A parameter sampling method, device and electronic equipment for a surge arrester

Abstract

The application discloses a lightning arrester parameter sampling method and device and electronic equipment, comprising: initial sampling of the parameters of the lightning arrester according to the sampling point number and the sampling value range, obtaining an initial sample set, and determining the information entropy of the initial sample set, wherein the parameters of the lightning arrester include the leakage current value; in the case that the information entropy is greater than a preset threshold, initializing a sampling coefficient set, and optimizing the sampling coefficient in the initialized sampling coefficient set by using a fish swarm optimization algorithm to obtain a target sampling coefficient; determining a target sampling sequence based on the target sampling coefficient, sampling the parameters of the lightning arrester based on the target sampling sequence, and obtaining a target sample set corresponding to the lightning arrester parameters. The technical scheme of the application improves the determination degree of the sampling sample of the lightning arrester parameters and optimizes the fitting precision of the voltage-current characteristic curve of the lightning arrester.

Description

Technical Field

[0001] This invention relates to the field of surge arrester nonlinear characteristic parameter identification technology, and in particular to a surge arrester parameter sampling method, device and electronic equipment. Background Technology

[0002] Metal oxide surge arresters (MOAs) are devices used to protect electrical equipment. Due to the non-linear characteristics of the MOA resistor, the current flowing through the arrester is extremely small under normal voltage conditions. However, under overvoltage conditions, the MOA resistance decreases rapidly, and energy is released quickly, thus protecting the electrical equipment. MOAs are simple in structure, small in size, and have a high current-carrying capacity, making them widely used in power systems.

[0003] During normal operation, surge arresters exhibit a highly nonlinear characteristic curve due to the excellent nonlinear properties of their metal oxide varistors. As the service life increases, surge arresters inevitably experience large voltage surges, causing migration of the Schottky barrier of the metal oxide varistors and a decrease in nonlinearity. This, in turn, leads to increased leakage current and power consumption, accelerating arrester degradation. Traditional surge arrester power outage tests, also known as surge arrester DC 1mA tests, involve measuring the DC voltage at 1mA and recording it as U. 1mA And record the leakage current value at 75% DC 1mA voltage, denoted as I. 75％U1mA However, this method is easily affected by the external environment and the level of contamination on the surface of the surge arrester.

[0004] To eliminate the influence of surface contamination and the external environment on DC 1mA tests, an expert in the industry has recently proposed using the DC 1mA test volt-ampere characteristic curve of surge arresters to identify parameters characterizing the volt-ampere characteristics of the arresters and to determine the degree of degradation of the arresters based on these parameters. Because the volt-ampere curve of a surge arrester has a large degree of nonlinearity, the leakage current of the arrester changes little when the terminal voltage is low, but changes drastically as the terminal voltage increases.

[0005] Therefore, with a fixed number of sampling points, using uniform linear sampling results in a large degree of uncertainty in the information contained in the final sampled sequence, leading to low fitting accuracy of the volt-ampere characteristic parameters. While increasing the number of sampling points can reduce information entropy, it also leads to excessive sample redundancy. Furthermore, increasing the number of sampling points increases sampling time and the workload of the fitting algorithm. Summary of the Invention

[0006] This invention provides a parameter sampling method, device, and electronic device for surge arresters, in order to improve the certainty of the sampled parameters of surge arresters and optimize the fitting accuracy of the surge arrester's volt-ampere characteristic curve.

[0007] According to one aspect of the present invention, a parameter sampling method for a surge arrester is provided, comprising:

[0008] The parameters of the surge arrester are initially sampled according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set, and the information entropy of the initial sample set is determined. The parameters of the surge arrester include the leakage current value.

[0009] If the information entropy is greater than a preset threshold, an initial sampling coefficient set is initialized, and the sampling coefficients in the initial sampling coefficient set are optimized using a fish swarm optimization algorithm to obtain the target sampling coefficients.

[0010] A target sampling sequence is determined based on the target sampling coefficient, and the parameters of the surge arrester are sampled based on the target sampling sequence to obtain a target sample set corresponding to the surge arrester parameters.

[0011] According to another aspect of the present invention, a parameter sampling device for a surge arrester is provided, comprising:

[0012] The initial sample sampling module performs initial sampling on the parameters of the surge arrester according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set and determine the information entropy of the initial sample set, wherein the parameters of the surge arrester include leakage current values;

[0013] The target coefficient determination module is used to initialize a set of sampling coefficients when the information entropy is greater than a preset threshold, and to optimize the sampling coefficients in the initialized set of sampling coefficients using a fish swarm optimization algorithm to obtain the target sampling coefficients.

