Light emitting device and method of manufacturing the same
By collecting time-series data of surge arrester leakage current signals and performing multi-order derivative calculations, the degradation characteristic signals are amplified, solving the problem of low detection accuracy in existing technologies and achieving accurate detection of surge arrester degradation status.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIEYANG POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-01-21
- Publication Date
- 2026-05-29
AI Technical Summary
Existing technologies cannot accurately identify minute harmonic changes in surge arresters, resulting in low detection accuracy.
By collecting the time-series data corresponding to the leakage current signal of the surge arrester, calculating the derivative order value, and using multi-order derivative operations to amplify the degradation characteristics, extract the degradation characteristic signal, and determine whether the surge arrester is in a degraded state.
It improves the accuracy of surge arrester degradation detection, enabling more precise identification of surge arrester degradation status.
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Figure CN122109658A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of surge arrester deterioration detection technology, and in particular to a surge arrester deterioration detection method, apparatus, equipment, medium and product. Background Technology
[0002] Metal oxide surge arresters (MOAs) are widely used in overvoltage protection of power systems due to their high current-carrying capacity, strong nonlinear characteristics, and no follow current. To ensure the normal operation of MOAs, regular condition monitoring and evaluation are necessary. To reduce equipment outages and ensure power supply reliability, MOA maintenance now primarily relies on uninterrupted power supply methods such as online monitoring and live-line testing.
[0003] In existing technologies, the current distortion caused by the nonlinear characteristics of MOA is mainly determined by performing Fourier decomposition on the leakage current of MOA to evaluate the harmonic content / distortion rate of the leakage current, thereby examining the change in the nonlinear characteristics of MOA and thus judging the state of the surge arrester.
[0004] Because existing technologies cannot detect minute changes in harmonics, they suffer from low detection accuracy. Summary of the Invention
[0005] This application provides methods, apparatus, equipment, media, and products for detecting surge arrester degradation, in order to achieve the technical effect of improving detection accuracy.
[0006] In a first aspect, embodiments of this application provide a method for detecting the deterioration of a surge arrester, including:
[0007] Collect timing data corresponding to the leakage current signal of the surge arrester;
[0008] The derivative order is calculated based on the time-series data corresponding to the current signal.
[0009] The time series data is differentiated based on the derivative order numerical value to obtain the first derivative result;
[0010] Data conversion is performed based on the result of the first derivative operation to obtain the first derivative processed signal;
[0011] Extracting degradation features from the maximum value of the signal based on the first derivative;
[0012] When the degradation characteristics are valid and exceed the first preset threshold, the surge arrester is determined to be in a degraded state.
[0013] In one possible implementation, the derivative order is calculated based on the time-series data corresponding to the current signal, including:
[0014] The harmonic significance and noise level of the current signal are calculated based on preset order values and time series data.
[0015] Based on the harmonic significance and noise level, the value of the derivative order to be determined is calculated;
[0016] If the harmonic significance does not meet the preset conditions and the value of the derivative to be determined is lower than the preset upper limit, the preset order value is updated based on the value of the derivative to be determined, and iterative calculation is performed until the harmonic significance meets the preset conditions or the value of the derivative to be determined reaches the preset upper limit.
[0017] In one possible implementation, the harmonic significance and noise level values of the current signal are calculated based on time-series data, including:
[0018] The time series data is differentiated based on a preset order value to obtain the second derivative result.
[0019] Based on the result of the second derivative operation and the preset angular frequency, the second derivative processed signal is obtained;
[0020] The harmonic significance is calculated based on the signal peak value of the signal processed by the second derivative and the preset capacitive current reference value.
[0021] Acquire current signal data within a preset voltage fluctuation period based on time-series data;
[0022] The noise level is obtained by calculating the standard deviation based on the current signal data.
[0023] In one possible implementation, after extracting degradation features from the maximum value of the processed signal based on the first derivative, the method further includes:
[0024] Based on a preset power frequency period, the time series data is divided into multiple time series subsequences;
[0025] Based on the numerical derivative order, the derivative is calculated for each time series subsequence to obtain the third derivative result for each time series subsequence.
[0026] For each result of the third derivative operation, a signal conversion is performed to obtain the third derivative processed signal;
[0027] The correlation coefficient matrix is obtained by calculating the correlation coefficients for multiple third derivative processed signals.
[0028] The effectiveness of determining the degradation characteristics of time series data based on the correlation coefficient matrix.
[0029] In one possible implementation, correlation coefficients are calculated for multiple third-derivative processed signals to obtain a correlation coefficient matrix, including:
[0030] The mean value of each third derivative processed signal is calculated.
