A detection method, device and equipment of a lightning arrester and a storage medium

Abstract

This invention discloses a method, apparatus, device, and storage medium for detecting surge arresters. The method includes: establishing an equivalent circuit for a metal oxide surge arrester in power equipment under DC voltage; converting the equivalent circuit into a mathematical model characterizing the volt-ampere characteristics of the surge arrester under the presence of pollution, wherein the mathematical model contains unknown parameters; fitting the parameters to the surge arrester under the presence of pollution; and if the fitting is successful, detecting the operating state of the surge arrester based on the learned model. By deriving a mathematical model to characterize the operating state of the surge arrester's surface pollution, obtaining parameters characterizing the surface pollution of the surge arrester through fitting, and quantifying the specific value of surface pollution based on the obtained parameters, the influence of surface pollution on the surge arrester is eliminated, and the degree of influence of surface pollution on the surge arrester is stably quantified. This improves the safety of surge arrester testing while also increasing work efficiency and the reliability of test results.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a method, apparatus, equipment and storage medium for detecting surge arresters. Background Technology

[0002] A surge arrester is an electrical device used to protect power equipment from high transient overvoltages and to limit the duration and amplitude of follow current. Surge arresters are also an important device frequently used to prevent lightning strikes on communication cables. Surge arresters are susceptible to high temperatures, extreme cold, and corrosion from pollution. During operation, they may experience aging, moisture absorption, and other issues, posing operational risks. Therefore, it is essential to monitor the operating status of surge arresters.

[0003] Currently, the operational status of surge arresters is mainly tracked through two methods: power-off testing and live testing. Among these, the most accurate testing method is power-off testing.

[0004] Live-line testing is susceptible to electric field interference. During de-energized testing, i.e., DC voltage testing of surge arresters, if the leakage current is excessive within a certain voltage range, the outer surface of the surge arrester's porcelain bushing is usually wiped or shielded to eliminate the influence of surface contamination. However, shielding or wiping are largely ineffective. Therefore, leakage current caused by surface contamination is a significant interference affecting the accuracy of de-energized testing. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for testing surge arresters, in order to solve the interference caused by surface contamination of surge arresters in the test results of monitoring the operating status of surge arresters.

[0006] According to one aspect of the present invention, a method for detecting a surge arrester is provided, the method comprising:

[0007] The equivalent circuit of a metal oxide surge arrester in power equipment under DC voltage;

[0008] The equivalent circuit is converted into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, wherein the mathematical model has unknown parameters;

[0009] The parameters are fitted to the surge arrester under polluted conditions;

[0010] If the fitting is successful, the operating status of the surge arrester is detected based on the learned model.

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

[0012] The equivalent circuit establishment module is used to establish the equivalent circuit of metal oxide surge arresters in power equipment under DC voltage.

[0013] An equivalent circuit conversion module is used to convert the equivalent circuit into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, wherein the mathematical model has unknown parameters.

[0014] A parameter fitting module is used to fit the parameters of the surge arrester under conditions of contamination.

[0015] The operation status detection module is used to detect the operation status of the surge arrester based on the learning model if the fitting is completed.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] 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 surge arrester detection method according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, the computer program being configured to cause a processor to execute and implement the surge arrester detection method according to any embodiment of the present invention.

[0021] In this embodiment of the invention, an equivalent circuit for a metal oxide surge arrester in power equipment under DC voltage is established. This equivalent circuit is then converted into a mathematical model characterizing the surge arrester's volt-ampere characteristics under contamination conditions. This mathematical model contains unknown parameters. The parameters are fitted to the surge arrester under contamination conditions. If the fitting is successful, the operating state of the surge arrester is detected based on the learned model. By deriving a mathematical model to characterize the operating state of the surge arrester's surface contamination, and by fitting parameters to characterize the surface contamination, the specific value of the surface contamination is quantified based on these parameters. This eliminates the influence of surface contamination on the surge arrester, eliminating the need for manual wiping or electrical shielding. The degree of influence of surface contamination is stably quantified, improving the safety of surge arrester testing while also increasing work efficiency and the reliability of test results.

[0022] 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

[0023] 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.

[0024] Figure 1 This is a flowchart of a surge arrester detection method according to Embodiment 1 of the present invention;

[0025] Figure 2 This is an equivalent circuit diagram of a metal oxide surge arrester under power frequency operating voltage according to Embodiment 1 of the present invention;

[0026] Figure 3 This is an equivalent circuit diagram of a surge arrester under DC voltage according to Embodiment 1 of the present invention;

[0027] Figure 4 This is a surge arrester volt-ampere characteristic curve provided in Embodiment 1 of the present invention;

[0028] Figure 5 This is an example diagram of a boundary value provided in Embodiment 1 of the present invention;

[0029] Figure 6 This is a flowchart of an improved particle swarm optimization algorithm provided in Embodiment 1 of the present invention;

[0030] Figure 7 This is a data graph of the actual measured data of the metal oxide surge arrester in Experiment 1 according to Embodiment 1 of the present invention;

[0031] Figure 8 This is a result graph of fitting the data from Experiment 1 using the particle swarm optimization algorithm, as provided in Embodiment 1 of the present invention.

[0032] Figure 9 This is a graph showing the calculation results of the particle swarm algorithm in Experiment 1 provided according to Embodiment 1 of the present invention;

[0033] Figure 10 This is a data graph of experimentally measured data of a metal oxide surge arrester according to Embodiment 1 of the present invention;

[0034] Figure 11 This is a result graph of fitting the data from Experiment 2 using the particle swarm optimization algorithm, as provided in Embodiment 1 of the present invention.

[0035] Figure 12 This is a graph showing the calculation results of the particle swarm algorithm in Experiment 2 provided according to Embodiment 1 of the present invention;

[0036] Figure 13 This is a comparison chart of the calculation results of Experiment 1 and Experiment 2 provided in Embodiment 1 of the present invention;

[0037] Figure 14 This is a schematic diagram of the structure of a surge arrester detection device according to Embodiment 2 of the present invention;

[0038] Figure 15 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention. Detailed Implementation

[0039] 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.

[0040] 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.