[0014] The target sample acquisition module is used to determine the target sampling sequence based on the target sampling coefficient, and to sample the parameters of the surge arrester based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the parameter sampling method for the surge arrester according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the parameter sampling method of the surge arrester according to any embodiment of the present invention.

[0020] The technical solution of this invention involves initially sampling the parameters of a surge arrester based on the number of sampling points and the range of sampling values ​​to obtain an initial sample set, and determining the information entropy of the initial sample set. The surge arrester parameters include leakage current values. If the information entropy exceeds a preset threshold, a sampling coefficient set is initialized, and a fish swarm optimization algorithm is used to optimize the sampling coefficients in the initialized sampling coefficient set to obtain target sampling coefficients. A target sampling sequence is determined based on the target sampling coefficients, and the surge arrester parameters are sampled based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters. This invention solves the problem that existing sampling methods result in significant uncertainty in the information contained in the final sampling sequence, leading to low fitting accuracy of the volt-ampere characteristic parameters. Alternatively, increasing the number of sampling points increases sampling time and the workload of the fitting algorithm. This invention improves the certainty of the samples and optimizes the fitting accuracy through a non-uniform data sampling strategy and a fish swarm optimization algorithm.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0023] Figure 1 A flowchart of a parameter sampling method for a surge arrester provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a frequency distribution diagram of the sampling point current values ​​provided in an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of a parameter sampling method for a surge arrester provided in Embodiment 2 of the present invention;

[0026] Figure 4 This is a flowchart of the algorithm provided in Embodiment 2 of the present invention;

[0027] Figure 5This is a schematic diagram of the structure of a parameter sampling device for a surge arrester provided in Embodiment 3 of the present invention;

[0028] Figure 6 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all 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.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] To further clarify the technical effects of the present invention, the relevant technologies are described before introducing specific embodiments:

[0032] 1. Voltage-current characteristic model of surge arrester DC 1mA test

[0033] The expression characterizing DC leakage of the surge arrester is shown in equation (1).

[0034]

[0035] Where a and k represent the nonlinear parameters of the valve plate and the constant coefficient of the leakage current flowing through the valve plate, respectively. Due to the excellent nonlinear characteristics of metal oxide valve plates, the nonlinear coefficient 'a' of a valve plate in good condition is relatively large, generally not less than 15, and can reach more than 30 for high-performance ones.

[0036] 2. Definition of Information Entropy

[0037] Information entropy is a commonly used metric for measuring sample uncertainty. The smaller the information entropy value, the higher the certainty of the sample, meaning the more clearly defined the information it contains, the lower the sample redundancy, and the more effective information the sample set contains. The definition of information entropy is:

[0038]

[0039] Where x is the sample set, xi is the sample value, and p(xi) is the probability that the sample takes the value xi.

[0040] 3. Uniform sampling of linear signals

[0041] Suppose the current-voltage characteristic of a certain material can be expressed as a linear function of I = U × K, where I ranges from [0, 1000] and the sampling precision is 1. If uniform sampling is adopted and the number of sampling points is set to 1000, then the sampling points can be set as follows: Therefore, the obtained sampling sequence is That is, each sampling point has a unique corresponding current I value. Therefore, the value of p(xi) is always 1, so the information entropy H(X) = 0; where p(xi) represents the probability that the current I at the i-th sampling point is xi, and H(X) represents the degree of certainty of this sampling sequence.

[0042] 4. Uniform sampling of nonlinear signals

[0043] Suppose that the current-voltage characteristic of a certain material can be expressed as I = KU 2 The quadratic function, I, still takes values ​​in the range [0, 1000], with a sampling precision of 1. If uniform sampling is adopted, and the number of sampling points is set to 1000, then the sampling points can be set as follows: Therefore, the obtained sampling sequence is In a statistical sampling sequence, the frequency of the value of I within its range is as follows: Figure 2 As shown.

[0044] As can be seen from the figure, the frequency distribution of I values ​​in the sampling results is uneven (compared to the example of uniform sampling of linear signals, where each sampling point has a unique corresponding I value). That is, in the uniform sampling of nonlinear signals, several sampling points correspond to the same I value. Using equation (2) to calculate the sampling results, we get H(X) = 61.92.

[0045] 5. Non-uniform sampling of nonlinear signals

[0046] Suppose that the current-voltage characteristic of a certain material can be expressed as I = KU 2The quadratic function, I, still takes values ​​in the range [0, 1000], with a sampling precision of 1. If a non-uniform sampling principle is adopted, the number of sampling points is set to 1000, and the sampling points are set to... u represents the u-th sampling point. h is defined as the non-uniform sampling coefficient, and h = 1 indicates uniform sampling. The information entropy H(X) of the sampling result is calculated when h is 1, 1.5, 1.7, 1.9, 2, 2.2, and 2.5 respectively, as shown in Table 1 below.

