A power distribution network lightning arrester differentiated configuration method based on multi-objective optimization

Abstract

The application discloses a power distribution network lightning arrester differentiated configuration method based on multi-target optimization, relates to the technical field of power distribution network lightning arrester configuration, and comprises the following steps: determining the flashover state of a line insulator; an evaluation model of the minimum flashover total number AFOL is established by using a hybrid PEEC-MTL and a Monte Carlo method; the flashover rate of the insulator is used as an index for describing the overall lightning performance of the overhead line, and the lightning stroke risk of each line segment is distinguished; whether the lightning arrester is failed is judged by using an energy threshold model; and the optimal installation position of the lightning arrester under the differentiated lightning arrester protection is obtained. The application considers the change of the total line flashover number, evaluates the damage rate of the lightning arrester, and designs the differentiated lightning arrester protection configuration from the aspects of economy and rationality.

Description

Technical Field

[0001] This invention relates to the field of surge arrester configuration technology in distribution networks, and in particular to a differentiated configuration method for surge arresters in distribution networks based on multi-objective optimization. Background Technology

[0002] As is well known, actual power distribution networks have complex line configurations. When considering installation and operating costs, it is always desirable to use a limited number of surge arresters to protect the overhead transmission lines of the power distribution network.

[0003] Due to the wide coverage of complex power distribution networks in practical engineering, cost-effective lightning protection for power distribution networks has received increasing attention in recent years. However, providing surge arresters at every tower would significantly increase the cost of lightning protection and the difficulty of maintaining the arresters. In this case, the selection of surge arrester locations becomes a critical factor. To achieve ideal lightning protection, it is necessary to improve the selection strategy of surge arresters so that only the minimum number of arresters is needed to achieve the best lightning protection performance. This arrangement of surge arresters is known as differentiated surge arrester protection configuration.

[0004] Previous studies have used lightning surge waveform analysis to determine the impact of tower span length on the lightning protection performance of surge arresters. However, these studies did not consider variations in lightning performance along the line, and therefore the conclusions drawn may be insufficient to determine the optimal surge arrester installation scheme for complex line configurations. Furthermore, the most commonly used indicator for evaluating lightning performance is the flashover rate of the entire system. This indicator only focuses on whether flashover occurs in the entire system and therefore cannot distinguish the lightning strike risk of individual line sections. In distributed overhead transmission lines, due to the limited insulation strength of insulators, flashovers are usually observed on the insulators. Therefore, using the annual flashover count of a single pole as an indicator to describe the overall lightning performance of the overhead line is reasonable and allows for a better evaluation of various surge arrester protection schemes. Summary of the Invention

[0005] In view of the lack of efficient numerical evaluation methods, this invention is proposed.

[0006] Therefore, the problem to be solved by this invention is how to provide a highly efficient assessment technique to evaluate flashover and surge arrester damage rates.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, embodiments of the present invention provide a method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization, comprising: determining the flashover state of line insulators; establishing an evaluation model for the minimum total flashover count (AFOL) using a hybrid PEEC-MTL and Monte Carlo method; using the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines and distinguishing the lightning strike risk of each line segment; using an energy threshold model to determine whether the surge arrester has failed; and obtaining the optimal installation position of the surge arrester under differentiated surge arrester protection.

[0009] As a preferred embodiment of the multi-objective optimization-based differentiated configuration method for surge arresters in distribution networks described in this invention, in order to find the optimal placement of the surge arresters, two objectives need to be considered, including the minimum total flashover quantity (AFO) across all lines. L and the minimum damage rate (DR) of all individual surge arresters L The mathematical formulas for the two objectives are as follows:

[0010]

[0011] Among them, P SA For candidate values ​​of differentiated surge arrester protection configuration, the number of surge arresters is limited, f AFOL (P SA ) and f DRL (P SA ) represent the evaluation models for obtaining variables AFOL and DRL, respectively.

[0012] As a preferred embodiment of the differentiated configuration method for distribution network surge arresters based on multi-objective optimization described in this invention, the evaluation model for establishing the minimum total number of flashovers (AFOL) using the hybrid PEEC-MTL and Monte Carlo methods includes considering the probability of the current parameters and the location of the lightning strike.