[0041] Example 1

[0042] Figure 1 This is a flowchart of a surge arrester detection method provided in Embodiment 1 of the present invention. This embodiment is applicable to monitoring the operating status of surge arresters during live-line tests, quantifying the current status of contaminated portions on the surface of the surge arrester. This method can be executed by a surge arrester detection device, which can be implemented in hardware and / or software and can be configured in electronic equipment. Figure 1 As shown, the method includes:

[0043] Step 101: Establish the equivalent circuit of the metal oxide surge arrester in the power equipment under DC voltage.

[0044] Metal oxide surge arresters (MOAs) are important protective electrical devices used to protect the insulation of power transmission and transformation equipment from overvoltage damage. They have advantages such as fast response, flat volt-ampere characteristics, stable performance, large current capacity, low residual voltage, long life, and simple structure, and are widely used in power generation, transmission, transformation, and distribution systems. Composite-jacketed metal oxide surge arresters use silicone rubber composite materials for the jacket, and compared with traditional porcelain-jacketed surge arresters, they have advantages such as small size, light weight, robust structure, strong pollution resistance, and good explosion-proof performance.

[0045] Metal oxide surge arresters have the following problems during operation: sealing issues, poor anti-aging performance of the resistor elements, porcelain bushing contamination, and poor impact resistance.

[0046] The aging problem of seals in metal oxide surge arresters is mainly due to imperfect sealing technology used by the manufacturer or unstable anti-aging performance of the sealing materials. When there are large temperature differences or when the operating time is nearing the end of the metal oxide's lifespan, poor sealing can lead to moisture intrusion, causing a decrease in internal insulation levels, accelerating the deterioration of the resistor elements, and ultimately causing an explosion.

[0047] In the later stages of the service life of a metal oxide surge arrester, the deterioration of resistance causes an increase in leakage current, and may even cause internal discharge with the porcelain bushing. When the discharge is severe, the gas pressure and temperature inside the metal oxide surge arrester increase sharply, which may cause the metal oxide surge arrester body to explode. When the internal discharge is not too severe, it may cause a single-phase grounding of the system.

[0048] Metal oxide surge arresters often fail under conditions of operational overvoltage or lightning strikes. This is because during the manufacturing process, the poor quality control of various processes results in weak resistance to square wave impacts. In the process of frequently absorbing overvoltage energy, the resistance deteriorates and is damaged, losing its technical performance.

[0049] Aging and moisture absorption during operation can lead to increased leakage current and overheating of metal oxide surge arresters, and in severe cases, explosions. Therefore, it is necessary to regularly monitor the operating status of metal oxide surge arresters and further analyze the equivalent circuit of metal oxide surge arresters under DC voltage in power equipment.

[0050] Under DC voltage, the stray capacitance of metal oxide surge arresters can be ignored. Therefore, the leakage current flowing through the metal oxide surge arrester is related to the nonlinear resistance of the metal oxide surge arrester varistors and the insulation resistance of the surface sheath.

[0051] like Figure 2The diagram shows the equivalent circuit of a metal oxide surge arrester under power frequency operating voltage. R1 represents the nonlinear characteristics of the metal oxide varistor. C0 represents the equivalent capacitance of the metal oxide surge arrester cavity, sheath, and air. R0 represents the resistance of the metal oxide surge arrester cavity, sheath, and air. Under DC voltage, in steady state, the capacitive reactance C0 approaches infinity; therefore, at this time… Figure 2 The circuit shown can be transformed as follows Figure 3 The figure shows the equivalent circuit of a surge arrester under DC voltage.

[0052] Step 102: Convert the equivalent circuit into a mathematical model that characterizes the current-voltage characteristics of the surge arrester in the presence of pollution. The mathematical model contains unknown parameters.

[0053] Because the metal oxide varistors of metal oxide surge arresters have excellent varistor characteristics—that is, when the applied voltage does not exceed the threshold, their equivalent impedance is extremely high, and when the applied voltage exceeds the threshold, their equivalent impedance drops rapidly to achieve the purpose of current discharge during overvoltage—based on existing research on metal oxide surge arresters, they mainly have three operating states: pre-breakdown region, breakdown region, and recovery region.

[0054] When a metal oxide surge arrester is operating in the pre-breakdown region: when the applied voltage is small, electrons cannot pass through the Schottky barrier of the grain boundary layer. The flow of electrons mainly relies on the conductivity effect of the grain boundaries. Due to the extremely high resistance of the grain boundaries, the current is extremely small at this time, and its equivalent impedance has an approximately linear characteristic.

[0055] When a metal oxide surge arrester operates in the breakdown region: as the applied voltage continues to rise, the Schottky barrier of the grain boundary layer weakens under the influence of the applied electric field. Due to the "tunneling effect," more and more electrons begin to directly cross the Schottky barrier. When the applied voltage reaches a critical value, although the voltage variation range is small, the current can exhibit an order-of-magnitude change. Therefore, the nonlinear characteristics of the metal oxide surge arrester are also reflected in the breakdown region stage. When the metal oxide varistor deteriorates, the Schottky barrier decreases, and electrons can more easily cross the barrier.

[0056] Schottky barrier refers to a metal-semiconductor contact with rectifying characteristics, that is, a metal-semiconductor contact with a large barrier height and a doping concentration lower than the density of states in the conduction band or valence band. Just like a diode has rectifying characteristics, it is a region with rectifying effect formed on the metal-semiconductor boundary.

[0057] When a metal oxide surge arrester is operating in the recovery region, the Schottky barrier is ineffective due to the strong electric field. At this point, the voltage acts directly on the metal oxide grains, and the leakage current exhibits a linear relationship with the voltage. The 1 mA DC test applied to the metal oxide surge arrester in the field is primarily to test its initial operating voltage. The applied voltage range is located in the pre-breakdown region of the expected breakdown area. The nonlinear characteristics of the metal oxide varistor are also evident in this part. Therefore, when studying the degradation problem of metal oxide surge arresters, only this part needs to be studied.