[0047] Table 1

[0048] h 1 1.5 1.7 1.9 2 2.2 2.5 H(X) 61.92 41.20 18 9.5 0 17.5 41

[0049] As shown in the table above, the information entropy H(X) is related to the value of h. The closer h is to the power of the current-voltage characteristic curve, the lower the information entropy, meaning the sampled sequence can contain the most effective information. Therefore, to maximize the amount of effective information contained in the sampled sequence, the information entropy value of the sampled sequence must be reduced.

[0050] Example 1

[0051] Figure 1 This is a flowchart of a parameter sampling method for a surge arrester provided in Embodiment 1 of the present invention. This embodiment is applicable to sampling the volt-ampere characteristic curve of a metal oxide surge arrester to determine the degradation maturity of the metal oxide surge arrester. This method can be executed by a parameter sampling device for the surge arrester, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0052] S110. Initially sample the parameters of the surge arrester according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set, and determine the information entropy of the initial sample set.

[0053] The number of sampling points can be the total number of sampling points set when sampling the parameters of the surge arrester; the range of sampling values ​​can be the range of values ​​of the sampling points when sampling parameters; the parameters of the surge arrester include the leakage current value.

[0054] Specifically, the number of sampling points is set according to the sampling performance of the device and the requirements. The number of sampling points can affect the final fitting accuracy. Theoretically, the more sampling points the better, but at the same time, the accuracy and storage performance of the instrument also limit the number of sampling points. Let the number of sampling points be N.

[0055] In this embodiment of the invention, the parameters of the surge arrester are initially sampled according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set, including:

[0056] Within the range of the sampling values, at least two sampling points to be used are calculated using a sampling point calculation function based on the number of sampling points and historical sampling coefficients; voltage is applied to the surge arrester based on the at least two sampling points to be used to obtain the leakage current value to be used corresponding to each sampling point to be used; the initial sample set is formed based on the at least two leakage current values ​​to be used.

[0057] Among them, the historical sampling coefficient can be the sampling coefficient used in the historical sampling process, that is, the non-uniform sampling coefficient h mentioned above. The sampling coefficient can be understood as the coefficient that affects the distribution of sampling points. Different sampling coefficients correspond to different distributions of sampling points. The sampling point calculation function can be a function specifically for calculating sampling points. The sampling points to be used can be the sampling points calculated by the number of sampling points. The leakage current to be used can be the surge arrester leakage current value obtained by sampling based on the sampling points to be used.

[0058] The sampling points to be used correspond to the voltage values ​​applied to the surge arrester. Preferably, the sampling points are the voltage values ​​U and U applied to the surge arrester. 1mA The ratio of the two values, and correspondingly, the leakage current value to be used is the leakage current value corresponding to that ratio.

[0059] Specifically, the historical sampling coefficient refers to the best historical sampling coefficient for this surge arrester or the same model. If this surge arrester is being tested for the first time, the value of the sampling coefficient h is randomly set, typically between 10 and 30. The number of sampling points and the historical sampling coefficient can be substituted into the sampling point calculation function to calculate multiple sampling points to be used. For each sampling point to be used, a voltage is applied to the surge arrester according to that sampling point, and then the corresponding leakage current value is collected. Multiple leakage current values ​​form an initial sample set.

[0060] The sampling point calculation function is shown in equation (3):

[0061]

[0062] Let si be the i-th sampling point. After setting the number of sampling points, perform the first voltage sampling on the surge arrester's volt-ampere characteristic curve. The sampling coefficient h for this sampling can be set to the historical best sampling coefficient for this surge arrester or the same model. If this surge arrester is being tested for the first time, the value of the sampling coefficient h is randomly set, generally between 10 and 30. The resulting sampling sequence X = [(s1,x1),(s2,x2)...,(si,xi)...(sN,xN)] is obtained, where si is the sampling point and xi is the sampled value.

[0063] In this embodiment of the invention, determining the information entropy of the initial sample set includes: calculating the information entropy of the initial sample set using an information entropy calculation function, wherein the information entropy calculation function is as follows:

[0064]

[0065] Where x is the initial sample set, xi is the leakage current value to be used, and p(xi) is the probability of obtaining xi.