[0013] The evaluation model for minimizing the total flashover count (AFOL) established using a hybrid PEEC-MTL and Monte Carlo method is as follows:

[0014]

[0015] Among them, q max This refers to the number of surge arresters selected in the configuration, DR L For surge arrester failure rate, DR L(i) is the probability of the i-th surge arrester failing, and DRL is the total failure rate of the configuration.

[0016] As a preferred embodiment of the differentiated configuration method for distribution network surge arresters based on multi-objective optimization described in this invention, the method of using the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines and distinguishing the lightning strike risk of each line segment includes the following steps: The calculation formula for the flashover state matrix is ​​as follows:

[0017]

[0018] In this matrix, each element has a value of either 0 or 1, indicating that no flashover or a flashover event occurred on the specific tower in the lightning strike sample; the matrix row p i (i = 1...56) represents the flashover state on the tower under different travel samples, and the matrix column L x The values ​​represent the flashover state on different towers under the lightning strike sample; the expected lightning strike location j and the current index parameter k (x=1……6*69), where k takes values ​​from 1 to 6, representing the combination of lightning current waveform of 8 / 20μs or 1 / 50μs and current amplitude.

[0019] As a preferred embodiment of the differentiated configuration method for distribution network surge arresters based on multi-objective optimization described in this invention, the method of using the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines, and distinguishing the lightning strike risk of each line segment, further includes the number of flashovers generated by the i-th pole under all lightning strike events, representing the severity of flashovers at that pole, calculated using the following formula:

[0020]

[0021] Where, N d The term refers to the number of flashovers that terminate on overhead transmission lines, expressed in flashovers per year, N. d The calculation formula is as follows:

[0022] N d =N g A

[0023] Where, N g It is the cloud-to-ground flash memory density, measured in flashover cycles per year per square kilometer, and A is the area being evaluated, measured in square kilometers.

[0024] As a preferred embodiment of the differentiated configuration method for distribution network surge arresters based on multi-objective optimization described in this invention, wherein: in order to describe the overall lightning strike risk of overhead transmission lines, AFO at all poles... L(i) The sum of AFO L As shown below:

[0025]

[0026] Where n represents the number of poles.

[0027] As a preferred embodiment of the differentiated configuration method for distribution network surge arresters based on multi-objective optimization described in this invention, the step of using an energy threshold model to determine whether a surge arrester has failed includes determining whether the surge arrester has failed if the energy absorbed by the surge arrester exceeds its maximum energy capacity E. th If the surge arrester is damaged, the absorbed energy Es follows a log-normal distribution with probability density f(E). x )as follows:

[0028]

[0029] Among them, E mean Let be the average energy absorbed by the surge arrester, and σ be the logarithmic standard deviation to base e; DR be the probability of the i-th surge arrester failing. L(i) The calculation can be obtained as follows:

[0030]

[0031] The sum of DRL(i) represents the total failure rate DRL of the configuration.

[0032] Secondly, to further address the problem of the lack of efficient numerical evaluation methods, this invention provides a differentiated configuration system for distribution network surge arresters based on multi-objective optimization, comprising: a flashover status module for determining the flashover status of line insulators; and an AFO (Automatic Field Operation) module. L The module is used to establish an evaluation model for the minimum total flashover number (AFOL) using a hybrid PEEC-MTL and Monte Carlo method; the lightning performance module is used to use the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines and to distinguish the lightning strike risk of each line segment; the surge arrester judgment module is used to determine whether the surge arrester has failed using an energy threshold model; and the optimal location module is used to obtain the optimal installation location of the surge arrester under differentiated surge arrester protection.

[0033] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, implements any step of the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization as described in the first aspect of the present invention.

[0034] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization as described in the first aspect of the present invention.

