (4) performing iterative solution on the extended debye model parameter identification objective function in the step (S3) by adopting a fusion optimization algorithm of a whale optimization algorithm and an LM algorithm to obtain a final solution of the extended debye model parameters.
Furthermore, the equivalent circuit of the extended Debye model is a capacitor C0And a resistor R0Parallel connection, and then parallel connection of n RC series branches for equivalence, wherein C0The insulation geometric capacitance under power frequency is used for reflecting the dielectric property of the oil paper insulation system; r0The insulation resistance is used for representing the conductivity loss of the oil paper insulation system, and n is more than or equal to 2.
Further, in step S2, the extended debye model is built to establish the model equivalent polarization current ipAnd depolarizingCurrent idAre respectively:
in the formula of U0Is a polarization voltage; r0Is an insulation resistor; t is the polarization time; tau isiThe time constant of the ith polarization branch is expressed as:
τi＝RiCi
wherein the polarization branch is an RC series branch, RiIs the equivalent resistance, C, of the i-th polarization branchiThe equivalent capacitance of the ith polarization branch circuit;
Aian index function representing an exponential term, whose expression is:
further, in step S4, the process of iteratively solving the extended debye model parameter identification objective function in step S3 by using a fusion optimization algorithm of a whale optimization algorithm and an LM algorithm is as follows:
firstly, an extended Debye model parameter identification target function, namely an error optimization function, is used as a fitness function, an optimal solution is searched by a whale optimization algorithm from a random solution through global search iteration, and the individual fitness of two continuous generations meets the requirement of | f (x)k+1)-f(xk) When | ≦ ε, f (x)k+1) For the next generation fitness function value, f (x)k) For the current fitness function value, epsilon is a constant with infinitesimal values, the whale optimization algorithm is ended, the approximate solution obtained by the whale optimization algorithm is used as an initial value of the LM algorithm, local search is carried out, and a group of the maximum iteration times meeting the precision requirement is output when the iteration times reach the preset maximum iteration times through the LM algorithm iterationAnd the optimal solution is used as a final solution of each parameter of the extended Debye model.
Further, the preset maximum number of iterations is 500.
Through the design scheme, the invention can bring the following beneficial effects: the method solves the extended Debye model parameter identification objective function through combination of a whale optimization algorithm and an LM algorithm, firstly, global search is carried out through the whale optimization algorithm, then the obtained approximate solution is transmitted to the LM algorithm as an initial value, then accurate search near the approximate solution is carried out through the LM algorithm, finally a group of proper solutions are determined, the problem that local optimal solutions are easy to fall into is solved, the solving efficiency and the solving precision are effectively improved, accurate representation of the extended Debye model on the oil paper insulation dielectric response is achieved, and the problem that reliability is insufficient when equivalent model parameters are used in the equivalent model research of the existing oil paper insulation system can be effectively solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention without limitation and are not intended to limit the invention in any way, and in which:
FIG. 1 is a schematic diagram of a prior art PDC;
FIG. 2 is a plot of polarization depolarizing current and voltage waveforms;
FIG. 3 is a diagram of an extended Debye model for oil-paper insulation;
fig. 4 is a flow chart of parameter identification of an oil-paper insulation expansion debye model based on a two-step optimization method in the embodiment of the invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures. It should be understood that the scope of the present subject matter is not limited to the following examples, and that any techniques implemented based on the teachings of the present invention are within the scope of the present invention.
In the invention, a two-step optimization method comprises the following steps: a fusion optimization algorithm based on a whale optimization algorithm and an LM algorithm fully exerts the advantages of the two algorithms to carry out iterative solution, and the solution efficiency and the solution precision can be improved to a certain extent.
Oil paper insulation system: the good insulating property of the important insulating medium of power equipment such as transformers in a power transmission and distribution system has important significance for ensuring the safe and reliable operation of the power equipment.
