A method, device, equipment and medium for online monitoring of surge arrester aging

Abstract

This invention discloses a method, device, equipment, and medium for online monitoring of surge arrester aging based on the BTO algorithm. The method includes: establishing a suitable equivalent simplified model based on parameters reflecting the aging state of the surge arrester (i.e., nonlinear coefficients k, α, and capacitance C); building a BTO algorithm model system to solve for the surge arrester model parameters; acquiring the voltage and leakage current data of the surge arrester; using the BTO algorithm model system to find the optimal values ​​of the surge arrester model parameters k, α, and C; and comparing the solved optimal values ​​of the model parameters with the values ​​solved by the algorithm at the initial installation of the surge arrester to accurately assess the aging condition of the surge arrester. The online monitoring technology for surge arrester aging employed in this invention can completely eliminate interference from harmonic voltage, voltage fluctuations, and frequency fluctuations in the power grid. The errors in the solved parameters k, α, and C are all zero, exhibiting excellent anti-interference performance and stability, and significantly improving the accuracy of online monitoring of surge arrester aging.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology for surge arresters, specifically to a method, device, equipment, and medium for online monitoring of surge arrester aging based on the BTO algorithm. Background Technology

[0002] Metal oxide arresters (MOAs) are devices widely used in power grid systems to protect electrical equipment from overvoltage. MOAs play a crucial role in the construction and development of power grid systems. However, in actual operation, they are not only affected by overvoltage but also by environmental factors such as humidity, temperature, and chemical pollution, which accelerate MOAs aging and, in severe cases, significantly impact the safety of the power grid system.

[0003] Currently, MOA (Metal Oxide Arrester) monitoring technology mainly employs two methods: online monitoring and offline detection. Offline detection methods can better analyze the internal condition of MOAs, but require disassembly and testing of MOAs installed near electrical equipment, resulting in drawbacks such as long cycles and high costs. Existing online monitoring technologies, on the other hand, suffer from weak anti-interference capabilities due to the influence of harmonic voltages, voltage fluctuations, and frequency fluctuations in the power grid system. Therefore, researching online monitoring technologies for MOA aging with excellent anti-interference capabilities and stability is of great significance for the safe operation of power systems. Summary of the Invention

[0004] To address the limitations of existing offline surge arrester monitoring methods, such as the inability to monitor arrester aging in real time and the high costs involved, as well as the weaknesses of online monitoring technologies in resisting interference, this invention provides a method, device, equipment, and medium for online surge arrester aging monitoring based on the BTO (Budorcas Taxicolor Optimization) algorithm. The BTO algorithm provided by this invention possesses powerful global optimization capabilities, enabling it to quickly and accurately solve for the optimal values ​​of the arrester model parameters k, α, and C, thereby providing a timely and accurate assessment of the arrester's aging status.

[0005] This invention provides a method for online monitoring of surge arrester aging based on the BTO algorithm, comprising:

[0006] 1) Establish a suitable equivalent simplified model of the surge arrester based on the parameters that can reflect the aging state of the surge arrester (i.e., nonlinear coefficients k, α and capacitance C).

[0007] 2) Construct a BTO algorithm model system to solve for the optimal values ​​of the parameters k, α, and C of the equivalent simplified model;

[0008] 3) Obtain the voltage data and leakage current data across the operating surge arrester;

[0009] 4) The BTO algorithm model system is used to search for the optimal values ​​of the equivalent simplified model parameters k, α, and C;

[0010] 5) Compare the optimal values ​​of the model parameters k, α, and C obtained by the solution with the model parameter values ​​obtained by the algorithm when the surge arrester was initially installed, so as to make an accurate assessment of the aging condition of the surge arrester.

[0011] Preferably, the equivalent simplified model of the surge arrester in step 1) is specifically a model structure consisting of a nonlinear resistor R and a capacitor C connected in parallel, and the mathematical model of the equivalent simplified model of the surge arrester is specifically as follows:

[0012]

[0013] Among them, i x Indicates the leakage current of the surge arrester; i r i c Represent the resistive and capacitive current components in the leakage current, respectively; u is the voltage across the surge arrester; k and α represent the nonlinear coefficients in the equivalent simplified model of the surge arrester; C is the equivalent capacitance; I ref and U ref These are the reference current and the reference voltage, respectively.