[0035] The beneficial effects of this invention are as follows: This invention uses the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines, replacing the evaluation method that uses the flashover rate of the entire system as an indicator. This allows for a better differentiation of the lightning strike risk of each line segment, thereby enabling an overall evaluation of the differentiated protection schemes for different surge arresters. This multi-objective optimization model uses the total number of line flashovers and the total damage rate of surge arresters as evaluation indicators. The former is evaluated using an artificial scene network trained by a genetic algorithm, while the latter uses an alternative model to evaluate the damage rate of surge arresters. Based on the multi-objective optimization objective, a genetic algorithm is used to select different surge arrester locations.

[0036] In the process of differentiated configuration of surge arresters, the variation in the overall flashover number was considered, and the surge arrester damage rate was evaluated. Differentiated surge arrester protection configurations were designed from both economic and rational perspectives. Due to the large computational load, an artificial neural network and alternative model were established based on the flashover state matrix and the probability distribution of surge arrester absorbed energy to effectively evaluate the minimum flashover number and surge arrester damage rate during the optimization process. There is an inseparable relationship between surge arrester damage rate and protection cost; therefore, this evaluation method can rationally allocate a limited number of surge arresters while ensuring cost control. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0038] Figure 1 The overall optimization process for differentiated surge arrester protection configuration.

[0039] Figure 2 This is a flowchart of the training process for an artificial neural network based on a genetic algorithm.

[0040] Figure 3 The distribution of surge arrester damage rate.

[0041] Figure 4 This is a flowchart of multi-objective optimization based on genetic algorithms. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0045] Example 1

[0046] Reference Figures 1-4 This is the first embodiment of the present invention, which provides a method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization, including the following steps:

[0047] S1: An evaluation model for the minimum total flashover number AFOL is established using a hybrid PEEC-MTL and Monte Carlo method.

[0048] Lightning protection design using differentiated surge arresters is typically an optimization problem, aiming to find the optimal placement of the arresters. It usually needs to consider two objectives: the first is the lowest possible total flashover factor (AFO) across all lines. L The second is the minimum damage rate (DR) of all individual surge arresters. L The mathematical formulas for the two objectives are as follows:

[0049]

[0050] Among them, P SA For candidate values ​​of differentiated surge arrester protection configuration, the number of surge arresters is limited, f AFOL (P SA ) and f DRL (P SA ) represent the evaluation models for obtaining variables AFOL and DRL, respectively.

[0051] The flashover condition of line insulators is determined by assessing lightning overvoltages on overhead transmission lines.

[0052] The minimum total number of flashovers must take into account the probability of the current parameters and the location of the lightning strike, and be determined using a hybrid PEEC-MTL method and Monte Carlo method.

[0053] Furthermore, an evaluation model for the minimum total flashover count (AFOL) is established using a hybrid PEEC-MTL and Monte Carlo method as follows:

[0054]

[0055] Among them, q max This refers to the number of surge arresters selected in the configuration, DR L For surge arrester failure rate, DR L(i) is the probability of the i-th surge arrester failing, and DRL is the total failure rate of the configuration.

[0056] To improve computational efficiency, an artificial neural network (ANN) technique combined with a genetic algorithm (GA) was used, and a model for the minimum number of flashovers was constructed based on simulation results.

[0057] If the energy absorbed by the surge arrester under lightning strike conditions exceeds the limit, the surge arrester is considered to have lost its lightning protection function.

[0058] By using a hybrid PEEC-MTL and Monte Carlo method, the energy absorption of the structure can be calculated, and then the damage rate of the structure can be calculated.

[0059] To ensure the overall accuracy of the evaluation model, a large number of calculations were performed taking into account the randomness of lightning return strokes, and the established surrogate model was applied to evaluate the minimum damage rate of the surge arrester during the optimization process.

[0060] Optimization process flowchart as follows Figure 1 As shown, the purpose of the proposed optimization process is to find the optimal design location for the surge arrester.

[0061] In pre-assessment A and pre-assessment B, AFOL and DRL models were established using line configuration data and lightning strike data, respectively, and a genetic optimization algorithm was used to effectively determine the location arrangement of individual surge arresters.

[0062] After the genetic optimization algorithm was completed, the optimal installation position of the surge arrester under differentiated surge arrester protection was obtained.

[0063] S3: The flashover rate of insulators is used as an indicator to describe the overall lightning performance of overhead lines and to distinguish the lightning risk of each line segment.