Extended debye model: the transformer is regarded as a composite dielectric medium integrally and formed by connecting n series-connected RC branches in parallel, and each RC series-connected branch reflects the relaxation and polarization inside the dielectric medium, so that the aging degree of the oil paper insulation of the transformer is represented.
Measurement principle of polarization depolarization current method:
the oil-impregnated paper insulation test product is subjected to manual accelerated thermal aging at high temperature for different times, a Polarization and Depolarization Current (PDC) test is adopted to track the complete aging process, and PDC tests at different temperatures are carried out at different aging stages.
A simplified circuit diagram of the PDC test, as shown in fig. 1, is composed of a dc power supply, a current meter, and a transformer test piece. The PDC test is performed in two steps, i.e., a polarization process and a depolarization process. Firstly, adding the transformer test sample with complete discharge and the amplitude value of U0Step voltage of, charging the transformer test article for a duration of time tpThe current flowing through the transformer test article in the process is the polarization current ip(ii) a Secondly, grounding the charged transformer test article for a short circuit for a duration tdThe current flowing through the transformer test article in the process is the depolarized current id。
Thus, two current curves can be obtained, and as shown in fig. 2, the properties of the insulating oil and the insulating paper in the transformer can be analyzed by analyzing the wave tail, the wave head and the trend of the curves of the two curves respectively.
Extended debye model for oiled paper insulation system:
the transformer oil paper insulation structure in the polarization depolarization current test is a composite dielectric structure, and the recovery time and dipole turning at different positions in the oil paper are different, so that a plurality of groups of series RC circuits can be connected in parallelThe extended Debye model of composition is equivalent, as shown in FIG. 3, where C0The insulation geometric capacitance under the power frequency reflects the dielectric property of the insulation; r0For insulation resistance, the conductivity loss of the oil paper insulation system can be represented; ri、CiAnd (i ═ 1,2, …, n) respectively represents the polarization resistance and the polarization capacitance of each RC series branch, i represents the ith RC series branch, and the parallel connection of the RC series branches (the dotted line block part in fig. 3) is the polarization circuit part, which can reflect the dielectric polarization phenomena of the oil paper insulation system with different relaxation times. Generally, the larger the number of serial branches of the polarization circuit part is, the higher the aging degree of the oil paper insulation system is, and the more the microscopic change process in the insulation system can be reflected.
The parameter identification of the equivalent model of the oil paper insulation system is to determine a group of error minimum R according to the measured polarization depolarization current data0、C0、Ri、Ci(i-1, 2, …, n),
polarization current i based on extended Debye modelpAnd depolarization current idAre respectively:
in the formula of U0Is a polarization voltage; t is the polarization time; tau isiThe time constant of the ith polarization branch is expressed as:
τi＝RiCi (3)
Aian index function representing an exponential term, whose expression is:
according to the formulae (1) andthe time constant τ can be selected from the formula (2)iAnd pre-exponential function A of the exponential termiAs a reference for parameter identification, the actually measured depolarization current idCurve of (d) and depolarization current i 'obtained from simulation of extended Debye model'dThe error comparison is carried out on the curve, and an error optimization function is constructed as follows:
the error optimization function is a parameter identification target function of the extended Debye model and is according to the formula (2) to i'dIterative solution is carried out, and when the value of the error optimization function is continuously reduced, more accurate R meeting the requirement can be obtained0、C0、Ri、Ci(i ═ 1,2, …, n).