[0014] Preferably, the BTO algorithm model system in step 2) is inspired by the foraging and migration behaviors of takin, specifically:

[0015] The mathematical model of the takin foraging behavior is expressed as follows:

[0016]

[0017] Among them, X n (k+1) = [x1, x2, ..., x d ] n , n = 1, 2, ..., N represents the position vector of the nth takin in the foraging takin herd during the (k+1)th foraging session, and N is the number of takin in the foraging takin herd; R represents the optimal position of the nth takin during the entire foraging process; R = [r1, r2, ..., r d ] n Let represent the foraging perception radius of the nth takin, where d is the dimension of the position vector and the perception radius of the i-th dimension is: in This represents the optimal position of the nth takin in the i-th dimension. Let represent the optimal position of the j-th takin in the entire foraging takin herd in the i-th dimension; w is a random number in (-1, 1); K max This represents the maximum number of iterations.

[0018] The mathematical model of the takin migration behavior is expressed as follows:

[0019]

[0020] Among them, X m (k+1) = [x1, x2, ..., x d ] m Let m = 1, 2, ..., M represent the position vector of the m-th takin in the migrating takin herd during the (k+1)th migration phase, and M represent the number of migrating takins; X * This represents the optimal position within the entire takin herd; β = 2exp(-20(k / K) max )) 2 ; γ = [γ1, γ2, ..., γ d ] m γ i ∈{-1, 1}; η=[η1, η2,..., η d ] m , where η i X is a random number in the interval [1, N]; α α∈[1,N] represents a random position vector in a herd of foraging takins.

[0021] Preferably, the objective optimization function for solving the arrester model parameters in step 4) of the BTO algorithm model system is:

[0022]

[0023] Where i represents the leakage current value calculated by the BTO algorithm; i x The measured leakage current of the surge arrester is represented by ε; T represents the online monitoring time; ε represents the sum of squares of the differences between the leakage current calculated by the algorithm and the measured value; preferably, the objective function in the discrete case is expressed as:

[0024]

[0025] Where N represents the actual number of sampling points for the leakage current of the surge arrester.

[0026] This invention provides an online monitoring device for surge arrester aging based on the BTO algorithm, comprising:

[0027] The data acquisition module is used to acquire the voltage data and leakage current data at both ends of the surge arrester in operation;

[0028] The parameter calculation module is used to solve for the optimal values ​​of the parameters k, α, and C of the equivalent simplified model of the surge arrester by using the BTO algorithm model iterative search based on the collected voltage data and leakage current data at both ends of the surge arrester.

[0029] The performance evaluation module is used to compare the optimal values ​​of the solved model parameters k, α, and C with the model parameter values ​​solved by the algorithm when the surge arrester is initially installed, so as to analyze and evaluate the performance of the surge arrester.

[0030] The results display module is used to output and visualize the aging status of surge arresters in operation.

[0031] This invention provides an online monitoring device for surge arrester aging based on the BTO algorithm, comprising: a processor and a memory for storing processor-executable programs, wherein the memory stores a computer program, and the processor executes the computer program to implement the online monitoring method for surge arrester aging.

[0032] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the online monitoring method for aging of surge arresters.

[0033] This invention provides a method, device, equipment, and medium for online monitoring of surge arrester aging based on the BTO algorithm. The technical solution adopted by the method includes: establishing a suitable equivalent simplified model of the surge arrester based on parameters that can reflect the aging state of the surge arrester (i.e., nonlinear coefficients k, α, and capacitance C); building a BTO algorithm model system to solve for the optimal values ​​of the surge arrester model parameters; acquiring the voltage data and leakage current data at both ends of the surge arrester in operation; using the BTO algorithm model system to search for and solve for the optimal values ​​of the surge arrester model parameters k, α, and C; and comparing the solved optimal values ​​of the model parameters k, α, and C with the model parameter values ​​solved by the algorithm at the initial installation of the surge arrester to accurately assess the aging condition of the surge arrester.

[0034] Compared with existing offline preventive testing and online monitoring technologies for surge arresters, the online aging monitoring method for surge arresters provided by this invention has the following advantages:

[0035] (1) The present invention provides an online monitoring method for surge arrester aging based on the BTO algorithm. Through the powerful global optimization capability of the BTO algorithm, the optimal values ​​of the surge arrester model parameters k, α, and C can be quickly and accurately solved, thereby achieving a precise assessment of the aging state of the surge arrester. In addition, the resistive component in the leakage current of the surge arrester can also be accurately calculated using the BTO algorithm.

