Traction network lightning arrester health state assessment method, device and equipment and storage medium

Abstract

The invention relates to a traction network lightning arrester health state assessment method and device, equipment and a storage medium. The method comprises the following steps: acquiring a leakage current signal and a signal decomposition parameter of a lightning arrester of a traction network; based on the signal decomposition parameter, performing signal decomposition on the leakage current signal to obtain a plurality of modal components and a center frequency of each modal component; screening out a target modal component of which the center frequency is matched with a preset target center frequency range from each modal component; reconstructing the target modal component to obtain an equivalent resistive leakage current signal; and respectively determining the amplitude change rate between each harmonic wave and the fundamental wave in the equivalent resistive leakage current signal, and evaluating the health state of the lightning arrester according to each amplitude change rate to obtain a health state evaluation result. The method is beneficial to improving the accuracy of lightning arrester health state evaluation.

Description

Technical Field

[0001] This application relates to the field of equipment condition monitoring technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the health status of traction network surge arresters. Background Technology

[0002] Surge arresters are widely used in power transmission lines. Their main function is to limit and absorb induced lightning overvoltages and the energy of lightning surge waves, suppressing overvoltages during line operation and thus protecting the normal operation of the entire transmission line. Therefore, real-time monitoring of surge arrester status helps to understand their operating conditions and ensure the normal operation of the transmission line.

[0003] Traction power supply systems contain a large number of harmonics. Traditional online monitoring methods for surge arresters determine the aging condition of the arrester by measuring the total leakage current or resistive current. However, in traction power supply systems, electric locomotives exhibit impact loads, which can cause the leakage current to change rapidly and repeatedly within a short period of time in certain scenarios. This can lead to missed or false alarms in online monitoring methods. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the health status of traction network surge arresters, which can improve the accuracy of surge arrester health status assessment, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for assessing the health status of surge arresters in traction networks, including:

[0006] Obtain the leakage current signal and signal decomposition parameters of the surge arrester in the traction network;

[0007] Based on the signal decomposition parameters, the leakage current signal is decomposed to obtain multiple modal components and the center frequency of each modal component;

[0008] Select target modal components whose center frequencies match the preset target center frequency range from each modal component;

[0009] The target modal components are reconstructed to obtain the equivalent resistive leakage current signal;

[0010] The amplitude variation rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal is determined. Based on each amplitude variation rate, the health status of the surge arrester is evaluated, and the health status evaluation result is obtained.

[0011] Secondly, this application also provides a device for assessing the health status of traction network surge arresters, comprising:

[0012] The data acquisition module is used to acquire the leakage current signal and signal decomposition parameters of the surge arrester in the traction network;

[0013] The signal decomposition module is used to decompose the leakage current signal based on the signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component.

[0014] The modal component filtering module is used to filter out target modal components whose center frequencies match the preset target center frequency range from each modal component.

[0015] The signal reconstruction module is used to reconstruct the target mode components to obtain the equivalent resistive leakage current signal;

[0016] The health status assessment module is used to determine the amplitude change rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal, and to assess the health status of the surge arrester based on each amplitude change rate, thereby obtaining the health status assessment result.

[0017] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the traction network surge arrester health status assessment method.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any of the above embodiments of the traction network surge arrester health status assessment method.

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the traction network surge arrester health status assessment method.

[0020] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for assessing the health status of surge arresters in traction networks differ from traditional methods based on harmonic analysis. First, it acquires the leakage current signal and signal decomposition parameters of the surge arresters in the traction network. Second, to address the leakage current fluctuations caused by load fluctuations, it decomposes the leakage current signal based on the signal decomposition parameters, obtaining multiple modal components and the center frequency of each modal component. Subsequently, it selects target modal components whose center frequencies match a preset target center frequency range from these modal components. Thus, it addresses the non-stationary leakage current fluctuations. The leakage current is decomposed into multiple modal components, and the main modal components are extracted. This helps to reduce the interference of the operating environment on the leakage current and improve the accuracy of the health status assessment. Next, the selected target modal components are reconstructed into an equivalent resistive leakage current signal. Finally, the amplitude change rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal is determined. Based on each amplitude change rate, the health status of the surge arrester is assessed, and the health status assessment result is obtained. This improves the accuracy of the surge arrester's health status assessment. Furthermore, it is beneficial to take timely equipment maintenance measures based on the health status assessment results, thereby improving the safety and reliability of the traction network. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a diagram illustrating the application environment of a method for assessing the health status of traction network surge arresters in one embodiment.

[0023] Figure 2 This is a flowchart illustrating a method for assessing the health status of surge arresters in a traction network, as shown in one embodiment.