[0031] Based on the signal mean and the sub-signal values of each third derivative processed signal, the covariance matrix corresponding to multiple third derivative processed signals is calculated.
[0032] The correlation coefficient matrix is obtained by calculating the correlation coefficient based on the covariance matrix.
[0033] In one possible implementation, the effectiveness of determining the degradation characteristics of time series data based on the correlation coefficient matrix includes:
[0034] Based on the correlation coefficient matrix, calculate the mean correlation coefficient for each row;
[0035] Sort the mean correlation coefficients of each row to obtain the median after sorting;
[0036] When the median is greater than or equal to the second preset threshold, the degradation characteristics of the time series data are determined to be valid.
[0037] When the median is less than the second preset threshold, the degradation characteristics of the time series data are invalidated.
[0038] Secondly, embodiments of this application provide a surge arrester degradation detection device, comprising:
[0039] The acquisition module is used to collect the timing data corresponding to the leakage current signal of the surge arrester;
[0040] The first processing module is used to calculate the derivative order value based on the time-series data corresponding to the current signal.
[0041] The second processing module is used to perform derivative operations on the time series data based on the derivative order value to obtain the first derivative operation result;
[0042] The third processing module is used to perform data conversion based on the result of the first derivative operation to obtain the first derivative processing signal;
[0043] The fourth processing module is used to extract degradation features from the maximum value of the processed signal based on the first derivative.
[0044] The fifth processing module is used to determine that the surge arrester is in a deteriorated state when the deterioration characteristics are valid and exceed the first preset threshold.
[0045] In one possible implementation, the first processing module is further configured to:
[0046] The harmonic significance and noise level of the current signal are calculated based on preset order values and time series data.
[0047] Based on the harmonic significance and noise level, the value of the derivative order to be determined is calculated;
[0048] If the harmonic significance does not meet the preset conditions and the value of the derivative to be determined is lower than the preset upper limit, the preset order value is updated based on the value of the derivative to be determined, and iterative calculation is performed until the harmonic significance meets the preset conditions or the value of the derivative to be determined reaches the preset upper limit.
[0049] In one possible implementation, the first processing module is further configured to:
[0050] The time series data is differentiated based on a preset order value to obtain the second derivative result.
[0051] Based on the result of the second derivative operation and the preset angular frequency, the second derivative processed signal is obtained;
[0052] The harmonic significance is calculated based on the signal peak value of the signal processed by the second derivative and the preset capacitive current reference value.
[0053] Acquire current signal data within a preset voltage fluctuation period based on time-series data;
[0054] The noise level is obtained by calculating the standard deviation based on the current signal data.
[0055] In one possible implementation, the fourth processing module is further configured to:
[0056] Based on a preset power frequency period, the time series data is divided into multiple time series subsequences;
[0057] Based on the numerical derivative order, the derivative is calculated for each time series subsequence to obtain the third derivative result for each time series subsequence.
[0058] For each result of the third derivative operation, a signal conversion is performed to obtain the third derivative processed signal;
[0059] The correlation coefficient matrix is obtained by calculating the correlation coefficients for multiple third derivative processed signals.
[0060] The effectiveness of determining the degradation characteristics of time series data based on the correlation coefficient matrix.
[0061] In one possible implementation, the fourth processing module is further configured to:
[0062] The mean value of each third derivative processed signal is calculated.
[0063] Based on the signal mean and the sub-signal values of each third derivative processed signal, the covariance matrix corresponding to multiple third derivative processed signals is calculated.
[0064] The correlation coefficient matrix is obtained by calculating the correlation coefficient based on the covariance matrix.
[0065] In one possible implementation, the fourth processing module is further configured to:
[0066] Based on the correlation coefficient matrix, calculate the mean correlation coefficient for each row;
[0067] Sort the mean correlation coefficients of each row to obtain the median after sorting;
[0068] When the median is greater than or equal to the second preset threshold, the degradation characteristics of the time series data are determined to be valid.
[0069] When the median is less than the second preset threshold, the degradation characteristics of the time series data are invalidated.
[0070] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;
[0071] The memory stores instructions that the computer executes;
[0072] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect above and various possible implementations of the first aspect.
[0073] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and various possible implementations thereof.
[0074] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and various possible implementations thereof.