[0066] Understandably, after obtaining the initial sample set, to determine whether it contains sufficient valid information, the information entropy of the initial sample set can be calculated using an information entropy calculation function. Based on the calculated information entropy, it can be determined whether the initial sample set can serve as a basis for reflecting the basic performance, insulation condition, and determining characteristic parameters of the surge arrester. For example, if the information entropy is less than a preset threshold (a relatively small value), this indicates that the information entropy is sufficiently small, the sample certainty is high, the information contained is clear, the sample redundancy is low, and the sample set contains a large amount of valid information. That is, the initial sample set can serve as a basis for reflecting the basic performance, insulation condition, and determining characteristic parameters of the surge arrester.

[0067] S120. When the information entropy is greater than a preset threshold, initialize the sampling coefficient set and use the fish swarm optimization algorithm to optimize the sampling coefficients in the initialized sampling coefficient set to obtain the target sampling coefficients.

[0068] The preset threshold can be a pre-set information entropy threshold used to determine the uncertainty of the initial sample set; the sampling coefficient set refers to a set including at least two sampling coefficients; the target sampling coefficient can be the optimal sampling coefficient determined by the fish swarm optimization algorithm, and the target sampling coefficient can minimize the information entropy of the sample set.

[0069] Specifically, when the information entropy of the initial sample set is greater than a preset threshold, it indicates that the initial sample set has high uncertainty and contains little priority information. It is necessary to initialize the sampling coefficient set and then optimize it using a fish swarm optimization algorithm to obtain the optimal target sampling coefficients.

[0070] Initializing the sample coefficient set refers to assigning an initial value to each sample coefficient in the set. The initial value can be historical empirical data. The sample coefficient set is the starting point for subsequent optimization processes. The fish swarm optimization algorithm is an optimization algorithm that simulates the behavior of fish swarms. It seeks the optimal solution by simulating the foraging, clustering, and tail-chasing behaviors of fish. The optimization goal is to find the target sample coefficients that minimize the information entropy of the sample set.

[0071] S130. Determine the target sampling sequence based on the target sampling coefficient, and sample the parameters of the surge arrester based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters.

[0072] The target sampling sequence refers to a series of sampling points arranged in a specific order.

[0073] Specifically, after determining the target sampling coefficients, a target sampling sequence can be determined based on the target sampling coefficients, and the parameters of the surge arrester can be sampled using this sequence to obtain the target sample set corresponding to the surge arrester parameters.

[0074] It is understandable that if the target sampling coefficient is the optimal sampling coefficient, the information entropy of the target sample set collected through the target sampling sequence is also the minimum. Therefore, the target sample set can accurately reflect the performance of the surge arrester.

[0075] In this embodiment of the invention, determining the target sampling sequence based on the target sampling coefficient, and sampling the parameters of the surge arrester based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters includes: substituting the target sampling coefficient into the sampling point determination function to obtain at least two target sampling points; and collecting the leakage current of the surge arrester based on the at least two target sampling points to obtain at least two leakage currents.

[0076] Wherein, at least two target sampling points constitute the target sampling sequence; at least two leakage currents constitute the target sample set.

[0077] Specifically, the target sampling coefficients obtained through the optimization algorithm are substituted into the sampling point determination function. This function calculates at least two target sampling points based on the target sampling coefficients. Arranging the calculated target sampling points in order constitutes the target sampling sequence.

[0078] Next, the leakage current of the surge arrester is sampled according to the sampling points specified in the target sampling sequence. At each target sampling point, the leakage current value of the surge arrester is measured and recorded. Through this process, at least two leakage current values ​​can be obtained, which reflect the parameter state of the surge arrester at different sampling points. A target sample set is then constructed. Finally, the leakage current values ​​measured at different target sampling points are combined to form the target sample set corresponding to the surge arrester parameters.

[0079] The above process determines the sampling sequence by using the target sampling coefficients and accurately samples the parameters of the surge arrester, ultimately obtaining a target sample set containing surge arrester parameter information, which provides important data support for surge arrester performance evaluation, fault diagnosis or parameter optimization.

[0080] The technical solution of this invention involves initially sampling the parameters of a surge arrester based on the number of sampling points and the range of sampling values ​​to obtain an initial sample set and determine the information entropy of the initial sample set. If the information entropy exceeds a preset threshold, a sampling coefficient set is initialized, and a fish swarm optimization algorithm is used to optimize the sampling coefficients in the initial sampling coefficient set to obtain target sampling coefficients. A target sampling sequence is determined based on the target sampling coefficients, and the surge arrester parameters are sampled based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters. This invention solves the problem that existing sampling methods result in significant uncertainty in the information contained in the final sampling sequence, leading to low fitting accuracy of the volt-ampere characteristic parameters. It also addresses the adverse effects of increasing the number of sampling points on sampling time and the workload of the fitting algorithm. By employing a non-uniform data sampling strategy and a fish swarm optimization algorithm, the determination of the samples is improved, and the fitting accuracy is optimized.