[0024] Figure 3 This is a flowchart illustrating the method for assessing the health status of traction network surge arresters in another embodiment;

[0025] Figure 4 This is a flowchart illustrating the method for assessing the health status of traction network surge arresters in yet another embodiment;

[0026] Figure 5 This is a schematic diagram of the leakage current signal and the resistive leakage current signal in one embodiment;

[0027] Figure 6This is a flowchart illustrating the method for assessing the health status of traction network surge arresters in another embodiment;

[0028] Figure 7 This is a flowchart illustrating the method for assessing the health status of traction network surge arresters in another embodiment;

[0029] Figure 8 This is a structural block diagram of a traction network surge arrester health status assessment device in one embodiment;

[0030] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0032] In the traction system, the leakage current will suddenly increase when the train passes by, but this does not mean that the surge arrester has aged. In addition, due to train-grid resonance, switching overvoltage, etc., the leakage current of the surge arrester will also increase rapidly, so traditional online monitoring methods are prone to missed or false alarms.

[0033] The method for assessing the health status of traction network surge arresters provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown depicts server 102 and terminal 104 communicating via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be integrated onto server 102 or located in the cloud or on other network servers.

[0034] Specifically, a technician can upload the leakage current signal of the surge arrester in the traction network to a server via a terminal. The server 102 obtains the leakage current signal and signal decomposition parameters of the surge arrester in the traction network. Then, based on the signal decomposition parameters, the leakage current signal is decomposed to obtain multiple modal components and the center frequency of each modal component. Then, the target modal component whose center frequency matches the preset target center frequency range is selected from each modal component. The target modal component is reconstructed to obtain the equivalent resistive leakage current signal. Finally, the amplitude change rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal is determined. Based on each amplitude change rate, the health status of the surge arrester is evaluated to obtain the health status evaluation result.

[0035] The terminal 104 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 102 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0036] In one exemplary embodiment, such as Figure 2 As shown, a method for assessing the health status of traction network surge arresters is provided, and this method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps (hereinafter referred to as S): S100 to S400. Wherein:

[0037] S100: Obtain the leakage current signal and signal decomposition parameters of the surge arrester in the traction network.

[0038] The traction network is a dedicated power supply network that provides electricity to electric locomotives or EMUs. It consists of feeder lines, contact wires, rails, and return lines. Because the traction network is an open-air high-voltage system erected along the railway line, with long lines, wide distribution, and exposure to the natural environment, it is susceptible to lightning strikes. Therefore, surge arresters are used in the traction network to protect the transmission lines from lightning strikes and overvoltage damage.

[0039] The signal decomposition parameters are determined based on the signal decomposition algorithm. In this embodiment, Variational Mode Decomposition (VMD) is used to decompose the leakage current signal. The signal decomposition parameters include the number of decomposed modes and a penalty factor. The number of decomposed modes represents the number of modal components extracted from the original signal, and the penalty factor is used to control the bandwidth of each modal component.

[0040] The leakage current signal can be collected during the operation of the surge arrester or it can be collected through a pre-built surge arrester circuit model, which is based on the physical structure and electrical characteristics of the surge arrester.

[0041] In practice, the leakage current signal of the surge arrester can be acquired through a current transformer or through a pre-built surge arrester circuit model. The signal decomposition parameters can be predetermined and stored in a local database. Specifically, the optimal signal decomposition parameters can be determined using the center frequency method or an optimization algorithm. For the center frequency method, the range of values ​​for the number of decomposition modes and the penalty factor can be pre-defined. Candidate decomposition modes and candidate penalty factors are selected from these ranges to decompose the leakage current signal. The optimal signal decomposition parameters are then determined based on the decomposition results. For example, the center frequencies of the various mode components in the decomposition results may be relatively dispersed. Optimization algorithms can include, but are not limited to, genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms.

[0042] S200 performs signal decomposition on the leakage current signal based on signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component.

[0043] In practice, the server performs variational mode decomposition on the leakage current signal based on the decomposed mode number and penalty factor to obtain a set of intrinsic mode components (IMFs) that match the decomposed mode number, as well as the center frequency of each intrinsic mode component (hereinafter referred to as mode component).

[0044] S300: Select target modal components whose center frequencies match the preset target center frequency range from each modal component.

[0045] The preset target center frequency range includes power frequency and higher harmonic frequencies (such as the 3rd harmonic, 5th harmonic and 7th harmonic, etc.).

[0046] In practice, modal components whose center frequencies fall within a preset target center frequency range can be selected from each modal component, and the selected modal components can be determined as target modal components.

[0047] S400 reconstructs the target modal components to obtain the equivalent resistive leakage current signal.

[0048] In the above embodiments, the leakage current collected includes capacitive current and resistive current, and the equivalent resistive leakage current signal includes the resistive component of the leakage current.