[0058] The equivalent circuit is transformed into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution. The mathematical model contains unknown parameters. The current-voltage characteristics of the metal oxide surge arrester are represented by the following mathematical model:

[0059]

[0060] Where I represents the current flowing through the metal oxide surge arrester, and U represents the terminal voltage of the metal oxide surge arrester. 1mA The reference voltage of the metal oxide surge arrester under a DC 1mA test is also called the starting operating voltage. k is a constant coefficient, and a represents the parameter that determines the nonlinear characteristics of the metal oxide surge arrester varistor.

[0061] Under normal circumstances, the larger the value of 'a', the greater the current gradient and the better the nonlinear characteristics of the metal oxide surge arrester during operation.

[0062] like Figure 4 The figure shows the volt-ampere characteristic diagram of a metal oxide surge arrester when the constant coefficient k is equal to 1000 and the parameter a, which determines the nonlinear characteristics of the varistor, takes different values. Figure 4 As shown, the larger the value of 'a', the better the characteristics of the metal oxide surge arrester; that is, the impedance is as large as possible when the voltage is below the threshold voltage, and as small as possible once the voltage exceeds the threshold voltage. When 'a' = 1, it represents linear impedance, at which point the metal oxide surge arrester will completely lack varistor characteristics. Most materials have an volt-ampere characteristic approximately 1, making it impossible to achieve characteristics similar to those of the metal oxide varistors in metal oxide surge arresters.

[0063] like Figure 3 As shown, in the equivalent circuit of a metal oxide surge arrester under DC voltage, the leakage current of the metal oxide surge arrester consists of two parts. One part is the current flowing through the varistor, and the other part is the current flowing through the insulating materials such as the metal oxide surge arrester sheath. In actual production sites, due to the contamination of the metal oxide surge arrester surface, this part of the current is mainly the leakage current from the surface of the metal oxide surge arrester sheath. Therefore, according to... Figure 3The equivalent circuit shown can be converted into a mathematical model characterizing the volt-ampere characteristics of the surge arrester in the presence of pollution using the following formula:

[0064]

[0065] Where a1, a2, k1, and k2 are all unknown parameters, a1 represents the leakage current parameter on the surface of the arrester's sheath, k1 represents the constant coefficient of the leakage current on the surface of the arrester's sheath, a2 represents the nonlinear parameter of the arrester's valve plate, k2 represents the constant coefficient of the leakage current flowing through the arrester's valve plate, and U represents the terminal voltage of the arrester. 1mA This indicates the initial operating voltage of the surge arrester.

[0066] When the surface of the metal oxide surge arrester is clean, k1 is close to 0, and the leakage current can be ignored. The more severe the contamination of the metal oxide surge arrester's surface, the larger the value of k2. The value of a1 will not be too large. When the metal oxide surge arrester passes the DC 1 mA test, the current flowing through the metal oxide surge arrester's varistor needs to be obtained. The leakage current flowing through the contamination on the surface of the metal oxide surge arrester's sheath is an interference factor. Therefore, during the field test, the interference caused by the leakage current is further eliminated by wiping or shielding measures, along with mathematical models.

[0067] Step 103: Fit parameters of the surge arrester under polluted conditions.

[0068] Furthermore, after eliminating the interference caused by leakage current on the surface of the metal oxide surge arrester and obtaining a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, the unknown parameters in the mathematical model are fitted to the metal oxide surge arrester under the condition of pollution.

[0069] In one embodiment of the present invention, step 103 may include the following steps:

[0070] S31. Initialize the particle swarm using parameters as particles. Each particle is configured with position and velocity.

[0071] Particle Swarm Optimization (PSO) is a stochastic search algorithm based on swarm intelligence, developed by simulating the foraging behavior of bird flocks. It is often considered a form of swarm intelligence. In PSO, each solution to the optimization problem is a particle in the search space. All particles have a fitness value determined by an optimized function, and each particle also has a velocity that determines its direction and distance. Each particle then searches the solution space by following the currently optimal particle.

[0072] Furthermore, in this embodiment, the parameters can be detected based on a binary particle swarm optimization algorithm. The parameters are treated as particles, and a particle swarm is initialized, with each particle configured with position and velocity. The parameters are initialized as a group of random particles, and then the optimal solution is found iteratively. In each iteration, the particles update themselves by tracking two extreme values. The first extreme value is the optimal solution found by the particle itself, called the individual extreme value. The other extreme value is the optimal solution found by the entire population, which is the global extreme value. Alternatively, instead of using the entire population, only a subset of the best particles' neighbors can be used; in this case, the extreme value among all neighbors is the local extreme value.

[0073] Specifically, the particle swarm optimization algorithm is applied to solve the leakage current localization problem in metal oxide surge arresters. The position of the particle represents the state of the metal oxide surge arrester, and the dimension of the particle represents the total number of feeder segments of the metal oxide surge arrester surface. Each feeder segment has two states, 0 and 1, where 0 represents the normal state and 1 represents the state of the metal oxide surge arrester surface. The state of the feeder segment is the variable to be determined.

[0074] Therefore, solving for the state of the N-terminal feeder segment is transformed into solving an N-dimensional particle swarm optimization problem. The N-dimensional position of each particle represents the potential state of the N-terminal feeder segment of the metal oxide surge arrester. In each iteration, the merits of each particle's position are evaluated using an evaluation function, updating the current optimal position of each particle and the optimal position of all particles, and subsequently updating the particle's velocity and position, until the program termination condition is met. The final globally optimal position of the particle swarm is the actual state of each feeder segment.

[0075] Therefore, the problem of locating surface contamination in metal oxide surge arresters can be transformed into an optimization problem, which can then be solved using a particle swarm optimization algorithm to construct a reasonable function. This constructed function should be able to evaluate the quality of each particle's position, ultimately iterating to find the solution that best explains the location information of the contamination on the metal oxide surge arrester surface.

[0076] In binary-based particle swarm optimization, particle position is encoded in binary form, meaning that each dimension of the particle position is restricted to 0 or 1, and the particle's velocity is interpreted as the probability of position change.

[0077] S32. Set the objective function for particles in the presence of pollution in the surge arrester.