[0081] Example 2

[0082] Figure 3 This is a flowchart illustrating a parameter sampling method for a surge arrester according to Embodiment 2 of the present invention. This embodiment further refines the process of optimizing the sampling coefficients using a fish swarm optimization algorithm, based on the above embodiments. For specific implementation details, please refer to the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 3 As shown, the method includes:

[0083] S210. Initially sample the parameters of the surge arrester according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set, and determine the information entropy of the initial sample set.

[0084] S220. When the information entropy is greater than a preset threshold, at least two sampling coefficients are initialized according to the number of sampling points and the range of sampling values, and the initial sampling coefficient set is formed based on the at least two sampling coefficients.

[0085] In a preferred embodiment, since the value of the sampling coefficient h in the initial sampling is arbitrary, the sampled samples will be unevenly distributed. Therefore, the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is used to interpolate and expand the sparsely distributed portion of the sampled sequence, artificially supplementing new samples.

[0086] Initialization yields at least two sampling coefficients, which can be defined as the set of sampling coefficient h values ​​as X = (h1, h2, ..., hi, ..., hn), where hi represents the position of the fish and is also the variable for optimization. The number of fish is selected as n.

[0087] S230. The initial sampling coefficient set is used as the fish swarm corresponding to the fish swarm optimization algorithm, and the sampling coefficients in the initial sampling coefficient set are used as individuals in the fish swarm.

[0088] Specifically, we can consider set X as a school of fish and h in the set as an individual fish.

[0089] S240. Define the objective function of the fish swarm optimization algorithm.

[0090] The objective function consists of a sampling point determination function and an information entropy calculation function, wherein the sampling point determination function is a function of the sampling coefficients.

[0091] Specifically, the objective function can be defined as Y = H(f(hi)), where Y is the objective function value, the objective function H is the information entropy calculation function, which is formula (4), and the function f is the function that determines the sampling sequence based on the sampling coefficient hi, which is the function corresponding to formula (3).

[0092] S250. Optimize the individuals in the fish swarm using a fish swarm optimization algorithm to minimize the value of the objective function, and use the individual corresponding to the minimum value of the objective function as the target sampling coefficient.

[0093] It should also be noted that when calculating using the fish swarm optimization algorithm, the distance between individual fish in the swarm, dij = |hi - hj|, needs to be set in advance; Visual represents the line of sight of the fish swarm; step represents the maximum movement distance of the fish swarm in a single iteration; δ is the fish swarm crowding factor; and try-num is the maximum number of repetitions of the fish swarm in the foraging behavior.

[0094] Based on the above technical solution, the step of optimizing individuals in the fish swarm using a fish swarm optimization algorithm to minimize the value of the objective function, and using the individual corresponding to the minimum objective function value as the target sampling coefficient, includes: for each individual in the fish swarm, executing algorithms corresponding to foraging behavior, gregarious behavior, and tail-chasing behavior to obtain individuals to be used for each behavior; determining candidate individuals from at least two individuals to be used, and updating each individual in the fish swarm based on the candidate individuals; repeating the process of determining and updating candidate individuals until a preset condition is met to obtain a target individual, and using the target individual as the target sampling coefficient.

[0095] Specifically, first, each individual fish in the school is instructed to perform individual foraging behavior, gregarious behavior, and tail-chasing behavior.

[0096] A. Foraging behavior:

[0097] Foraging behavior simulates the purposeful movement of fish towards areas where food is more likely to be found. In this case, it specifically refers to the h value corresponding to a smaller objective function value Y. This can be expressed by the following formula:

[0098]

[0099] Where rand is a random number between 0 and 1, and hi represents the action of moving fish with index i to the better position of fish with index j. The objective function value Y is calculated after each move. If the value of Y does not decrease after try-num times, then a random step is moved, expressed as follows:

[0100] h next =h i +r*visual (6)

[0102] The value of r is in the range of [-1, 1];

[0103] B. Grouping behavior:

[0104] Schooling behavior mimics how fish avoid danger or increase their chances of obtaining food by approaching a group. Since there is no leader in a school, the behavior of individual fish follows two rules: 1. Individual fish will search for the center of the school within their field of vision and move towards it; 2. If an individual fish finds the school too crowded, it will not move towards it.