[0049] In practice, the resistive leakage current is usually in the same frequency and phase as the applied voltage and contains odd harmonics. Therefore, after screening out the target mode components, the phase of the target mode components can be determined, the resistive mode components that match the phase of the voltage signal can be identified from the target mode, and the resistive mode components can be added together to obtain the reconstructed equivalent resistive leakage current signal.

[0050] S500 determines the amplitude variation rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal, and evaluates the health status of the surge arrester based on each amplitude variation rate to obtain the health status assessment result.

[0051] The health status assessment results include the health status of the surge arrester, which includes, but is not limited to, normal, abnormal, and fault states. An abnormal state indicates that the surge arrester is showing signs of aging, such as an increase in leakage current that does not exceed the specified limit. Fault states include, but are not limited to, moisture in the surge arrester, aging of the valve plates, and damage to the valve plates.

[0052] The rate of change of amplitude characterizes the degree to which the amplitude of the harmonic changes relative to the amplitude of the fundamental wave. Let the amplitude of the harmonic in the equivalent resistive leakage current signal be A. n cosφ n Let n be the harmonic order, and let the amplitude of the fundamental wave be M. Then the rate of change of the harmonic amplitude Δn = |(M-Ancosφ)| n ) / M|*100%.

[0053] In practical applications, pre-defined criteria for different health states can be established. For example, if the amplitude change rate for each harmonic is within a preset range corresponding to its harmonic order, the surge arrester is deemed to meet the health state criteria, resulting in a health state assessment. It is understandable that the amplitude change rate range for each harmonic may differ under the same health state criteria, and the amplitude change rate range for the same harmonic order may differ under different health state criteria. The amplitude change rate range for each health state can be set based on the analysis results of the surge arrester's equivalent resistive leakage current signal under different health states.

[0054] In practice, frequency domain analysis can be performed using the equivalent resistive leakage current signal to determine the amplitude of each harmonic. Then, the amplitude variation rate between each harmonic and the fundamental frequency can be determined using the above formula. Afterward, for each health status assessment condition, it is checked whether the amplitude variation rate of each harmonic is within the range of amplitude variation rates matching the harmonic order in that assessment condition. If they are all within the amplitude variation rate range, the health status corresponding to that assessment condition is determined as the current health status of the surge arrester, thus obtaining the health status assessment result.

[0055] In other embodiments, after obtaining the health status assessment result, the health status assessment result can also be pushed to the technician's terminal. If the health status assessment result indicates that the surge arrester is in a fault state, an early warning message is generated and pushed to the technician's terminal. The method for pushing the early warning message can include pushing the alarm message in the notification bar, displaying the alarm message in a full-screen or half-screen pop-up window, providing an alarm prompt by emitting a specific sound or vibration pattern, or providing a visual alarm prompt by flashing an indicator light, etc. It is understood that the method for pushing the alarm message can be any one of the aforementioned methods or any combination of multiple methods, and is not limited here.

[0056] The aforementioned method for assessing the health status of surge arresters in traction networks differs from traditional methods based on harmonic analysis. First, it acquires the leakage current signal and signal decomposition parameters of the surge arresters in the traction network. Second, to address the leakage current fluctuations caused by load fluctuations, it decomposes the leakage current signal based on the signal decomposition parameters, obtaining multiple modal components and the center frequency of each modal component. Subsequently, it selects target modal components whose center frequencies match a preset target center frequency range. In this way, the non-stationary leakage current is decomposed into multiple modal components. The main modal components are extracted, which helps reduce the interference of the operating environment on the leakage current and improves the accuracy of health status assessment. Then, the selected target modal components are reconstructed into an equivalent resistive leakage current signal. Finally, the amplitude change rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal is determined. Based on the amplitude change rate, the health status of the surge arrester is assessed, and the health status assessment result is obtained, which improves the accuracy of the surge arrester's health status assessment. Furthermore, it is beneficial to take timely equipment maintenance measures based on the health status assessment result, thereby improving the safety and reliability of the traction network.

[0057] In an exemplary embodiment, the signal decomposition parameters include the number of mode decompositions and a penalty factor, such as Figure 3 As shown, the signal decomposition parameters are obtained through the following steps S110 to S150, where:

[0058] S110 uses the modal decomposition number and penalty factor as the parameter combination to be optimized, and initially sets multiple parameter combinations.

[0059] S120, Signal decomposition step: For each parameter combination, the leakage current signal is decomposed according to the parameter combination to obtain multiple modal components and the center frequency of each modal component.

[0060] S130, determine the fitness of each parameter combination.

[0061] S140, if there are modal components with center frequencies of power frequency and preset harmonic frequencies among each modal component, then the parameter combination corresponding to the maximum fitness value is determined as the optimal signal decomposition parameters.