[0078] In on-site sampling, the terminal voltage and leakage current values ​​of the metal oxide surge arrester are acquired during a 1 mA test. A higher number of sampling points improves the accuracy of the particle swarm optimization (PSO) algorithm. Specifically, higher accuracy in measuring the leakage current leads to higher recognition accuracy of the PSO algorithm. Figure 4As shown in the voltage-current characteristic curve of the metal oxide surge arrester, when the voltage applied to the metal oxide surge arrester is less than a certain value, the total leakage current of the metal oxide surge arrester is very small.

[0079] In this embodiment, based on the principle that the predicted leakage current of the metal oxide surge arrester corresponding to the actual state of each feeder section should be minimized compared with the measured leakage current of the metal oxide surge arrester, the following objective function can be constructed, that is, the following objective function is set for particles under the condition that the surge arrester is polluted:

[0080]

[0081] Where G represents the objective function, a1, a2, k1, and k2 are all unknown parameters, a1 represents the leakage current parameter on the surface of the arrester's sheath, k1 represents the constant coefficient of the leakage current on the surface of the arrester's sheath, a2 represents the nonlinear parameter of the arrester's valve plate, k2 represents the constant coefficient of the leakage current flowing through the arrester's valve plate, f represents the mathematical model, and I T This represents the current detected by the surge arrester when pollution is present, where i represents the number of currents detected.

[0082] S33. Set a boundary for particles in the presence of pollution in the surge arrester.

[0083] The particles include the leakage current parameters of the surge arrester's sheath surface, the constant coefficient of the leakage current of the surge arrester's sheath surface, the nonlinear parameters of the surge arrester's valve plate, and the constant coefficient of the leakage current flowing through the valve plate of the surge arrester.

[0084] To improve the convergence speed and effectiveness of the particle swarm optimization algorithm, parameter boundaries can be appropriately set according to the meaning of each parameter. For example, k1 is related to the degree of contamination on the surface of the metal oxide surge arrester sheath. The more severe the contamination, the larger the value of k1. Therefore, the k1 boundary can be fuzzily set during the experiment by combining the service life of the metal oxide surge arrester with the observed degree of surface contamination.

[0085] The boundary of the leakage current constant coefficient on the sheath surface of the surge arrester is determined by the following formula:

[0086]

[0087] Where k1 represents the constant coefficient of leakage current on the sheath surface of the surge arrester, n represents the index dimension, and x i Let represent the value of the i-th indicator, and x represent the indicator mean.

[0088] Furthermore, a first range is set as the boundary for the leakage current parameter on the sheath surface of the surge arrester, a second range is set as the boundary for the nonlinear parameter of the surge arrester's valve plate, and a third range is set as the boundary for the constant coefficient of the leakage current flowing through the valve plate of the surge arrester.

[0089] like Figure 5 The leakage current constant coefficient of the surge arrester sheath surface is determined by different boundary values ​​under different conditions, such as operating time, surface contamination, and height from the coastline.

[0090] For example, such as Figure 5 As shown, when the operation time of the surge arrester is less than 6 years, the upper limit of k1 is 10 and the lower limit is 1. When the value of k1 is between the lower limit and the upper limit, the fuzzy evaluation of the surface condition of the surge arrester is excellent.

[0091] For example, such as Figure 5 As shown, when the surface of the surge arrester is slightly dirty, the upper limit of k1 is 100 and the lower limit is 1. When the value of k1 is between the lower limit and the upper limit, the surface condition of the surge arrester is fuzzily evaluated as good.

[0092] For example, such as Figure 5 As shown, when the height of the surge arrester above the coastline is more than 100 kilometers, the upper limit of k1 is 10 and the lower limit is 1. When the value of k1 is between the lower limit and the upper limit, the fuzzy evaluation of the surface state of the surge arrester is excellent.

[0093] S34. Calculate the fitness value of a particle based on its position.

[0094] The result of the objective function represents the fitness value corresponding to each potential solution. The smaller the result, the better the fitness value. The fitness value of the particle is calculated based on the particle's position using the objective function.

[0095] In this embodiment, in each iteration, the fitness values ​​of each particle can be traversed, and the position of each particle can be evaluated by the fitness values. Thus, the individual extreme value of each particle (i.e., the current optimal position of the particle) and the group extreme value of the particle swarm (i.e., the optimal position of all particles, also known as the global extreme value) are updated according to the fitness values.

[0096] On the one hand, the fitness value of a particle is compared with the fitness value of the particle's individual extreme value, where the particle's individual extreme value is initially empty.

[0097] If a particle's fitness value is greater than the fitness value corresponding to its individual extreme value, then in this iteration, the particle's position is updated to the particle's individual extreme value.

[0098] If the fitness value of a particle is less than the fitness value corresponding to the particle's individual extreme value, then the particle's individual extreme value remains unchanged in this round of iteration.

[0099] The fitness value of a particle is compared with the fitness value corresponding to the population extremum of the particle swarm, where the population extremum of the particle swarm is initially empty.

[0100] If a particle's fitness value is greater than the fitness value corresponding to the swarm's population extremum, then in this iteration, the particle's position is updated to the swarm's population extremum.

[0101] If the fitness value of a particle is less than the fitness value corresponding to the population extremum of the particle swarm, then the population extremum of the particle swarm will remain unchanged in this round of iteration.

[0102] S35. Identify particle types based on fitness values.

[0103] S36. Iterate through the fitness values ​​of the particles to update the individual extreme values ​​of each particle and the population extreme values ​​of the particle swarm within the boundary.

[0104] Iterate through the fitness values ​​of the particles to update the individual extreme values ​​of each particle and the population extreme values ​​of the particle swarm within the boundary.

[0105] S37. Classify particles according to their fitness values.

[0106] The particles are sorted according to their fitness values ​​from largest to smallest to obtain a sequence. Specific quantiles are determined within the sequence. Particles ranked above a quantile are classified as active particles. Particles ranked below a quantile are classified as lazy particles, where the number of active particles is less than the number of lazy particles.

[0107] In this embodiment, particles are divided into two types: active particles and lazy particles. The position update method for lazy particles is the same as that of the particle swarm optimization (PSO) algorithm, while the position update for active particles is a random jump to enhance the global optimization capability of the PSO algorithm. The PSO algorithm distinguishes between lazy and active particles according to a certain ratio. Assuming active particles account for 20% of the population and lazy particles account for 80%, the distinction is based on the particle fitness value. The top 20% of particles with fitness values ​​are considered active particles, and the bottom 80% are considered lazy particles.