Two-step optimization algorithm
Whale Optimization Algorithm (WOA) is a heuristic search algorithm, and is widely applied due to the characteristics of simple algorithm structure, convenience in implementation, high convergence speed and the like. The algorithm carries out optimization search by simulating the hunting process of the whale with a standing head, and the whole hunting process comprises three behavior modes of surrounding prey, spiral bubble net attack and prey searching:
(1) surrounding prey
The whale standing in the head can identify and surround the prey position, but the optimal solution of the search space, i.e. the prey position, is unknown. In the actual algorithm, the optimal candidate solution of the current iteration is taken as a prey. Once the current optimal candidate solution is identified, the rest of the individuals in the population surround the optimal individual by updating their own position, and the mathematical modeling of this behavior is as follows:
D＝|C·X*(t)-X(t)| (6)
X(t+1)＝X*(t)-A·D (7)
in the formula, t represents the current iteration number; x is a position vector of a population individual; x*The position vector of the current optimal solution is continuously updated along with the iteration; d is the position X of the individual population and the current optimal position C.X after correction*The distance between them; A. c is a coefficient matrix calculated by the following formula:
A＝2a·r1-a (8)
C＝2·r2 (9)
wherein a decays linearly from 2 to 0 throughout the iteration; r is1、r2Is the interval [0,1]A random number in between;
(2) spiral bubble net attack
The whale with the standing head can send spiral bubbles to the prey in the hunting process to form a bubble net to surround the prey; the behavior of the bubble net surrounding attack can be mathematically modeled as a spiral approaching the current optimal individual, and the specific mathematical model is as follows:
D′＝|X*(t)-X(t)| (10)
X(t+1)＝D′·eθl·cos(2πl)+X*(t) (11)
in the formula, D' represents the distance between the tth iteration population individual and the current optimal individual; l is a random number between the intervals [ -1,1 ]; theta is a fixed constant and determines the shape of the logarithmic spiral curve;
(3) hunting article searching
The behavior of searching prey means the process that individuals in the population give up getting close to the current optimal solution and seek the optimal solution in other directions; the significance of the process is to prevent the algorithm from falling into the local optimal solution, so that the algorithm is artificially dispersed to the current optimal solution, and the search range of the solution space is expanded. In WOA, the whale in the standing position performs a hunting search according to the position of random individuals in the population, and the mathematical modeling of the searching process is similar to the hunting process surrounded by equation (7), except that the target individual is transformed from the current best individual to random individuals in the population, and the mathematical modeling of the searching process is shown as follows:
D″＝|C·Xrand-X(t)| (12)
X(t+1)＝Xrand-A·D″ (13)
in the formula, XrandThe position of random individual in the population; d' is the individual position X in the population and the random individual position C.X after the correctionrandThe distance between(ii) a A is still the coefficient matrix described above; since the mathematical model used is similar to that of the surrounding prey, whether the population is close to or far away from the target is still determined by A; in order to make the search space larger, the population individuals need to be dispersed as much as possible, so that | A | ≧ 1 in the behavior of searching a prey is required to be searched;
among the three behaviors, WOA assumes that there is a 50% probability of spiral bubble net attack, and that there is another 50% probability of surrounding or searching for a prey. The coefficient matrix A determines whether to surround the prey and converge to the current optimal solution, or to search for the prey and spread to the solution space. Surrounding the prey when the absolute value of A is less than 1, and searching the prey when the absolute value of A is more than or equal to 1; the mathematical modeling summarizing the population individual location update approach is therefore as follows:
wherein p is the probability of occurrence of the hunting behavior of whales, and the range is [0,1 ].
The LM algorithm is called Levenberg Marquardt method, and is an estimation method of regression parameter least square estimation in nonlinear regression. This method is a method in which the steepest descent method and the linearization method (taylor series) are integrated. Because the steepest descent method is suitable for the case that the parameter estimation value is far from the optimal value in the initial stage of the iteration, and the linearization method, namely the gauss-newton method is suitable for the later stage of the iteration, the parameter estimation value is close to the optimal value range. The two methods combine to find the optimum value faster.
Firstly, an extended Debye model parameter identification target function, namely an error optimization function, is used as a fitness function, a whale optimization algorithm is utilized to randomly solve and search an optimal solution through global search iteration, and the individual fitness of two continuous generations meets the requirement of | f (x) and the individual fitness of two successive generationsk+1)-f(xk) When | ≦ ε, f (x)k+1) For the next generation fitness function value, f (x)k) For the value of the current fitness function, if epsilon is a constant with infinitesimal value, ending the whale optimization algorithm, and obtaining the value through the whale optimization algorithmAnd the obtained approximate solution is used as an initial value of the LM algorithm, local search is carried out, and a group of optimal solutions meeting the precision requirement is output as a final solution of each parameter of the extended Debye model when the iteration times reach the preset maximum iteration times through LM algorithm iteration.