[0062] S150, otherwise, update the parameter combination and return to the signal decomposition step until there are modal components in each modal component with a center frequency of power frequency and a center frequency of preset harmonic frequency.

[0063] The setting of signal decomposition parameters directly affects the signal decomposition effect. Therefore, in this embodiment, a genetic algorithm is used to optimize the signal decomposition parameters in order to improve the decomposition accuracy.

[0064] For S110, in specific implementation, the ranges for the modality decomposition number and the penalty factor can be preset separately, thereby initializing and generating multiple sets of modality decomposition numbers and penalty factors to be optimized within the preset ranges. For example, the range for the modality decomposition number K is defined as 2 ≤ K ≤ 10; the range for the penalty factor alpha is defined as 1000 ≤ α ≤ 5000. Subsequently, multiple individuals are randomly generated within these preset ranges to form an initial population, with each individual representing the parameter combination (K, α) to be optimized.

[0065] For S120, in a specific implementation, for each parameter combination, variational mode decomposition can be performed on the leakage current signal to obtain the signal decomposition result. The signal decomposition result includes the K modal components IMFs obtained by decomposition and the center frequency of each of the K modal components.

[0066] For S130, in specific implementation, for each parameter combination, the fitness of each parameter combination is calculated based on the parameter combination, the preset fitness function, and the modal components and center frequencies obtained by variational mode decomposition using that parameter combination. This fitness is based on the optimization objective. In the fitness function, if the decomposition result includes modal components with center frequencies close to the power frequency and modal components with preset harmonic frequencies, the fitness is higher; if the number of decomposed modes K is smaller, the fitness is higher; if the sum of the bandwidths of the decomposed modal components is smaller, indicating more concentrated energy, the fitness is higher. The preset harmonic frequency is a high harmonic frequency; for example, the preset harmonic frequency is the frequency of the 17th harmonic.

[0067] For S140, in specific implementation, if there are modal components with a center frequency of power frequency and a preset harmonic frequency in the signal decomposition results of individuals in the current population, then the individual with the highest fitness in the current population is determined as the optimal individual, that is, the optimal modal decomposition number and penalty factor are obtained.

[0068] In S150, if no modal components with a center frequency of power frequency and a preset harmonic frequency are detected in the signal decomposition results of individuals in the current population, a new population can be generated through selection, crossover, and mutation, i.e., updating the parameter combinations. Specifically, for selection, individuals with fitness greater than a preset fitness threshold are selected from the current population based on their fitness level. For crossover, some signal decomposition parameters in the parameter combinations of the selected individuals are partially exchanged to generate new individuals. For mutation, some individuals are selected from the updated individuals, their parameters are adjusted, and the individuals are updated again, thus generating a new population. Subsequently, the process returns to step S120 until modal components with a center frequency of power frequency and a preset harmonic frequency are found in the signal decomposition results of individuals in the current population.

[0069] In this embodiment, the genetic algorithm is used to optimize the signal decomposition parameters used to decompose the leakage current signal, which helps to improve the accuracy of leakage current signal decomposition.

[0070] In an exemplary embodiment, the leakage current signal is decomposed based on signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component, including S210 to S220, wherein:

[0071] S210, based on the signal decomposition parameters, transforms the preset constrained signal decomposition model into an unconstrained signal decomposition model. The constrained signal decomposition model is constructed with the goal of minimizing the sum of the bandwidths of the center frequencies of each modal component and the constraint condition that the sum of each modal component matches the leakage current signal.

[0072] The expression for the modal components is as follows:

[0073]

[0074] The constrained signal decomposition model is as follows:

[0075]

[0076]

[0077] in, It is a signal The envelope amplitude, This is the instantaneous phase. k ={u1,u2,…,u k} represents each modal component, ω k ={ω1,ω2,…ω k} represents the center frequency of each modal component, and K is the number of decomposed modes, which can be the optimal number of decomposed modes obtained through the above embodiments.

[0078] In practice, using a quadratic penalty term and the Lagrange operator, an augmented Lagrange function for the constrained signal decomposition model (and for the unconstrained signal decomposition model) is constructed, thereby transforming the constrained variational problem into an unconstrained variational problem. The augmented Lagrange function is as follows:

[0079]

[0080] Where α is the penalty factor alpha, which can be the optimal penalty factor obtained through the above embodiments.

[0081] S220 uses the alternating direction multiplier method to determine the saddle point of the unconstrained signal decomposition model, where the saddle point includes multiple modal components and the center frequency of each modal component.

[0082] In the unconstrained signal decomposition model, the saddle point is the optimal solution, which includes a set of optimal modal components {u k A set of optimal center frequencies {ω} k} and an optimal Lagrange multiplier λ. The idea of ​​the alternating direction multiplier method can be summarized as follows: for the current variable, fix the other two variables and update the current variable.