[0105] In practice, we define the current individual fish in the swarm as hi; its visual field as visual; the number of fish in the visual field as nf; the center position of the swarm as hc; and the corresponding objective function value as Yc; δ is the crowding factor. Then, we first perform the following judgment: if Yc < hc < nf, we determine the fish population. i >δn f Y c If the condition is met, the fish at position hi will exhibit gregarious behavior; otherwise, they will remain at their original positions. The gregarious behavior is defined as follows:

[0106]

[0107] C. Rear-end collision:

[0108] Tail-chasing behavior mimics the behavior of fish in search of food, often following those that have already obtained food to improve foraging efficiency. This process is also similar to schooling behavior; if the fish being followed are in a crowded area, the tail-chasing behavior will cease.

[0109] In practice, we define the current individual fish in the swarm as hi; its visual field as visual; and within the visual field, we search for companions hmax, which corresponds to the optimal objective function value Ymax. The number of fish in the visual field of companion hmax is nf; δ remains the crowding factor. Then, we first perform the following judgment: if Y... i >δn f Y max If the condition is met, the individual fish at position hi will engage in tail-chasing behavior; otherwise, they will remain in their original positions. Tail-chasing behavior is defined as follows:

[0110]

[0111] Then, update the next position of the fish school.

[0112] Comparing the objective function values ​​Yc corresponding to the positions hnext obtained from the fish's foraging behavior, schooling behavior, and tail-chasing behavior in the above process, the hnext corresponding to the smallest objective function value is taken as the final position of the individual fish in the school.

[0113] In an optional implementation, the process of repeatedly determining and updating candidate individuals until a preset condition is met to obtain a target individual, and using the target individual as the target sampling coefficient, includes: stopping the repetition when the number of updates is greater than a preset update number threshold or the value of the objective function calculated based on the individuals in the fish swarm is less than a preset entropy threshold, and using the optimal individual in the fish swarm as the target individual.

[0114] Repeat the above steps until the number of iterations or the objective function value is less than the set value (preset entropy threshold), and the optimal sampling coefficient value h can be obtained.

[0115] S260. Determine the target sampling sequence based on the target sampling coefficient, and sample the parameters of the surge arrester based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters.

[0116] Specifically, re-sampling is performed. After obtaining the optimal sampling coefficient h, the distribution of sampling points can be calculated based on the optimal sampling coefficient h and equation (3), and re-sampling can be performed based on the calculated sampling points to obtain the target sample set.

[0117] In a preferred embodiment, addressing the problem of poor fitting accuracy of the volt-ampere characteristic curve caused by uniform sampling in existing DC 1mA surge arrester tests, this invention proposes a non-uniform data sampling strategy. This strategy uses the principle of minimizing sample information entropy to determine the sampling points of the sampling sequence, aiming to improve the certainty of the samples and optimize the fitting accuracy with a fixed number of sampling points. A fish swarm optimization algorithm is employed, using the information entropy of the sampled data as the optimization objective to optimize the sampling parameters until the sample information entropy meets the requirements. Finally, the volt-ampere characteristic curve is resampled. This strategy is applicable to the sampling of volt-ampere characteristic curves for all metal oxide surge arresters. The algorithm flowchart provided in Embodiment Two of this invention is as follows: Figure 4 As shown.

[0118] The technical solution of this invention involves initially sampling the parameters of a surge arrester based on the number of sampling points and the range of sampling values ​​to obtain an initial sample set and determine the information entropy of the initial sample set. If the information entropy exceeds a preset threshold, a sampling coefficient set is initialized, and a fish swarm optimization algorithm is used to optimize the sampling coefficients in the initial sampling coefficient set to obtain target sampling coefficients. A target sampling sequence is determined based on the target sampling coefficients, and the surge arrester parameters are sampled based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters. This invention solves the problem that existing sampling methods result in significant uncertainty in the information contained in the final sampling sequence, leading to low fitting accuracy of the volt-ampere characteristic parameters. It also addresses the adverse effects of increasing the number of sampling points on sampling time and the workload of the fitting algorithm. By employing a non-uniform data sampling strategy and a fish swarm optimization algorithm, the determination of the samples is improved, and the fitting accuracy is optimized.

[0119] Example 3

[0120] Figure 5 This is a schematic diagram of the parameter sampling device for a surge arrester provided in Embodiment 3 of the present invention. Figure 5 As shown, the device includes:

[0121] The initial sample sampling module 310 performs initial sampling on the parameters of the surge arrester according to the number of sampling points and the range of sampling values ​​to obtain an initial sample set and determine the information entropy of the initial sample set, wherein the parameters of the surge arrester include leakage current values;

[0122] The target coefficient determination module 320 is used to initialize a set of sampling coefficients when the information entropy is greater than a preset threshold, and to optimize the sampling coefficients in the initialized set of sampling coefficients using a fish swarm optimization algorithm to obtain the target sampling coefficients.