[0036] (2) The present invention provides an online monitoring method for surge arrester aging based on the BTO algorithm. The BTO algorithm used has excellent anti-interference ability and stability under the interference of harmonic voltage, voltage fluctuation and frequency fluctuation in the power grid system. The error of the surge arrester model parameters k, α and C is all 0, which significantly improves the accuracy of online monitoring of surge arrester aging and provides important technical guarantee for the safe operation of the power system. Attached Figure Description

[0037] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating an online monitoring method for surge arrester aging based on the BTO algorithm, provided in an embodiment of the present invention;

[0039] Figure 2 A simplified equivalent model of a surge arrester is provided in this embodiment of the invention.

[0040] Figure 3 This is a schematic diagram of the BTO algorithm operation flow provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a surge arrester aging online monitoring device based on the BTO algorithm, provided in an embodiment of the present invention. Detailed Implementation

[0042] To more clearly illustrate the technical solution of the present invention, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0043] Example 1

[0044] Figure 1 This is a flowchart of an online monitoring method for surge arrester aging based on the BTO algorithm, provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where electricians are assisted in detecting the performance status of surge arresters. This method can be executed by an online monitoring device for surge arrester aging. Figure 1 As shown, the method includes:

[0045] S1. Establish a suitable equivalent simplified model of the surge arrester based on the parameters that reflect its aging state (i.e., nonlinear coefficients k, α, and capacitance C). For example, the surge arrester can be equivalently represented as a model of a nonlinear resistor and capacitor connected in parallel, as shown in the simplified model below. Figure 2 As shown. The mathematical model of the equivalent simplified model of the surge arrester is specifically as follows:

[0046]

[0047] Among them, i xIndicates the leakage current of the surge arrester; i r i c Represent the resistive and capacitive current components in the leakage current, respectively; u is the voltage across the surge arrester; k and α represent the nonlinear coefficients in the equivalent simplified model of the surge arrester; C is the equivalent capacitance; I ref and U ref These are the reference current and the reference voltage, respectively. For example, the reference value I... ref Set to 1mA, U ref Set it to 1 pu.

[0048] S2. Construct a BTO algorithm model system to solve for the optimal values ​​of the surge arrester model parameters k, α, and C; the operation flow of the BTO algorithm model system is as follows: Figure 3 As shown. Specifically, including:

[0049] S21. The BTO algorithm performs population initialization operations, including: initializing the foraging population size N, the migrating population size M, the population dimension d, and the maximum number of iterations K. max And the initial random position of the population. For example, the population dimension d is set to 3.

[0050] S22. The BTO algorithm performs a population information update operation, which specifically involves: in each algorithm iteration, calculating the current fitness of the population and sorting it in descending order; taking the top N takins with higher optimal fitness as the foraging population and the bottom M takins with lower optimal fitness as the migrating population.

[0051] S23, the BTO algorithm performs foraging behavior operations, and the mathematical model of the foraging behavior is expressed as:

[0052]

[0053] Among them, X n (k+1) = [x1, x2, ..., x d ] n , n = 1, 2, ..., N represents the position vector of the nth takin in the foraging takin herd during the (k+1)th foraging session, and N is the number of takin in the foraging takin herd; R represents the optimal position of the nth takin during the entire foraging process; R = [r1, r2, ..., r d ] n Let represent the foraging perception radius of the nth takin, where d is the dimension of the position vector and the perception radius of the i-th dimension is: in This represents the optimal position of the nth takin in the i-th dimension. Let represent the optimal position of the j-th takin in the entire foraging takin herd in the i-th dimension; w is a random number in (-1, 1); K maxThis represents the maximum number of iterations. For example, N can be 20, and K... max The value can be 500, but this embodiment of the invention does not limit this.

[0054] S24. The BTO algorithm performs migration operations, and the mathematical model of the migration behavior is expressed as follows:

[0055]

[0056] Among them, X m (k+1) = [x1, x2, ..., x d ] m Let m = 1, 2, ..., M represent the position vector of the m-th takin in the migrating takin herd during the (k+1)th migration phase, and M be the number of migrating takins; X * The optimal location within the entire takin herd; β = 2exp(-20(k / K) max )) 2 ; γ = [γ1, γ2, ..., γ d ] m γ i ∈{-1, 1}; η=[η1, η2,..., η d ] m , where η i X is a random number in the interval [1, N]; α α∈[1,N] represents a random position vector in a herd of foraging takins. For example, M can be 10, but this embodiment of the invention does not impose any limitation on it.