[0083] In practice, firstly, k modes and k center frequencies are initialized, along with the Lagrange multipliers. Then, the alternating direction multiplier method is used to update each modal component, each center frequency, and the Lagrange multiplier. Specifically, the modal components and center frequencies are updated as follows:

[0084]

[0085]

[0086] in, Equivalent to the current remaining amount Wiener filtering, This represents the center frequency of the current modal component. The update method for the Lagrange multipliers can be referenced from the update method in the variational mode decomposition algorithm, and will not be elaborated here.

[0087] Next, it is determined whether the updated modal components, center frequencies, and Lagrange multipliers satisfy the preset convergence conditions. If not, the process returns to the step of updating each modal component, center frequency, and Lagrange multiplier using the alternating direction multiplier method. Otherwise, the K modal components and their center frequencies are output. The preset convergence conditions can be found in the variational mode decomposition algorithm flow, and will not be elaborated here.

[0088] In this embodiment, compared with the traditional method of directly analyzing non-stationary leakage current using fast Fourier transform, VMD decomposition using optimized signal decomposition parameters is beneficial for separating power frequency, harmonics and noise, and further, it is beneficial for improving the accuracy of surge arrester health status assessment.

[0089] In one exemplary embodiment, such as Figure 4 As shown, the target modal components are reconstructed to obtain the equivalent resistive leakage current signal, including S410 to S420, where:

[0090] S410, perform time-frequency analysis on the target modal components to obtain the time-frequency analysis results.

[0091] S420 reconstructs the target modal components based on the time-frequency analysis results to obtain the equivalent resistive leakage current signal.

[0092] The time-frequency analysis results can include amplitude and phase angle.

[0093] In practice, the time-frequency characteristics of each target modal component can be extracted using a Fast Fourier Transform (FFT) to obtain the amplitude and phase angle of each frequency point of each modal component. After applying the FFT, the time-domain signal of the target modal component can be reconstructed: f(ω) n )=A n sin(ω n +φ n ), where ω n φ represents the frequency under normal operating conditions. n This indicates the phase angle. When a surge arrester experiences a moisture or aging fault, the total resistive current will differ significantly from that under normal operating conditions.

[0094] Subsequently, the resistive current amplitude corresponding to each frequency point was determined to be A. n cosφ n Combining the aforementioned time-domain signal, the reconstructed target mode components are obtained. The sum of the reconstructed target mode components is the equivalent resistive leakage current signal. For example, as shown... Figure 5 The diagram shows a comparison between the original leakage current signal before reconstruction and the equivalent resistive leakage current signal after reconstruction.

[0095] In this embodiment, for the case of leakage current fluctuation in traction network surge arresters under load fluctuation, the sine wave of the target mode component is reconstructed into an equivalent resistive leakage current by FFT, which helps to improve the accuracy of assessing the health status of surge arresters based on the equivalent resistive leakage current.

[0096] In one exemplary embodiment, such as Figure 6As shown, the health status of the surge arrester is assessed based on the rate of change of each amplitude, and the health status assessment results are obtained, including S510 to S520, where:

[0097] S510, for each harmonic number, determines the baseline health status assessment result of the surge arrester based on the amplitude change rate corresponding to the harmonic number, the preset first amplitude change rate threshold, and the preset second amplitude change rate threshold.

[0098] S520 If the baseline health status assessment results for each harmonic order are the same, then the baseline health status assessment results shall be determined as the health status assessment results of the surge arrester.

[0099] The baseline health status assessment result represents the preliminary health status determination result, which can include normal status, abnormal status, and fault status. An abnormal status indicates that the surge arrester is showing signs of aging, such as an increase in leakage current that does not exceed the specified limit. Fault status includes, but is not limited to, surge arrester dampness, valve plate aging, valve plate damage, etc.

[0100] The preset first amplitude change rate threshold and the preset second amplitude change rate threshold are used to determine the health status of the surge arrester. These thresholds can be set using historical amplitude change rate data from surge arresters in different health states. The preset first amplitude change rate threshold is lower than the second amplitude change rate threshold. Different first and second amplitude change rate thresholds correspond to different amplitude change rates for each harmonic order, as shown in the table below:

[0101]

[0102] In practice, the amplitude change rate Δ corresponding to each harmonic order is determined. n =|(MA n cosφ n After 100% () / M|*100%, for each harmonic order n, the amplitude change rate Δ corresponding to harmonic order n is calculated. n Each is compared with the corresponding first amplitude change rate threshold x n Second amplitude change rate threshold y n Compare, if Δ n <x n If the harmonic number n corresponds to the baseline health status assessment result, it is determined to be normal; if x n <Δ n <y n If the harmonic number n corresponds to the baseline health status assessment result, it is determined to be an abnormal state; if Δ n >y n If the harmonic number n is determined to be the baseline health status assessment result, it is determined to be a fault state.