As shown in fig. 4, the two-step optimization method is implemented as follows:
1) initializing and setting the improved whale population position, setting the iteration number to be 500, and taking an error optimization function as an adaptive value function;
2) calculating individual fitness and selecting a current optimal solution;
3) updating the individual position;
4) judging whether an iteration condition is met, if so, obtaining a whale fitness value with optimal fitness and taking the value as an initial value of an LM algorithm; otherwise, returning to the step 2);
5) taking the fitness value as an initial value of an LM algorithm for iteration; if the termination condition is met, namely the iteration times reach 500 times, the algorithm flow is ended, and the solution at the moment is output; if not, the iteration is continued.
Compared with the prior art, the method for solving the parameters of the transformer oil paper insulation model based on the two-step fusion algorithm optimization method is firstly proposed in the invention; the WOA algorithm can reduce the error rate of the algorithm and can also accelerate the searching speed. By combining the LM algorithm, the advantages of the two algorithms can be fully exerted, the situation that the algorithm is trapped in local optimization is avoided, and the solution precision is effectively improved.
It should be understood that the above-mentioned embodiments of the present invention are only examples for clearly explaining the present invention, and are not intended to limit the embodiments of the present invention, and it will be obvious to those skilled in the art that other variations and modifications may be made on the basis of the above description, and all embodiments may not be exhaustive, and obvious variations and modifications may be made within the scope of the present invention.
Claims (5)
1. The method for identifying the parameters of the oil paper insulation expansion Debye model based on the two-step optimization method is characterized by comprising the following steps of:
step S1: testing the transformer test product by adopting a polarization depolarization current method to obtain the polarization current i of the transformer test productpAnd depolarization current id；
Step S2: establishing a model equivalent polarization current i according to an extended Debye modelpAnd depolarization current idTo obtain a depolarization current i 'simulated according to the extended debye model'd；
Step S3: depolarization current i obtained through actual measurement in step S1dAnd the depolarization current i 'obtained by simulation according to the extended Debye model in step S2'dThe error comparison is carried out on the curve, and an error optimization function is constructed as follows:
the error optimization function is an extended Debye model parameter identification target function;
step S4: and (4) adopting a fusion optimization algorithm of a whale optimization algorithm and an LM algorithm to carry out iterative solution on the extended debye model parameter identification target function in the step (S3) to obtain a final solution of the extended debye model parameters.
2. The oil paper insulation expansion debye model parameter identification method based on the two-step optimization method as claimed in claim 1, wherein: the equivalent circuit of the extended Debye model is a capacitor C0And a resistor R0Parallel connection, and then parallel connection of n RC series branches for equivalence, wherein C0The insulation geometric capacitance under power frequency is used for reflecting the dielectric property of the oil paper insulation system; r0The insulation resistance is used for representing the conductivity loss of the oil paper insulation system, and n is more than or equal to 2.