[0064] Furthermore, using the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines, instead of the evaluation method that uses the flashover rate of the entire system as an indicator, can better distinguish the lightning strike risk of each line segment. The formula for calculating the flashover state matrix is ​​as follows:

[0065]

[0066] In this matrix, each element has a value of either 0 or 1, indicating that no flashover or a flashover event occurred on a specific tower in the lightning strike sample.

[0067] Matrix row p i (i = 1...56) represents the flashover state on the tower under different travel samples, and the matrix column L x The value represents the flashover state on different towers under the lightning strike sample, at the possible lightning strike location (pole) j and the current index parameter k (x=1……6*69), where k takes values ​​from 1 to 6, representing the combination of lightning current waveform (8 / 20μs or 1 / 50μs) and current amplitude (30kA, 50kA or 80kA).

[0068] l max This represents the number of all possible lightning strike locations or nodes on a straight line.

[0069] The number of flashovers generated at the i-th pole under all lightning strike events represents the severity of flashovers at that pole, and its calculation formula is as follows:

[0070]

[0071] Where, N d The number of flashovers that terminates on overhead transmission lines, expressed in flashovers per year, is calculated using the following formula:

[0072] N d =N g A

[0073] Where, N g It is the cloud-to-ground flash memory density, measured in flashover cycles per year per square kilometer, and A is the area being evaluated, measured in square kilometers.

[0074] To describe the overall lightning strike risk of overhead transmission lines, AFO at all poles L(i) The sum of AFO L As shown below:

[0075]

[0076] S4: Use the energy threshold model to determine whether the surge arrester has failed.

[0077] In a multi-objective genetic algorithm, there may be thousands of individuals that need to be evaluated. If each individual is numerically simulated, it will consume a lot of computing resources. In order to accelerate the optimization process, this invention uses a backpropagation artificial neural network model for optimization.

[0078] Since surge arresters must be installed on utility poles with distribution transformers, the input to this artificial neural network is the possible installation location of the surge arrester. The value of 0 or 1 indicates whether the surge arrester is at that pole, i.e., 0 means the surge arrester is not at that pole, and 1 means the surge arrester is at that pole.

[0079] The power distribution network is a highly nonlinear system, so it is difficult for a single artificial neural network training algorithm to achieve a satisfactory prediction model accuracy.

[0080] Genetic algorithms are incorporated into the training of artificial neural networks. By optimizing weights and thresholds, their accuracy can be improved. The flowchart of the artificial neural network algorithm optimization is as follows: Figure 2 As shown.

[0081] Besides flashover events occurring in insulators on power lines, surge arresters can also lose their lightning protection function during lightning strikes. Therefore, the failure rate of surge arresters is an important indicator for distinguishing their lightning protection performance. This invention uses an energy threshold model to determine whether a surge arrester has failed.

[0082] If the surge arrester absorbs energy exceeding its maximum energy capacity E th If the surge arrester is damaged, then the surge arrester's absorbed energy Es follows a log-normal distribution with probability density f(E). x )as follows:

[0083]

[0084] Among them, E mean Let σ be the average energy absorbed by the surge arrester, and let σ be the logarithmic standard deviation with base e. Using this probability density function, the damage rate of the surge arrester can be estimated, such as... Figure 3 As shown.

[0085] The shaded area represents the probability DR of the i-th surge arrester failing. L(i) The calculation is as follows:

[0086]

[0087] It can be inferred that if the average energy absorbed and the logarithmic standard deviation of each surge arrester are given, the above evaluation model can be used to effectively evaluate the surge arrester. The sum of DRL(i) represents the total damage rate DRL of the configuration, used for the second objective.

[0088] Based on the evaluation model of total line flashover number and total line damage rate, a multi-objective genetic optimization method can be used to effectively select different line flashover configurations.

[0089] This multi-objective problem can be solved using the weighted coefficient transformation method. Based on the weighted summation, the multi-objective optimization is transformed into a single-objective optimization, and the optimization flowchart is as follows. Figure 4 As shown.