[0103] Subsequently, it is checked whether the baseline health status assessment results corresponding to each harmonic number are the same. If the baseline health status assessment results corresponding to each harmonic number are the same, the baseline health status assessment results are determined as the final health status assessment results of the surge arrester.

[0104] In this embodiment, the health status of the surge arrester is assessed based on the amplitude change rate of each harmonic, which improves the accuracy of the health status assessment.

[0105] In one exemplary embodiment, such as Figure 7 As shown, after determining the baseline health status assessment results of the surge arrester, the method further includes S530 to S550, wherein:

[0106] S530 If the baseline health status assessment results for each harmonic order are different, obtain the operating parameters and environmental parameters of the surge arrester, and determine the adjustment weight corresponding to each harmonic order based on the operating parameters and environmental parameters.

[0107] S540, based on the adjustment weights corresponding to each harmonic order, performs a weighted summation of the amplitude change rate corresponding to each harmonic order, the preset first amplitude change rate threshold, and the second amplitude change rate threshold.

[0108] S550 determines the health status assessment result of the surge arrester based on the weighted summation of the amplitude change rate, the first amplitude change rate threshold, and the second amplitude change rate threshold.

[0109] The operating parameters of a surge arrester may include its service life and cumulative discharge count. Environmental parameters may include temperature and humidity.

[0110] For S530, when the baseline health assessment states corresponding to different harmonic orders differ, the adjustment weights corresponding to each harmonic order can be determined using the analytic hierarchy process (AHP). Specifically, multiple potential fault states of surge arresters (such as moisture, external insulation damage, valve plate aging, and partial breakdown) are pre-set, and different adjustment weights corresponding to different harmonic orders are assigned to different potential fault states. In practice, a judgment matrix for different potential fault states is constructed based on the amplitude change rate of each harmonic order under different health assessment states, the operating parameters of the surge arrester, and environmental parameters. By solving the judgment matrix, the potential fault state of the surge arrester is determined. Based on the pre-set correspondence between the potential fault states and the adjustment weights corresponding to each harmonic, the adjustment weights corresponding to each harmonic are determined.

[0111] For S540, the weight β can be adjusted according to the harmonic order n. n We then perform a weighted summation of the amplitude change rates Δn to obtain the weighted summation amplitude change rate Δn. n', then, based on the adjusted weight β n For the first amplitude change rate threshold x n Perform a weighted summation to obtain the first magnitude change rate threshold x after weighted summation. n ', then, based on the adjusted weight β n For the second amplitude change rate threshold y n Perform a weighted summation to obtain the second magnitude change rate threshold y. n '. If Δ n ' <x n If x ', then the health status assessment result of the surge arrester is determined to be normal; if x n '<Δ n ' <y n If Δ n '>y n If the condition is ', then the health status assessment result of the surge arrester is determined to be a fault state.

[0112] In this embodiment, the health status assessment of the surge arrester is performed by combining operating parameters, environmental parameters, and amplitude change rate, which improves the accuracy of health status determination.

[0113] To provide a clearer explanation of the health status assessment method for traction network surge arresters provided in this application, a specific embodiment and accompanying drawings are described below. Figure 6 The specific embodiment includes the following steps:

[0114] S1, obtain the leakage current signal of the surge arrester in the traction network.

[0115] S2 uses the modal decomposition number and penalty factor as the parameter combination to be optimized, with multiple initial parameter combinations.

[0116] S3, Signal decomposition step: For each parameter combination, the leakage current signal is decomposed according to the parameter combination to obtain multiple modal components and the center frequency of each modal component.

[0117] S4. Determine the fitness of each parameter combination. If there are modal components with a center frequency of power frequency and a preset harmonic frequency in each modal component, then determine the parameter combination corresponding to the maximum fitness as the optimal signal decomposition parameters. Otherwise, update the parameter combination and return to S3 until there are modal components with a center frequency of power frequency and a preset harmonic frequency in each modal component.

[0118] S5. Based on the optimal signal decomposition parameters, the preset constrained signal decomposition model is transformed into an unconstrained signal decomposition model. The constrained signal decomposition model is constructed with the goal of minimizing the sum of the bandwidths of the center frequencies of each modal component and the constraint condition that the sum of each modal component matches the leakage current signal.

[0119] S6. The saddle point of the unconstrained signal decomposition model is determined by the alternating direction multiplier method. The saddle point includes multiple modal components and the center frequency of each modal component.