[0123] The target sample acquisition module 330 is used to determine the target sampling sequence based on the target sampling coefficient, and to sample the parameters of the surge arrester based on the target sampling sequence to obtain the target sample set corresponding to the surge arrester parameters.

[0124] Optionally, the initial sample sampling module 310 includes:

[0125] The sampling point determination submodule is used to calculate at least two sampling points to be used within the range of the sampling values, based on the number of sampling points and historical sampling coefficients, through a sampling point calculation function, wherein the sampling points to be used correspond to the voltage values ​​applied to the surge arrester;

[0126] The leakage current value determination submodule is used to apply voltage to the surge arrester based on at least two of the sampling points to be used, and obtain the leakage current value to be used corresponding to each of the sampling points to be used.

[0127] An initial sample set construction submodule is used to construct the initial sample set based on at least two of the leakage current values ​​to be used.

[0128] Optionally, the initial sample sampling module 310 includes:

[0129] The information entropy calculation submodule is used to calculate the information entropy of the initial sample set using an information entropy calculation function, which is as follows:

[0130]

[0131] Where x is the initial sample set, xi is the leakage current value to be used, and p(xi) is the probability of obtaining xi.

[0132] Optionally, the target coefficient determination module 320 includes:

[0133] The sampling coefficient initialization submodule is used to initialize at least two sampling coefficients based on the number of sampling points and the range of sampling values, and to form the initial sampling coefficient set based on the at least two sampling coefficients.

[0134] Optionally, the target coefficient determination module 320 includes:

[0135] The fish swarm determination submodule is used to take the initial sampling coefficient set as the fish swarm corresponding to the fish swarm optimization algorithm, and the sampling coefficients in the initial sampling coefficient set as individuals in the fish swarm;

[0136] The objective function definition submodule is used to define the objective function of the fish swarm optimization algorithm, wherein the objective function consists of a sampling point determination function and an information entropy calculation function, and the sampling point determination function is a function of the sampling coefficients;

[0137] The target sampling coefficient determination submodule is used to optimize individuals in the fish swarm using a fish swarm optimization algorithm to minimize the value of the objective function, and the individual corresponding to the minimum value of the objective function is used as the target sampling coefficient.

[0138] Optionally, the target sampling coefficient determination submodule includes:

[0139] The individual to be used determination unit executes the algorithms corresponding to foraging behavior, gregarious behavior and tail-chasing behavior for each individual in the fish group to obtain the individual to be used corresponding to each behavior;

[0140] A candidate individual determination unit is configured to determine candidate individuals from at least two individuals to be used, and update each individual in the fish swarm based on the candidate individuals;

[0141] The repeat stop judgment unit is used to repeatedly execute the process of determining and updating candidate individuals until a preset condition is met, in which case the target individual is obtained and the target individual is used as the target sampling coefficient.

[0142] Optionally, the repetition stop determination unit is specifically used for:

[0143] If the number of updates exceeds a preset update threshold or the value of the objective function calculated based on the individual fish in the swarm is less than a preset entropy threshold, then the repetition stops, and the best individual in the swarm is selected as the target individual.

[0144] Optionally, the target sample acquisition module 330 is specifically used for:

[0145] Substituting the target sampling coefficients into the sampling point determination function yields at least two target sampling points; wherein, the at least two target sampling points constitute the target sampling sequence;

[0146] Based on at least two target sampling points, the leakage current of the surge arrester is collected to obtain at least two leakage currents, wherein the at least two leakage currents constitute the target sample set.

[0147] The surge arrester parameter sampling device provided in this embodiment of the invention can execute the surge arrester parameter sampling method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0148] Example 4

[0149] Figure 6 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0150] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0151] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0152] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the parameter sampling method for a surge arrester.

[0153] In some embodiments, the surge arrester parameter sampling method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the surge arrester parameter sampling method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the surge arrester parameter sampling method by any other suitable means (e.g., by means of firmware).

[0154] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations 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.

[0155] 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.

[0156] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0157] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0158] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0159] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0160] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0161] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of sampling parameters of a surge arrester, characterized in that, The method comprises the following steps: initial sampling of the parameters of the surge arrester according to the sampling point number and the sampling value range, to obtain an initial sample set, and determining the information entropy of the initial sample set, wherein the parameters of the surge arrester include the leakage current value; in the case where the information entropy is greater than a preset threshold, initializing a sampling coefficient set, and optimizing the sampling coefficients in the initialized sampling coefficient set by using a fish swarm optimization algorithm to obtain a target sampling coefficient; determining a target sampling sequence based on the target sampling coefficient, and sampling the parameters of the surge arrester based on the target sampling sequence to obtain a target sample set corresponding to the parameters of the surge arrester.