[0057] S25. The BTO algorithm outputs a conditional check. If the convergence condition is met, the algorithm terminates; otherwise, it recalculates. For example, the convergence target value could be 10. -9 However, the embodiments of the present invention do not impose any limitations on this.

[0058] S3. Obtain the voltage data and leakage current data at both ends of the surge arrester in operation;

[0059] S4. The optimal values ​​of the surge arrester model parameters k, α, and C are searched using the BTO algorithm model system. The optimal values ​​of the surge arrester model parameters k, α, and C need to be solved by establishing an objective function, which is specifically:

[0060]

[0061] Where i represents the leakage current value calculated by the BTO algorithm; i x The measured leakage current of the surge arrester is represented by ε; T represents the online monitoring time; ε represents the sum of squares of the differences between the calculated leakage current and the measured value; the objective function in the discrete case is expressed as:

[0062]

[0063] Where N represents the actual number of sampling points for the leakage current of the surge arrester.

[0064] S5. Compare the optimal values ​​of the solved model parameters k, α, and C with the model parameter values ​​solved by the algorithm at the initial installation of the surge arrester to accurately assess the aging status of the surge arrester. For example, if the solved model parameter values ​​are greater than the initial model parameter values, it indicates that the surge arrester has aged, and the relative difference in model parameters can indicate the degree of aging of the surge arrester.

[0065] Example 2

[0066] Embodiment 2 of the present invention provides an online monitoring device for surge arrester aging based on the BTO algorithm. A schematic diagram of the surge arrester aging online monitoring device module is shown below. Figure 4 As shown, it includes:

[0067] Data acquisition module 10 is used to acquire voltage data and leakage current data at both ends of the surge arrester in operation;

[0068] The parameter calculation module 20 is used to solve for the optimal values ​​of the arrester model parameters k, α, and C by using the BTO algorithm model iterative search based on the collected arrester voltage and leakage current data.

[0069] The performance evaluation module 30 is used to compare the optimal values ​​of the solved model parameters k, α, and C with the model parameter values ​​solved by the algorithm when the surge arrester is initially installed, so as to analyze and evaluate the performance status of the surge arrester.

[0070] Result display module 40 is used to output and visualize the aging status of surge arresters in operation.

[0071] Example 3

[0072] Embodiment 3 of the present invention provides an online monitoring device for surge arrester aging based on the BTO algorithm. It includes: a processor and a memory for storing a processor-executable program. The memory stores a computer program, and when the processor executes the computer program, it implements the online monitoring method for surge arrester aging.

[0073] Example 4

[0074] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the online monitoring method for surge arrester aging.

[0075] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Various embodiments of the methods, systems, and computer program products described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, load-programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: implementation in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0076] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0077] The computer-readable storage medium used in this invention may contain or store computer programs for use by or in conjunction with an instruction execution system, apparatus, or device. Computer-readable storage media include, but are not limited to, hard disks, optical storage devices, random access memory (RAM), read-only memory (ROM), or any suitable combination thereof.

[0078] It should be understood that the various processes described above can be reordered, steps can be added or deleted. For example, the steps described in this invention can be executed sequentially or in parallel, and this is not a limitation herein.

[0079] The above embodiments do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention are included within the scope of protection of this invention.