[0120] S7, select the target modal components whose center frequency matches the preset target center frequency range from each modal component.

[0121] S8 performs time-frequency analysis on the target modal components to obtain the time-frequency analysis results. Based on the time-frequency analysis results, the target modal components are reconstructed to obtain the equivalent resistive leakage current signal.

[0122] S9. For each harmonic number, the baseline health status assessment result of the surge arrester is determined based on the amplitude change rate corresponding to the harmonic number, the preset first amplitude change rate threshold, and the preset second amplitude change rate threshold.

[0123] S10. If the baseline health status assessment results for each harmonic order are the same, then the baseline health status assessment results shall be determined as the health status assessment results of the surge arrester.

[0124] S11. If the baseline health status assessment results corresponding to each harmonic order are different, obtain the operating parameters and environmental parameters of the surge arrester. Based on the operating parameters and environmental parameters, determine the adjustment weight corresponding to each harmonic order. Based on the adjustment weight corresponding to each harmonic order, perform weighted summation on the amplitude change rate, the preset first amplitude change rate threshold, and the second amplitude change rate threshold corresponding to each harmonic order. Based on the weighted summation of the amplitude change rate, the first amplitude change rate threshold, and the second amplitude change rate threshold, determine the health status assessment result of the surge arrester.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] In one exemplary embodiment, such as Figure 8 As shown, a traction grid surge arrester health status assessment device 600 is provided, including: a data acquisition module 610, a signal decomposition module 620, a modal component screening module 630, a signal reconstruction module 640, and a health status assessment module 650, wherein:

[0127] The data acquisition module 610 is used to acquire the leakage current signal and signal decomposition parameters of the surge arrester in the traction network;

[0128] The signal decomposition module 620 is used to decompose the leakage current signal based on the signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component.

[0129] The modal component filtering module 630 is used to filter out target modal components whose center frequencies match the preset target center frequency range from each modal component.

[0130] The signal reconstruction module 640 is used to reconstruct the target mode components to obtain the equivalent resistive leakage current signal;

[0131] The health status assessment module 650 is used to determine the amplitude change rate between each harmonic and the fundamental wave in the equivalent resistive leakage current signal, and to assess the health status of the surge arrester based on each amplitude change rate, thereby obtaining the health status assessment result.

[0132] In an exemplary embodiment, the data acquisition module 610 is further configured to initialize multiple parameter combinations with the modal decomposition number and penalty factor as the parameter combinations to be optimized; the signal decomposition step is as follows: for each parameter combination, the leakage current signal is decomposed according to the parameter combination to obtain multiple modal components and the center frequency of each modal component; the fitness of each parameter combination is determined; if there are modal components with a center frequency of power frequency and a preset harmonic frequency among the modal components, the parameter combination corresponding to the maximum fitness value is determined as the optimal signal decomposition parameter; otherwise, the parameter combination is updated and the process returns to the signal decomposition step until there are modal components with a center frequency of power frequency and a preset harmonic frequency among the modal components.

[0133] In an exemplary embodiment, the signal decomposition module 620 is further configured to convert a preset constrained signal decomposition model into an unconstrained signal decomposition model based on signal decomposition parameters. The constrained signal decomposition model is constructed with the objective of minimizing the sum of the bandwidths of the center frequencies of each modal component and the constraint condition that the sum of each modal component matches the leakage current signal. The saddle point of the unconstrained signal decomposition model is determined by the alternating direction multiplier method. The saddle point includes multiple modal components and the center frequency of each modal component.

[0134] In an exemplary embodiment, the signal reconstruction module 640 is further configured to perform time-frequency analysis on the target modal components to obtain time-frequency analysis results; and reconstruct the target modal components based on the time-frequency analysis results to obtain an equivalent resistive leakage current signal.

[0135] In an exemplary embodiment, the health status assessment module 650 is further configured to determine the baseline health status assessment result of the surge arrester for each harmonic number based on the amplitude change rate corresponding to the harmonic number, a preset first amplitude change rate threshold, and a preset second amplitude change rate threshold; if the baseline health status assessment results corresponding to each harmonic number are the same, then the baseline health status assessment result is determined as the health status assessment result of the surge arrester.

[0136] In an exemplary embodiment, the health status assessment module 650 is further configured to obtain the operating parameters and environmental parameters of the surge arrester if the baseline health status assessment results corresponding to each harmonic number are different, and determine the adjustment weight corresponding to each harmonic number based on the operating parameters and environmental parameters.

[0137] Based on the adjustment weights corresponding to each harmonic order, the amplitude change rate corresponding to each harmonic order, the preset first amplitude change rate threshold, and the second amplitude change rate threshold are weighted and summed respectively.

[0138] The health status assessment result of the surge arrester is determined based on the weighted summation of the amplitude change rate, the first amplitude change rate threshold, and the second amplitude change rate threshold.