[0075] This application provides a method, apparatus, device, medium, and product for detecting surge arrester degradation. The method involves collecting time-series data corresponding to the leakage current signal of the surge arrester, calculating the derivative order value based on the time-series data, performing a derivative operation on the time-series data using the calculated derivative order value to obtain a first derivative operation result, performing data conversion based on the first derivative operation result to obtain a first derivative processing signal, and extracting the degradation characteristics of the surge arrester based on the maximum value of the first derivative processing signal. When the degradation characteristics are valid and exceed a first preset threshold, the surge arrester is determined to be in a degraded state. Compared with the prior art's method of using Fourier transform for feature extraction, this application uses a multi-order derivative method to expand the feature confidence of the current signal, thereby enabling accurate acquisition of degradation characteristics and achieving the technical effect of improving detection accuracy. Attached Figure Description
[0076] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0077] Figure 1 Flowchart of the surge arrester degradation detection method provided in this application Figure 1 ;
[0078] Figure 2 A schematic diagram of a surge arrester circuit model provided in an embodiment of this application;
[0079] Figure 3 Flowchart of the surge arrester degradation detection method provided in this application Figure 2 ;
[0080] Figure 4 Flowchart of the surge arrester degradation detection method provided in this application Figure 3 ;
[0081] Figure 5 A schematic diagram of the lightning arrester deterioration detection device provided in this application;
[0082] Figure 6 A schematic diagram of the structure of the electronic device provided in this application.
[0083] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation
[0084] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0085] In existing technologies, the mainstream methods for online detection and live testing involve obtaining the phase difference between the system voltage and the MOA leakage current, and then calculating the magnitude of the MOA resistive current to measure the MOA's condition. This primarily utilizes Fourier processing to decompose the current signal, assessing the harmonic content or distortion rate in the leakage current to determine changes in the MOA's nonlinear characteristics and further identify its degradation state.
[0086] However, in the early stages of MOA degradation, the leakage current harmonic content in the Fourier analysis of existing technologies is very low, making it difficult to identify minute changes in harmonics through Fourier decomposition; thus, existing technologies suffer from low detection accuracy.
[0087] To address the aforementioned technical problems, this application proposes the following technical concept: Time-series data corresponding to the leakage current signal of the surge arrester is collected; the derivative order value is calculated using the time-series data, and this derivative order value is used to indicate subsequent data differentiation and signal amplification. The time-series data is differentiated using the derivative order value to obtain a first derivative result; this first derivative result is then converted to obtain a first derivative processed signal, the purpose of which is to obtain an amplified current signal; degradation features are extracted from the maximum value of the first derivative processed signal, the purpose of which is to determine whether the surge arrester is degraded; when the degradation feature is determined to be valid and exceeds a first preset threshold, the surge arrester is determined to be in a degraded state. Compared with existing technologies, this application only performs degradation analysis on the leakage current signal of the surge arrester, and simultaneously amplifies the degradation features in the current signal through multi-order differentiation, achieving more accurate feature capture and improving detection accuracy.
[0088] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.
[0089] Figure 1 Flowchart of the surge arrester degradation detection method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:
[0090] S101. Collect the timing data corresponding to the leakage current signal of the surge arrester.
[0091] In this step, the timing data refers to the leakage current signal generated by the surge arrester within a preset time period. The current signal can be acquired by using a current sensor to collect the leakage current signal of the surge arrester, obtaining the leakage current signal output by the current sensor through a data acquisition system, and then eliminating noise interference to obtain a clear leakage current signal.
[0092] For example, one possible implementation for acquiring time-series data is as follows:
[0093] a1. Obtain the duration, frequency, and resolution of data sampling.
[0094] a2. Collect leakage current signals within a preset time period based on sampling duration, frequency, and resolution.
[0095] a3. Use filters to eliminate noise and interference in the leakage current signal to obtain a clear current signal.
[0096] a4. Match the clear current signal with the timestamp of the signal acquisition to obtain time series data, and store the time series data in the cloud or on a local computer for subsequent data analysis.
[0097] For example, suppose there is a power substation where it is necessary to monitor the leakage current of a surge arrester. A Hall effect current sensor can be installed on the grounding wire of the surge arrester. The current signal output by the sensor is connected via cable to a data acquisition device, which records the current data at a sampling rate of 1000 times per second. The data acquisition device is connected to a computer, and the software running on the computer displays the current waveform and stores the data in real time.
[0098] S102. Based on the time-series data corresponding to the current signal, the derivative order is calculated.
[0099] In this step, the derivative order refers to the number of times or the order of derivative calculations performed on the time series data. This is used to amplify the current signal through multiple derivative calculations, thereby making the characteristics of the current signal more obvious.
[0100] Alternatively, one possible way to calculate the numerical value of the derivative is as follows:
[0101] S1021. The harmonic significance and noise level of the current signal are calculated based on the preset order value and time sequence data.