Claims

1. A method for online monitoring of surge arrester aging based on the BTO algorithm, characterized in that, include: Establish an equivalent simplified model that reflects the aging state of surge arresters; A BTO algorithm model system is constructed to solve for the optimal values ​​of parameters k, α, and C in the equivalent simplified model of a surge arrester; where k and α are nonlinear parameters, and C is the equivalent capacitance. Acquire the voltage and leakage current data across the operating surge arrester; The optimal values ​​of parameters k, α, and C in the equivalent simplified model are obtained by using the BTO algorithm model system search. The optimal values ​​of the equivalent simplified model parameters k, α, and C obtained by the solution are compared with the model parameter values ​​obtained by the BTO algorithm model system when the surge arrester is initially installed, so as to make an accurate assessment of the aging condition of the surge arrester. The BTO algorithm is proposed based on the foraging and migration behaviors of takins. Specifically, the foraging behavior of takins involves them finding a suitable feeding location and feeding in their optimal area. The mathematical model of this foraging behavior is described as follows: Among them, X n (k+1)=[x1,x2…,x d ] n ,n=1,2,…,N represents the position vector of the nth takin in the foraging takin herd during the (k+1)th foraging session, and N is the number of takin in the foraging takin herd; R represents the optimal position of the nth takin during the entire foraging process; R = [r1, r2, ..., r d ] n Let represent the foraging perception radius of the nth takin, where d is the dimension of the position vector and the perception radius of the i-th dimension is: in This represents the optimal position of the nth takin in the i-th dimension. Let represent the optimal position of the j-th takin in the entire foraging takin herd in the i-th dimension; w is a random number in (-1, 1); K max The maximum number of iterations; The migratory behavior of takins involves them emitting low, guttural calls during foraging to guide other members of the herd toward them or to change their migration direction, allowing takins in unfavorable positions to find better feeding spots. This migratory behavior is represented by the following mathematical model: Among them, X m (k+1)=[x1,x2…,x d ] m Let m = 1, 2, ..., M represent the position vector of the m-th takin in the migrating takin herd during the (k+1)th migration phase, and M represent the number of migrating takins; X * This represents the optimal position within the entire takin herd; β = 2exp(-20(k / K) max )) 2 ; γ = [γ1, γ2, ..., γ d ] m ,γ i ∈{-1,1}; η=[η1, η2,…, η d ] m , where η i X is a random number in the interval [1, N]; α Let α represent a random location vector in a herd of foraging takins, where α∈[1,N]; The update process of the population position and population distribution of the BTO algorithm is as follows: In each iteration, the current fitness of the BTO algorithm population is calculated and sorted in descending order; the top N takins with the highest optimal fitness are regarded as the foraging population, and the bottom M takins with the lowest optimal fitness are regarded as the migrating population.

2. The online monitoring method for surge arrester aging based on the BTO algorithm according to claim 1, characterized in that, The simplified equivalent model of the surge arrester is specifically a model structure consisting of a nonlinear resistor R and an equivalent capacitance C connected in parallel. The mathematical model of the simplified equivalent model of the surge arrester is as follows: Among them, i x Indicates the leakage current of the surge arrester; i r i c Represent the resistive and capacitive current components in the leakage current, respectively; u is the voltage across the surge arrester; k and α represent the nonlinear coefficients in the equivalent simplified model of the surge arrester; C is the equivalent capacitance; I ref and U ref These are the reference current and the reference voltage, respectively.

3. The online monitoring method for surge arrester aging based on the BTO algorithm according to claim 1, characterized in that, The BTO algorithm for solving the parameters k, α, and C of the equivalent simplified model of the surge arrester requires the establishment of an objective function, which is specifically: Where i represents the leakage current value calculated by the BTO algorithm; i x The measured leakage current of the surge arrester is represented by ε; T represents the online monitoring time; ε represents the sum of squares of the differences between the leakage current calculated by the algorithm and the measured value; the objective function in the discrete case is expressed as: Where N represents the actual number of sampling points for the leakage current of the surge arrester.

4. An online monitoring device for surge arrester aging, performing the method as described in any one of claims 1-3, characterized in that, include: The data acquisition module is used to acquire the voltage data and leakage current data at both ends of the surge arrester in operation; The parameter calculation module is used to iteratively search for the optimal values ​​of parameters k, α, and C of the equivalent simplified model of the surge arrester based on the collected voltage and leakage current data at both ends of the surge arrester and the BTO algorithm model. The performance evaluation module is used to compare the optimal values ​​of the solved model parameters k, α, and C with the model parameter values ​​solved by the BTO algorithm when the surge arrester is initially installed, so as to analyze and evaluate the performance of the surge arrester. The results display module is used to output and visualize the aging status of surge arresters in operation.

5. An online monitoring device for surge arrester aging, comprising a processor and a memory for storing a processor-executable program, characterized in that, The memory stores a computer program corresponding to the method described in any one of claims 1-3, and when the processor executes the computer program, it implements the online monitoring method for surge arrester aging as described in any one of claims 1-3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the online monitoring method for surge arrester aging as described in any one of claims 1-3.

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