[0108] S38. Update the particle velocity based on the individual extreme value and the group extreme value.

[0109] Suppose we have a D-dimensional target search space, and the particle swarm optimization algorithm randomly initializes a swarm of m particles. The position X of the i-th particle (a potential solution to the optimization problem) can be represented as {x i1 x i2 , ..., x iD Substituting this into the evaluation function yields the fitness value, which measures the particle's quality. The corresponding flight velocity V can be expressed as {v i1 v i2 , ..., v iDIn each iteration, the particle updates its velocity and position by tracking two extreme values: one extreme value is the best solution the particle has found so far, i.e., the individual extreme value P. ibest , represented as {P ibest.1 P ibest.2 ..., P ibest.D The other extreme value is the optimal solution found by the swarm of particles so far, i.e., the swarm extreme value, denoted as {P}. gbest.1 P gbest.2 ..., P gbest.D}

[0110] In the swarm iteration, particles search for the optimal particle in the solution space. In this embodiment, an improvement is proposed to the particle swarm optimization algorithm. The search range of the solution space is adjusted by using the distance between the particle's position and the position where the fault occurs, thereby converging the search range and improving the search accuracy. Under the condition of limiting the search range, the particle velocity is updated according to the individual extreme value and the swarm extreme value.

[0111] S39. If the particle velocity update is complete, then update the particle position according to the particle type.

[0112] If the particle velocity update is complete, and if the particle type is a lazy particle, the particle velocity can be added to the particle's position to obtain the new particle position. The position of the lazy particle is then updated using the following formula:

[0113]

[0114] Where i is the index of the lazy particle, d is the dimension index of the lazy particle, k is the iteration number, w is the inertia weight, c1 is the learning weight, c2 is the population learning weight, and r1 and r2 are both random numbers. Let be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration. Let be the velocity of the d-dimensional vector of the lazy particle i at the k-th iteration. Let i be the position of the d-dimensional vector of the lazy particle i in the k-th iteration. Let be the individual extreme value of the d-dimensional vector of lazy particle i at the k-th iteration. The population extremum of the d-dimensional vector of all lazy particles at the k-th iteration;

[0115] If the particle type is an active particle, then the position of the active particle is updated using the following formula:

[0116]

[0117] Where T is the maximum number of iterations. Let r3 be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration, where r3 is a random number. Let be the ranking of the fitness of the i-th active particle in the k-th iteration. Let i be the position of the d-dimensional vector of the active particle i in the k-th iteration. Let be the velocity of the d-dimensional vector of active particle i at the k-th iteration.

[0118] S40. Determine whether the current iteration count has reached the preset threshold, or whether the output value of the objective function has converged. If yes, proceed to step S41; otherwise, return to step S34.

[0119] Furthermore, in this embodiment, a threshold for ending the iteration can be preset as the termination condition for the iteration to complete. For example, the number of iterations reaches a preset maximum number, satisfying the threshold for the optimal fitness value.

[0120] Alternatively, the output values ​​of the objective function can be compared with each other, the difference between the output values ​​of every two objective functions can be calculated, and it can be determined whether the difference between the differences is large. If the difference is large, then S34 is executed; if the difference is small, it can be determined that the output value of the objective function has converged, then S41 is executed.

[0121] In the DC 1 mA test of metal oxide surge arresters in the production field, it is necessary to eliminate the leakage current on the surface of the metal oxide surge arrester sheath. The difference between the current of the metal oxide surge arrester varistor and the surface leakage current lies in the fact that the metal oxide surge arrester varistor has better nonlinear characteristics, while the equivalent resistance of the surface contamination of the metal oxide surge arrester is closer to a linear state. Based on the measured data of the DC 1 mA test of the metal oxide surge arrester in the field, an improved particle swarm optimization algorithm is used to identify the parameters. Finally, the actual current flowing through the metal oxide surge arrester is obtained by subtracting the surface leakage part from the measured current value, thus achieving the elimination of surface contamination interference. Through on-site data sampling, the terminal voltage and leakage current values ​​of the metal oxide surge arrester during the DC 1 mA test are obtained.

[0122] For example, such as Figure 6The flowchart of the improved particle swarm optimization algorithm is shown. After sampling data on site, the boundary of the constant coefficient parameter k is set based on the operating time and pollution level of the metal oxide arrester. The boundary of the parameter a, which determines the nonlinear characteristics of the metal oxide arrester varistor, is set based on historical test records. The parameters are then identified based on the improved particle swarm optimization algorithm. The fitness value of the parameters is judged to determine whether the identified parameters k and a converge. If the identified parameters k and a converge, the current flowing through the polluted part of the surface of the metal oxide arrester is obtained through the parameters k and a and the mathematical model. The actual test value of the metal oxide in the DC 1 mA test is then obtained.

[0123] Specifically, firstly, the terminal voltage and leakage current values ​​of the metal oxide surge arrester during a 1 mA DC test are obtained on-site, with as many sampling points as possible to increase the identification accuracy of the particle swarm optimization algorithm. Furthermore, the measurement accuracy of the leakage current is required to be at least ±0.5 μA. Figure 4 The voltage-current characteristic curve of the metal oxide surge arrester shown can be seen from the following: when the voltage applied to the metal oxide surge arrester is less than 50% U 1mA At that time, the total leakage current of metal oxide lightning arresters is very small, therefore, it is limited by the current sampling accuracy and is less than 50% U. 1mA Voltage data is largely unusable. Therefore, to improve the efficiency of the particle swarm optimization algorithm and conserve hardware resources, the initial sampling point can be set at a value greater than 50% of the voltage U. 1mA Start until the voltage is 100% U 1mA The voltage range is sampled. The sampling interval is approximately set to 0.5% of the voltage. 1mA A smaller sampling interval is more beneficial for improving the computational accuracy of the particle swarm optimization algorithm. The sampling sequence can be recorded and stored by a microcomputer; the voltage and current sampling sequences are denoted as U, respectively. P and I T Further, an objective function is constructed, and the boundaries of parameter k are set based on the running time, the degree of pollution of the metal oxide surge arrester, and the height from the coastline. Parameters k and a are identified through the objective function. The identified parameters k and a are then applied to an improved particle swarm optimization algorithm to obtain the position updates of lazy and active particles in the parameters. The fitness of active and lazy particles is further calculated, and the corresponding fitness values ​​are judged according to the objective function to determine whether they satisfy the minimum value of the objective function, i.e., the global optimum. If so, the optimal set of parameters a and k is obtained. Then, the surface interference leakage current and the internal valve plate current of the metal oxide surge arrester are calculated using a mathematical model characterizing the volt-ampere characteristics of the metal oxide surge arrester under pollution conditions. The internal valve plate current of the metal oxide surge arrester is then determined.