[0090] S5: Obtain the optimal installation position of the surge arrester under differentiated surge arrester protection.

[0091] This embodiment also provides a distribution network surge arrester differentiated configuration system based on multi-objective optimization, including: a flashover status module for determining the flashover status of line insulators; and an AFO (Automatic Field Control) module. L The module is used to establish an evaluation model for the minimum total flashover number (AFOL) using a hybrid PEEC-MTL and Monte Carlo method; the lightning performance module is used to use the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines and to distinguish the lightning strike risk of each line segment; the surge arrester judgment module is used to determine whether the surge arrester has failed using an energy threshold model; and the optimal location module is used to obtain the optimal installation location of the surge arrester under differentiated surge arrester protection.

[0092] This embodiment also provides a computer device applicable to the case of a distribution network surge arrester differentiated configuration method based on multi-objective optimization, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the distribution network surge arrester differentiated configuration method based on multi-objective optimization as proposed in the above embodiment.

[0093] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0094] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0095] In summary, this invention uses the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines, replacing the evaluation method that uses the flashover rate of the entire system as an indicator. This allows for a better differentiation of lightning strike risks in different line segments, thus enabling a comprehensive evaluation of differentiated protection schemes for different surge arresters. This multi-objective optimization model uses the total number of line flashovers and the total damage rate of surge arresters as evaluation indicators. The former is evaluated using an artificial scene network trained by a genetic algorithm, while the latter uses an alternative model to evaluate the damage rate of surge arresters. Based on the multi-objective optimization objective, a genetic algorithm is used to select different surge arrester locations.

[0096] In the process of differentiated configuration of surge arresters, the variation in the overall flashover number was considered, and the surge arrester damage rate was evaluated. Differentiated surge arrester protection configurations were designed from both economic and rational perspectives. Due to the large computational load, an artificial neural network and alternative model were established based on the flashover state matrix and the probability distribution of surge arrester absorbed energy to effectively evaluate the minimum flashover number and surge arrester damage rate during the optimization process. There is an inseparable relationship between surge arrester damage rate and protection cost; therefore, this evaluation method can rationally allocate a limited number of surge arresters while ensuring cost control.

[0097] Example 2

[0098] Reference Figures 1-4 This is the second embodiment of the present invention. To further verify the advancement of the present invention, experimental simulation and comparative data with existing technologies of the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization are provided.

[0099] Operating environment: A 110kV overhead transmission line with a length of 30 kilometers and 56 towers. The area is hit by an average of 20 lightning strikes per year per km2.

[0100] Specifically, an evaluation model for the minimum total number of flashovers (AFOL) is established using a hybrid PEEC-MTL and Monte Carlo method. Considering lightning strike conditions of different waveforms and amplitudes, the possible number of flashovers at different tower locations along the line is calculated. The final flashover evaluation matrix for all tower locations along the line is obtained through calculation.

[0101] Based on the line configuration data, the possible tower locations for installing surge arresters are determined. There are 10 possible installation locations for the surge arresters. Using a multi-objective genetic algorithm, with the minimum total flashover (AFOL) and minimum surge arrester failure rate (DRL) as optimization objectives, the optimal combination of surge arrester installation locations is found after multiple generations of iteration.

[0102] In the optimization process, in order to improve computational efficiency, artificial neural networks were used to build prediction models for AFOL and DRL, avoiding the need for time-consuming detailed simulation calculations for each one. Finally, after about 2,000 evaluation calculations, the optimal solution was obtained, and the best installation positions for the four surge arresters were determined, which reduced the total number of flashovers by 35% and the average damage rate of the surge arresters by 25%.

[0103] It can be seen that by adopting this optimized configuration scheme, differentiated surge arrester protection is achieved, which improves the surge protection level and power supply reliability of transmission lines.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization, characterized in that: include: Determine the flashover status of the line insulators; An evaluation model for the minimum total flashover number (AFOL) is established using a hybrid PEEC-MTL and Monte Carlo method. The flashover rate of insulators is used as an indicator to describe the overall lightning performance of overhead lines and to distinguish the lightning risk of each line segment. An energy threshold model is used to determine whether a surge arrester has failed. The optimal installation position of the surge arrester under differentiated surge arrester protection is obtained.

2. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 1, characterized in that: To find the optimal placement of surge arresters, two objectives need to be considered, including the minimum total flashover AFO across all lines. L and the minimum damage rate (DR) of all individual surge arresters L The mathematical formulas for the two objectives are as follows: Among them, P SA For candidate values ​​of differentiated surge arrester protection configuration, the number of surge arresters is limited, f AFOL (P SA ) and f DRL (P SA ) represent the evaluation models for obtaining variables AFOL and DRL, respectively.

3. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 1, characterized in that: The evaluation model for establishing the minimum total flashover count (AFOL) using the hybrid PEEC-MTL and Monte Carlo methods includes: The minimum total number of flashovers must take into account the probability of the current parameters and the location of the lightning strike; The evaluation model for minimizing the total flashover count (AFOL) established using a hybrid PEEC-MTL and Monte Carlo method is as follows: Where, q max This refers to the number of surge arresters selected in the configuration, DR L For surge arrester failure rate, DR L(i) is the probability of the i-th surge arrester failing, and DRL is the total failure rate of the configuration.

4. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 3, characterized in that: The method of using the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines and differentiating the lightning strike risk of each line segment includes the following steps: The formula for calculating the flashover state matrix is ​​as follows: In this matrix, each element takes the value of either 0 or 1, indicating that no flashover or a flashover event occurred on a specific tower in the lightning strike sample. Matrix row p i (i = 1...56) represents the flashover state on the tower under different travel samples, and the matrix column L x This indicates the flashover state on different towers under lightning strike samples; The expected lightning strike location j and current index parameter k (x=1……6*69), where k takes values ​​from 1 to 6, representing the combination of lightning current waveform and current amplitude.

5. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 4, characterized in that: The use of the flashover rate of insulators as an indicator to describe the overall lightning performance of overhead lines, and the differentiation of lightning strike risk for different line segments, also include... The number of flashovers generated at the i-th pole under all lightning strike events represents the severity of flashovers at that pole, and the calculation formula is as follows: Where, N d The term refers to the number of flashovers that terminate on overhead transmission lines, expressed in flashovers per year, N. d The calculation formula is as follows: N d =N g A Where, N g It is the cloud-to-ground flash memory density, measured in flashover cycles per year per square kilometer, and A is the area being evaluated, measured in square kilometers.

6. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 5, characterized in that, To describe the overall lightning strike risk of overhead transmission lines, AFO at all poles L(i) The sum of AFO L As shown below: Where n represents the number of poles.

7. The method for differentiated configuration of surge arresters in distribution networks based on multi-objective optimization as described in claim 6, characterized in that: The method of using an energy threshold model to determine whether a surge arrester has failed includes... If the energy absorbed by the surge arrester exceeds its maximum energy capacity E th If the surge arrester is damaged, the absorbed energy Es follows a log-normal distribution with probability density f(E). x )as follows: Among them, E mean Let σ be the average energy absorbed by the surge arrester, and let σ be the logarithmic standard deviation to base e. The probability DR of the i-th surge arrester failing L(i) The calculation can be obtained as follows: The sum of DRL(i) represents the total failure rate DRL of the configuration.

8. A distribution network surge arrester differentiated configuration system based on multi-objective optimization, based on the distribution network surge arrester differentiated configuration method based on multi-objective optimization as described in any one of claims 1 to 7, characterized in that: include: Flashover status module, used to determine the flashover status of line insulators; AFO L Module for establishing the minimum total flashover AFO using a hybrid PEEC-MTL and Monte Carlo method. L Evaluation model; The lightning performance module is used to describe the overall lightning performance of overhead lines by using the flashover rate of insulators as an indicator, and to distinguish the lightning strike risk of each line segment. The surge arrester judgment module is used to determine whether the surge arrester has failed using an energy threshold model. The optimal location module is used to obtain the optimal installation location of the surge arrester under differentiated surge arrester protection.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for differentiated configuration of distribution network surge arresters based on multi-objective optimization as described in any one of claims 1 to 7.

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