3. The oil paper insulation expansion debye model parameter identification method based on the two-step optimization method as claimed in claim 2, wherein: step by stepIn step S2, the extended Debye model is modeled as an equivalent polarization current ipAnd depolarization current idAre respectively:
in the formula of U0Is a polarization voltage; r0Is an insulation resistor; t is the polarization time; tau isiThe time constant of the ith polarization branch is expressed as:
τi＝RiCi
wherein the polarization branch is an RC series branch, RiIs the equivalent resistance, C, of the i-th polarization branchiThe equivalent capacitance of the ith polarization branch circuit;
Aian index function representing an exponential term, whose expression is:
4. the oil-paper insulation expansion Debye model parameter identification method based on the two-step optimization method according to claim 3, characterized in that: in step S4, the process of iteratively solving the extended debye model parameter identification objective function in step S3 by using the fusion optimization algorithm of the whale optimization algorithm and the LM algorithm is as follows:
firstly, an extended Debye model parameter identification target function, namely an error optimization function, is used as a fitness function, an optimal solution is searched by a whale optimization algorithm from a random solution through global search iteration, and the individual fitness of two continuous generations meets the requirement of | f (x)k+1)-f(xk) When | ≦ ε, f (x)k+1) For the next generation fitness function value, f (x)k) For the current adaptationAnd (3) ending the whale optimization algorithm, taking the approximate solution obtained by the whale optimization algorithm as an initial value of the LM algorithm, carrying out local search, and outputting a group of optimal solutions meeting the precision requirement as final solutions of each parameter of the extended Debye model when the iteration times reach the preset maximum iteration times through LM algorithm iteration.
5. The oil-paper insulation expansion Debye model parameter identification method based on the two-step optimization method according to claim 4, characterized in that: the preset maximum number of iterations is 500.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283176A (en) * | 2021-06-11 | 2021-08-20 | 国网陕西省电力公司电力科学研究院 | Optimization method and system of dielectric response extended Debye model |
| CN113419147A (en) * | 2021-06-29 | 2021-09-21 | 广西电网有限责任公司电力科学研究院 | Radar spectrum diagram based visualized cable insulation state diagnosis and evaluation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104764984A (en) * | 2015-04-16 | 2015-07-08 | 三峡大学 | Improved transformer oil-paper insulation medium response equivalent circuit parameter identification method |
| CN107679327A (en) * | 2017-10-10 | 2018-02-09 | 国网江苏省电力公司电力科学研究院 | Paper oil insulation extension Debye model parameter identification method based on FDS |
| CN109063406A (en) * | 2018-10-26 | 2018-12-21 | 中铁工程装备集团隧道设备制造有限公司 | A kind of horizontal transport locomotive emulation modelling method based on ADVISOR |
| CN110598245A (en) * | 2019-07-26 | 2019-12-20 | 福州大学 | Oil paper insulation Debye circuit parameter identification method based on self-adaptive sampling interval Prony algorithm |
-
2020
- 2020-10-10 CN CN202011076146.6A patent/CN112287520B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104764984A (en) * | 2015-04-16 | 2015-07-08 | 三峡大学 | Improved transformer oil-paper insulation medium response equivalent circuit parameter identification method |
| CN107679327A (en) * | 2017-10-10 | 2018-02-09 | 国网江苏省电力公司电力科学研究院 | Paper oil insulation extension Debye model parameter identification method based on FDS |
| CN109063406A (en) * | 2018-10-26 | 2018-12-21 | 中铁工程装备集团隧道设备制造有限公司 | A kind of horizontal transport locomotive emulation modelling method based on ADVISOR |
| CN110598245A (en) * | 2019-07-26 | 2019-12-20 | 福州大学 | Oil paper insulation Debye circuit parameter identification method based on self-adaptive sampling interval Prony algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 向莹等: "基于WOA-Kriging算法的钛合金切削力预测数学模型研究", 《机电工程》 * |
| 杜林等: "基于频域介电谱的油纸绝缘宽频等效模型参数辨识研究", 《电工技术学报》 * |
| 林朝明等: "油纸绝缘混合极化的频域谱模型及参数辨识", 《仪器仪表学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113283176A (en) * | 2021-06-11 | 2021-08-20 | 国网陕西省电力公司电力科学研究院 | Optimization method and system of dielectric response extended Debye model |
| CN113283176B (en) * | 2021-06-11 | 2023-07-25 | 国网陕西省电力公司电力科学研究院 | Optimization method and system for dielectric response extended debye model |
| CN113419147A (en) * | 2021-06-29 | 2021-09-21 | 广西电网有限责任公司电力科学研究院 | Radar spectrum diagram based visualized cable insulation state diagnosis and evaluation method |
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