[0124] S41. Assign parameters to the population extrema of the particle swarm.

[0125] Step 104: If the fitting is complete, the operating status of the surge arrester is detected based on the learned model.

[0126] If the fitting is complete, the operating status of the surge arrester is detected based on the learned model.

[0127] If the particle position update is complete, it is possible to check whether the termination condition is met. If no termination condition is met, the next iteration can be performed. If any termination condition is met, the particle corresponding to the population extremum can be output as the leakage current of the metal arrester.

[0128] For example, such as Figure 7 The data from Experiment 1, conducted on a metal oxide surge arrester under a DC 1 mA test, involved performing normal testing on the same metal oxide surge arrester. Further, the surface of the metal oxide surge arrester was cleaned to ensure sufficient insulation of its outer sheath. After wiping, the insulation resistance of the metal oxide surge arrester was measured to be 34.7 ohms. A DC volt-ampere characteristic test was then performed, yielding the following results: Figure 7 The test data is shown below. An improved particle swarm optimization algorithm is used to fit the measured test data to obtain the following results: Figure 8 The fitting results are shown below. The results of the improved particle swarm optimization algorithm are as follows: Figure 9 As shown in the figure. The calculation results show that the root mean square error between the fitting calculation result and the measured value of the method in this embodiment is 4.14. This error includes the instrument's measurement error. As the instrument's measurement accuracy improves, the error will further decrease. The value of k1 is 2.85, indicating that the surface insulation of the metal oxide surge arrester is good. The nonlinear characteristic a2 of the metal oxide surge arrester is 24.69, indicating that the MOA valence plate is in good condition.

[0129] For example, the surface of a metal oxide surge arrester was artificially contaminated. After the contamination, the insulation resistance of the metal oxide surge arrester was measured to be 17.2 ohms, indicating a significant decrease in surface insulation resistance. A DC volt-ampere characteristic test was then performed, and the test data for Experiment 2 were obtained as follows: Figure 10 As shown in the figure. The improved particle swarm optimization algorithm of this invention is used to fit the test data, and the fitting result is as follows. Figure 11 As shown. The results of the improved particle swarm optimization algorithm are as follows. Figure 12 As shown in the algorithm results, the k1 parameter, which characterizes the insulation size of the metal oxide surface, increases significantly, indicating that the surface leakage increases.

[0130] like Figure 13 The results of the comparison between Experiment 1 and Experiment 2 were obtained by... Figure 13 The comparison results show that surface contamination can interfere with the DC 1 mA test of metal oxide surge arresters. For U...1mA There is virtually no impact because when the voltage is close to the threshold, the leakage current is mainly due to the current passing through the valve plate via the "tunneling effect," with surface contamination contributing very little. However, at lower voltages (0.75 times U), the leakage current is significantly reduced. 1mA At voltage levels, because the varistor of the metal oxide surge arrester is in good condition, the leakage current is mainly composed of the leakage current flowing through the surface of the metal oxide surge arrester. With the accumulation of pollutants, I... 75％U1mA This will increase exponentially, severely affecting the accuracy of the experimental results. However, after adopting the method of the embodiments of the present invention, the I values ​​of experiments one and two are significantly reduced. 75％U1mA The degree of value variation is significantly reduced. This eliminates the influence of surface leakage circuits in metal oxide surge arresters and improves the accuracy of DC 1 mA tests on metal oxide surge arresters.

[0131] In embodiments of this invention, an equivalent circuit for a metal oxide surge arrester in power equipment under DC voltage is established. This equivalent circuit is then converted into a mathematical model characterizing the surge arrester's volt-ampere characteristics under contamination conditions. This mathematical model contains unknown parameters. The parameters are fitted to the surge arrester under contamination conditions. If the fitting is successful, the operating state of the surge arrester is detected based on the learned model. By deriving a mathematical model to characterize the operating state of the surge arrester's surface contamination, and by fitting parameters to characterize the surface contamination, the specific value of the surface contamination is quantified based on these parameters. This eliminates the influence of surface contamination on the surge arrester, eliminating the need for manual wiping or electrical shielding. The degree of influence of surface contamination is stably quantified, improving the safety of surge arrester testing while also increasing work efficiency and the reliability of test results.

[0132] Example 2

[0133] Figure 14 This is a schematic diagram of the structure of a surge arrester detection device provided in Embodiment 3 of the present invention. Figure 14 As shown, the device includes:

[0134] Equivalent circuit establishment module 1401 is used to establish the equivalent circuit of metal oxide surge arresters in power equipment under DC voltage.

[0135] Equivalent circuit conversion module 1402 is used to convert the equivalent circuit into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, wherein the mathematical model has unknown parameters;

[0136] The parameter fitting module 1403 is used to fit the parameters of the surge arrester under the condition of pollution.

[0137] The operation status detection module 1404 is used to detect the operation status of the surge arrester according to the learning model if the fitting is completed.

[0138] In one embodiment of the present invention, the equivalent circuit conversion module 1402 includes:

[0139] The mathematical model representation module is used to convert the equivalent circuit into a mathematical model representing the volt-ampere characteristics of the surge arrester in the presence of pollution using the following formula:

[0140]

[0141] Where a1, a2, k1, and k2 are all unknown parameters, a1 represents the leakage current parameter on the sheath surface of the surge arrester, k1 represents the constant coefficient of the leakage current on the sheath surface of the surge arrester, a2 represents the nonlinear parameter of the valve plate of the surge arrester, k2 represents the constant coefficient of the leakage current flowing through the valve plate of the surge arrester, and U represents the terminal voltage of the surge arrester. 1mA This indicates the initial operating voltage of the surge arrester.