2. The method of claim 1, wherein, The initial sampling of the parameters of the surge arrester according to the sampling point number and the sampling value range to obtain an initial sample set comprises: in the sampling value range, at least two to-be-used sampling points are calculated according to the sampling point number and historical sampling coefficients by using a sampling point calculation function, wherein the to-be-used sampling points correspond to voltage values applied to the surge arrester; voltage is applied to the surge arrester according to the at least two to-be-used sampling points, to obtain to-be-used leakage current values corresponding to each to-be-used sampling point; the initial sample set is constituted based on the at least two to-be-used leakage current values.

3. The method of claim 2, wherein, The determination of the information entropy of the initial sample set comprises: the information entropy of the initial sample set is calculated by using an information entropy calculation function, and the information entropy calculation function is as follows: wherein, x is the initial sample set, xi is the to-be-used leakage current value, and p(xi) is the probability of taking xi.

4. The method of claim 1, wherein, In the case where the information entropy is greater than a preset threshold, the initialization of a sampling coefficient set comprises: at least two sampling coefficients are initialized according to the sampling point number and the sampling value range, and the initialized sampling coefficient set is constituted based on the at least two sampling coefficients.

5. The method of claim 1, wherein, The optimization of the sampling coefficients in the initialized sampling coefficient set by using a fish swarm optimization algorithm to obtain a target sampling coefficient comprises: the initialized sampling coefficient set is taken as a fish swarm corresponding to the fish swarm optimization algorithm, and the sampling coefficients in the initialized sampling coefficient set are taken as individuals in the fish swarm; a target function of the fish swarm optimization algorithm is defined, wherein the target function is constituted by a sampling point determination function and an information entropy calculation function, and the sampling point determination function is a function of the sampling coefficients; the individuals in the fish swarm are optimized by using the fish swarm optimization algorithm, so that the value of the target function is minimized, and the individual corresponding to the minimum value of the target function is taken as the target sampling coefficient.

6. The method of claim 5, wherein, The optimization of the individuals in the fish swarm by using the fish swarm optimization algorithm, so that the value of the target function is minimized, and the individual corresponding to the minimum value of the target function is taken as the target sampling coefficient, comprises: for each individual in the fish swarm, the algorithms corresponding to the foraging behavior, the aggregation behavior and the following behavior are executed to obtain to-be-used individuals corresponding to each behavior; a candidate individual is determined from the at least two to-be-used individuals, and each individual in the fish swarm is updated based on the candidate individual; The determination and updating process of the candidate individual is repeatedly performed until a preset condition is met, and a target individual is obtained, and the target individual is taken as the target sampling coefficient.

7. The method of claim 6, wherein, The determination and updating process of the candidate individual is repeatedly performed until a preset condition is met, and a target individual is obtained, and the target individual is taken as the target sampling coefficient, including: When the number of updates is greater than a preset update number threshold or a value of the target function calculated according to the individual in the fish group is less than a preset entropy threshold, the repetition is stopped, and the optimal individual in the fish group is taken as the target individual.

8. The method of claim 1, wherein, The target sampling sequence is determined based on the target sampling coefficient, the parameters of the lightning arrester are sampled based on the target sampling sequence, and a target sample set corresponding to the parameters of the lightning arrester is obtained, including: The target sampling coefficient is substituted into a sampling point determination function to obtain at least two target sampling points; wherein the at least two target sampling points constitute the target sampling sequence; Based on the at least two target sampling points, the leakage current of the lightning arrester is collected to obtain at least two leakage currents, wherein the at least two leakage currents constitute the target sample set.

9. A parameter sampling device of a surge arrester, characterized by, Including: An initial sample sampling module samples the parameters of the lightning arrester according to a sampling point number and a sampling value range to obtain an initial sample set, and determines an information entropy of the initial sample set, wherein the parameters of the lightning arrester include a leakage current value; A target coefficient determination module is configured to initialize a sampling coefficient set when the information entropy is greater than a preset threshold, and optimize the sampling coefficients in the initialized sampling coefficient set by using a fish group optimization algorithm to obtain a target sampling coefficient; A target sample collection module is configured to determine a target sampling sequence based on the target sampling coefficient, sample the parameters of the lightning arrester based on the target sampling sequence, and obtain a target sample set corresponding to the parameters of the lightning arrester.

10. An electronic device, comprising: The electronic device includes: At least one processor; and A memory connected in communication with the at least one processor; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the parameter sampling method of the lightning arrester in any one of claims 1-8.

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