[0139] Each module in the aforementioned traction network surge arrester health status assessment device 600 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for assessing the health status of traction network surge arresters.

[0141] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the embodiments of the above-described traction network surge arrester health status assessment method.

[0143] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in any of the embodiments of the above-described traction network surge arrester health status assessment method.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the traction network surge arrester health status assessment method.

[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0148] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing the health status of surge arresters in traction networks, characterized in that, The method includes: Obtain the leakage current signal and signal decomposition parameters of the surge arrester in the traction network; Based on the signal decomposition parameters, the leakage current signal is decomposed to obtain multiple modal components and the center frequency of each modal component. Target modal components whose center frequencies match the preset target center frequency range are selected from each modal component. The target modal components are reconstructed to obtain the equivalent resistive leakage current signal; The amplitude variation rate between each harmonic and the fundamental frequency in the equivalent resistive leakage current signal is determined respectively. Based on each amplitude variation rate, the health status of the surge arrester is evaluated to obtain the health status evaluation result.

2. The method according to claim 1, characterized in that, The signal decomposition parameters include the number of mode decompositions and the penalty factor; The signal decomposition parameters are obtained in the following way: Using the modality decomposition number and the penalty factor as the parameter combination to be optimized, multiple parameter combinations are initially set. Signal decomposition step: For each parameter combination, the leakage current signal is decomposed according to the parameter combination to obtain multiple modal components and the center frequency of each modal component; Determine the fitness of each of the parameter combinations; If there are modal components with a center frequency of power frequency and a center frequency of preset harmonic frequency among the modal components, then the parameter combination corresponding to the maximum fitness value is determined as the optimal signal decomposition parameters. Otherwise, update the parameter combination and return to the signal decomposition step until there are modal components in each modal component with a center frequency of power frequency and a center frequency of preset harmonic frequency.

3. The method according to claim 1, characterized in that, The step of decomposing the leakage current signal based on the signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component includes: Based on the signal decomposition parameters, the preset constrained signal decomposition model is transformed into an unconstrained signal decomposition model. The constrained signal decomposition model is constructed with the goal of minimizing the sum of the bandwidths of the center frequencies of each modal component and the constraint condition that the sum of each modal component matches the leakage current signal. The saddle point of the unconstrained signal decomposition model is determined by the alternating direction multiplier method. The saddle point includes multiple modal components and the center frequency of each modal component.

4. The method according to claim 1, characterized in that, The process of reconstructing the target mode components to obtain the equivalent resistive leakage current signal includes: Time-frequency analysis was performed on the target modal components to obtain the time-frequency analysis results; Based on the time-frequency analysis results, the target mode components are reconstructed to obtain the equivalent resistive leakage current signal.

5. The method according to claim 1, characterized in that, The health status of the surge arrester is assessed based on each of the amplitude change rates to obtain a health status assessment result, including: For each harmonic number, the baseline health status assessment result of the surge arrester is determined based on the amplitude change rate corresponding to the harmonic number, a preset first amplitude change rate threshold, and a preset second amplitude change rate threshold. If the baseline health status assessment results corresponding to each of the harmonic orders are the same, then the baseline health status assessment results are determined as the health status assessment results of the surge arrester.

6. The method according to claim 5, characterized in that, After determining the baseline health status assessment result of the surge arrester, the method further includes: If the baseline health status assessment results corresponding to each harmonic order are different, obtain the operating parameters and environmental parameters of the surge arrester, and determine the adjustment weight corresponding to each harmonic order based on the operating parameters and environmental parameters. Based on the adjustment weights corresponding to each harmonic order, the amplitude change rate corresponding to each harmonic order, the preset first amplitude change rate threshold, and the second amplitude change rate threshold are weighted and summed respectively. The health status assessment result of the surge arrester is determined based on the weighted summation of the amplitude change rate, the first amplitude change rate threshold, and the second amplitude change rate threshold.

7. A device for assessing the health status of traction network surge arresters, characterized in that, The device includes: The data acquisition module is used to acquire the leakage current signal and signal decomposition parameters of the surge arrester in the traction network; The signal decomposition module is used to decompose the leakage current signal based on the signal decomposition parameters to obtain multiple modal components and the center frequency of each modal component. The modal component filtering module is used to filter out target modal components whose center frequencies match a preset target center frequency range from the modal components. The signal reconstruction module is used to reconstruct the target mode components to obtain an equivalent resistive leakage current signal; The health status assessment module is used to determine the amplitude change rate between each harmonic and the fundamental wave in the equivalent resistive leakage current signal, and to assess the health status of the surge arrester based on each amplitude change rate, thereby obtaining the health status assessment result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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