[0142] In one embodiment of the present invention, the parameter fitting module 1403 includes:

[0143] A particle swarm initialization module is used to initialize a particle swarm using the parameters as particles, wherein the particles are configured with position and velocity.

[0144] The objective function setting module is used to set the objective function for the particles in the arrester under the condition of pollution.

[0145] A particle boundary setting module is used to set a boundary for the particles in the surge arrester under the condition of contamination.

[0146] The particle fitness calculation module is used to calculate the fitness value of the particle based on its position.

[0147] A particle type identification module is used to identify the type of particle according to the fitness value;

[0148] The particle fitness traversal module is used to traverse the fitness values ​​of the particles in order to update the individual extreme value of each particle and the population extreme value of the particle swarm within the boundary.

[0149] A particle classification module is used to classify the particles according to the fitness value;

[0150] A particle velocity update module is used to update the velocity of the particles based on the individual extreme value and the group extreme value.

[0151] The particle position update module is used to update the position of the particle according to the particle type if the velocity update of the particle is completed.

[0152] The threshold judgment module is used to determine whether the current number of iterations has reached a preset threshold, or whether the output value of the objective function has converged. If yes, the parameter assignment module is executed; otherwise, the execution of the particle fitness calculation module is returned.

[0153] The parameter assignment module is used to assign the population extremum of the particle swarm to the parameter.

[0154] In one embodiment of the present invention, the objective function setting module includes:

[0155] The objective function setting submodule is used to set the following objective function for the particles when the surge arrester is in the presence of pollution:

[0156]

[0157] Where G represents the objective function, a1, a2, k1, and k2 are all unknown parameters, a1 represents the leakage current parameter on the sheath surface of the surge arrester, k1 represents the constant coefficient of the leakage current on the sheath surface of the surge arrester, a2 represents the nonlinear parameter of the valve plate of the surge arrester, k2 represents the constant coefficient of the leakage current flowing through the valve plate of the surge arrester, f represents the mathematical model, and I T This represents the current detected by the surge arrester when pollution is present, where i represents the number of detections.

[0158] In one embodiment of the present invention, the particle includes the leakage current parameter of the sheath surface of the surge arrester, the constant coefficient of the leakage current of the sheath surface of the surge arrester, the nonlinear parameter of the valve plate of the surge arrester, the constant coefficient of the leakage current flowing through the valve plate of the surge arrester, and the particle boundary setting module includes:

[0159] The particle boundary setting submodule is used to determine the boundary of the leakage current constant coefficient on the sheath surface of the surge arrester using the following formula:

[0160]

[0161] Where k1 represents the constant coefficient of leakage current on the sheath surface of the surge arrester, n represents the index dimension, and x i Let represent the value of the i-th indicator, and x represent the indicator mean.

[0162] The first range boundary setting module is used to set the first range as a boundary for the leakage current parameter of the sheath surface of the surge arrester.

[0163] The second range boundary setting module is used to set the second range as a boundary for the nonlinear parameters of the valve plate of the surge arrester;

[0164] The third range boundary setting module is used to set the third range as the boundary for the constant coefficient of the leakage current of the valve plate flowing through the surge arrester.

[0165] In one embodiment of the present invention, the particle classification module includes:

[0166] The particle sequence acquisition module is used to sort the particles from largest to smallest according to their fitness values ​​to obtain a sequence.

[0167] The sequence is divided into a point determination module, which is used to determine the specified quantiles in the sequence;

[0168] An active particle classification module is used to classify the particles sorted above the quantile points as active particles.

[0169] The lazy particle classification module is used to classify the particles sorted below the quantile as lazy particles, wherein the number of active particles is less than the number of lazy particles.

[0170] In one embodiment of the present invention, the particle position update module includes:

[0171] The lazy particle position update module is used to update the position of the lazy particle using the following formula if the particle type is the lazy particle:

[0172]

[0173] Where i is the index of the lazy particle, d is the dimension index of the lazy particle, k is the iteration number, w is the inertia weight, c1 is the learning weight, c2 is the population learning weight, and r1 and r2 are both random numbers. Let i be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration. Let be the velocity of the d-dimensional vector of the lazy particle i at the k-th iteration. Let i be the position of the d-dimensional vector of the lazy particle i in the k-th iteration. Let i be the individual extreme value of the d-dimensional vector of the lazy particle i in the k-th iteration. The population extremum of the d-dimensional vector of all the lazy particles at the k-th iteration;

[0174] The active particle position update module is used to update the position of the active particle using the following formula if the particle type is the active particle:

[0175]

[0176] Where T is the maximum number of iterations. Let r3 be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration, where r3 is a random number. The fitness ranking of the i-th active particle in the k-th iteration is given by the following formula: Let i be the position of the d-dimensional vector of the active particle i in the k-th iteration. Let be the velocity of the d-dimensional vector of the active particle i at the k-th iteration.

[0177] The surge arrester detection device provided in this embodiment of the invention can execute the surge arrester detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the surge arrester detection method.

[0178] Example 3

[0179] Figure 15 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. 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.

[0180] like Figure 15 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.

[0181] 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.

[0182] 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 surge arrester detection method.

[0183] In some embodiments, the surge arrester detection 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 detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the surge arrester detection method by any other suitable means (e.g., by means of firmware).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] Example 4

[0191] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the surge arrester detection method provided in any embodiment of this invention.

[0192] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0193] 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.

[0194] 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 for testing a surge arrester, characterized in that, When applied to a surge arrester under power outage conditions, the method includes: The equivalent circuit of a metal oxide surge arrester in power equipment is established under DC voltage; under the DC voltage, the leakage current of the equivalent circuit of the metal oxide surge arrester includes the current flowing through the valve plate and the current flowing through the metal oxide surge arrester sheath. The equivalent circuit is converted into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, wherein the mathematical model has unknown parameters; The parameters are fitted to the surge arrester under polluted conditions; If the fitting is successful, the operating status of the surge arrester is detected based on the mathematical model. The step of converting the equivalent circuit into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution includes: The equivalent circuit is converted into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution using the following formula: in, , , , All of these are unknown parameters. This represents the leakage current parameter on the sheath surface of the surge arrester. This represents the leakage current constant coefficient on the sheath surface of the surge arrester. This represents the nonlinear parameters of the valve plate of the surge arrester. The constant coefficient represents the leakage current flowing through the valve plate of the surge arrester, and U represents the terminal voltage of the surge arrester. This indicates the initial operating voltage of the surge arrester.

2. The method according to claim 1, characterized in that, The fitting of the parameters to the surge arrester under polluted conditions includes: The particle swarm is initialized using the parameters described above as particles, wherein the particles are configured with position and velocity. The target function is set for the particles in the presence of pollution in the surge arrester; The surge arrester sets a boundary for the particles in the presence of contamination. The fitness value of the particle is calculated based on its position; The type of particle is identified according to the fitness value; The fitness values ​​of the particles are iterated to update the individual extreme value of each particle and the population extreme value of the particle swarm within the boundary. The particles are classified into different types based on their fitness values. The velocity of the particle is updated based on the individual extreme value and the group extreme value; If the velocity of the particle is updated, then the position of the particle is updated according to the type of particle; Determine whether the current number of iterations has reached a preset threshold, or whether the output value of the objective function has converged. If yes, assign the population extremum of the particle swarm to the parameter; otherwise, return to the step of calculating the fitness value of the particle based on its position.

3. The method according to claim 2, characterized in that, The step of setting a target function for the particles in the presence of pollution in the surge arrester includes: The following objective function is set for the particles in the presence of pollution in the surge arrester: in, G represents the objective function. , , , All of these are unknown parameters. This represents the leakage current parameter on the sheath surface of the surge arrester. This represents the leakage current constant coefficient on the sheath surface of the surge arrester. This represents the nonlinear parameters of the valve plate of the surge arrester. The constant coefficient representing the leakage current flowing through the valve plate of the surge arrester. Representing a mathematical model, This represents the current detected by the surge arrester when pollution is present, where i represents the number of detections.

4. The method according to claim 2, characterized in that, The particles include the leakage current parameters of the surge arrester's sheath surface, the constant coefficient of the leakage current of the surge arrester's sheath surface, the nonlinear parameters of the surge arrester's valve plate, and the constant coefficient of the leakage current flowing through the valve plate of the surge arrester. The step of setting a boundary for the particles under the condition that the surge arrester is contaminated includes: The boundary of the leakage current constant coefficient on the sheath surface of the surge arrester is determined by the following formula: in, This represents the constant coefficient of leakage current on the sheath surface of the surge arrester, where n represents the index dimension. This represents the value of the i-th indicator. This represents the mean of the indicator; The first range of the leakage current parameter on the sheath surface of the surge arrester is set as the boundary; The nonlinear parameters of the surge arrester's valve plate are set to a second range as a boundary; The constant coefficient of the leakage current of the valve plate flowing through the surge arrester is set to a third range as the boundary.

5. The method according to claim 2, characterized in that, The classification of particles according to the fitness value includes: The particles are sorted from largest to smallest according to their fitness values ​​to obtain a sequence. The specified quantiles are determined in the sequence; The particles ranked above the quantile points are classified as active particles; The particles sorted below the quantile are classified as lazy particles, wherein the number of active particles is less than the number of lazy particles.

6. The method according to claim 5, characterized in that, Updating the position of the particle according to the type of particle includes: If the particle is a lazy particle, then the position of the lazy particle is updated using the following formula: Where i is the index of the lazy particle, d is the dimension index of the lazy particle, k is the iteration number, and w is the inertia weight. For learning weights, For population learning weights, , All are random numbers. Let i be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration. Let be the velocity of the d-dimensional vector of the lazy particle i at the k-th iteration. Let i be the position of the d-dimensional vector of the lazy particle i in the k-th iteration. Let i be the individual extreme value of the d-dimensional vector of the lazy particle i in the k-th iteration. The population extremum of the d-dimensional vector of all the lazy particles at the k-th iteration; If the particle is an active particle, then the position of the active particle is updated using the following formula: Where T is the maximum number of iterations. Let i be the position of the d-dimensional vector of the lazy particle i in the (k+1)th iteration. It is a random number. The fitness value of the i-th active particle in the k-th iteration is ranked in the sequence. Let i be the position of the d-dimensional vector of the active particle i in the k-th iteration. Let be the velocity of the d-dimensional vector of the active particle i at the k-th iteration.

7. A detection device for a surge arrester, characterized in that, The device, applied under the condition of a surge arrester power failure, includes: An equivalent circuit establishment module is used to establish an equivalent circuit for a metal oxide surge arrester in a power equipment under a DC voltage; under the DC voltage, the leakage current of the equivalent circuit of the metal oxide surge arrester includes the current flowing through the valve plate and the current flowing through the metal oxide surge arrester sheath. An equivalent circuit conversion module is used to convert the equivalent circuit into a mathematical model characterizing the current-voltage characteristics of the surge arrester in the presence of pollution, wherein the mathematical model has unknown parameters. A parameter fitting module is used to fit the parameters of the surge arrester under conditions of contamination. The operation status detection module is used to detect the operation status of the surge arrester according to the mathematical model if the fitting is completed. The equivalent circuit conversion module includes: The mathematical model representation module is used to convert the equivalent circuit into a mathematical model representing the volt-ampere characteristics of the surge arrester in the presence of pollution using the following formula: ； in, , , , All of these are unknown parameters. This represents the leakage current parameter on the sheath surface of the surge arrester. This represents the leakage current constant coefficient on the sheath surface of the surge arrester. This represents the nonlinear parameters of the valve plate of the surge arrester. The constant coefficient represents the leakage current flowing through the valve plate of the surge arrester, and U represents the terminal voltage of the surge arrester. This indicates the initial operating voltage of the surge arrester.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the surge arrester detection method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the detection method for the surge arrester according to any one of claims 1-6.

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