[0102] In this step, harmonic significance refers to the distortion rate of the leakage current waveform within a preset period, and noise level refers to the standard deviation of the leakage current at the zero-crossing point of the voltage.
[0103] Alternatively, one possible way to calculate the harmonic significance and noise level values is as follows:
[0104] A1. Perform derivative operations on the time series data based on the preset order value to obtain the second derivative operation result.
[0105] In this step, the preset order value refers to the derivative order value used in the current iteration calculation. The preset order value will be continuously updated in the continuous iteration calculation.
[0106] For example, assuming the preset order value is 1, the result of the second derivative operation is the result of the first derivative of the time series data.
[0107] A2. Based on the result of the second derivative operation and the preset angular frequency, the second derivative processing signal is obtained.
[0108] In this step, the second derivative processing signal is obtained based on the second derivative calculation result and the preset angular frequency by dividing the second derivative calculation result by the Mth power of the angular frequency, where M refers to the current preset order value, which is a positive integer greater than 0. The purpose of this step is to eliminate the fundamental frequency dependence and ensure that the results under different power grid frequencies are comparable.
[0109] A3. The harmonic significance is calculated based on the signal peak value of the signal processed by the second derivative and the preset capacitive current reference value.
[0110] In this step, the preset capacitive current reference value can be 85% of the effective value of the leakage current; harmonic significance refers to the ratio of the second derivative processed signal to the preset capacitive current reference value.
[0111] Optionally, the preset capacitive current reference value can also be the capacitive current component actually measured by the surge arrester under normal conditions.
[0112] For example, assuming the current preset capacitive current reference value is 0.425mA, the peak value of the calculated second derivative processed signal is 0.12mA. According to the calculation method of harmonic significance, the obtained harmonic significance is 0.12mA / 0.425mA ≈ 0.28. Assuming the surge arrester is deteriorating and the peak value of the second derivative processed signal rises to 0.25mA, the calculated harmonic significance at this time is 0.25mA / 0.425mA ≈ 0.59. It can be observed that during the process of surge arrester deterioration, the harmonic significance of the leakage current signal also increases.
[0113] A4. Obtain current signal data within a preset voltage fluctuation period based on time-series data.
[0114] In this step, the preset voltage fluctuation period refers to a range of values for the voltage zero-crossing point. The current signal value within the preset voltage fluctuation period refers to data that is significantly affected by voltage fluctuations. The data during this period can be used to evaluate the noise caused by voltage fluctuations.
[0115] For example, using the voltage zero-crossing point as a reference, 10% of the data before and after the zero-crossing point is collected from the time-series data as the current signal data for calculating the noise level. Here, 10% refers to 10% of the duration corresponding to a single power frequency cycle of the element and time period. When the power frequency cycle is 20ms, the current signal data 2ms before and after the zero-crossing point is used as the current signal data for calculating the noise level.
[0116] A5. Calculate the standard deviation based on the current signal data to obtain the noise level value.
[0117] In this step, the noise level value is used to describe the degree of noise impact caused by voltage fluctuations. The method of calculating the standard deviation reflects the dispersion of the data, which indicates the degree of interference of the current voltage fluctuation on the current signal.
[0118] For example, when the surge arrester is in a normal state, the current signal data corresponding to the voltage zero-crossing point is [0.01mA, -0.02mA, 0.005mA, -0.015mA], and the calculated standard deviation is 0.012mA. When the surge arrester is in a deteriorated state, the current signal data corresponding to the voltage zero-crossing point is [0.1mA, -0.3mA, 0.2mA, -0.4mA], and the calculated standard deviation is 0.25mA. As the degree of deterioration of the surge arrester increases, the corresponding noise level also increases.
[0119] S1022. Based on the harmonic significance and noise level, the value of the derivative order to be determined is calculated.
[0120] In this step, the value of the derivative to be determined is calculated by weighting the difference between the harmonic significance and the noise level. When the harmonic significance is high, a higher derivative is allowed to improve the detection sensitivity. When the noise level is high, the value of the derivative to be determined needs to be limited to avoid amplifying interference.
[0121] For example, when the surge arrester is in the initial stage of deterioration, the calculated harmonic significance is 0.3 and the noise level is 0.02. The weight of the harmonic significance is 3 and the weight of the noise level is 2. The weighted calculation yields a value of 3×0.3-2×0.02+1=1.86, which is rounded to 2. Therefore, the value of the derivative to be determined is 2.
[0122] S1023. When the harmonic significance does not meet the preset conditions and the value of the derivative to be determined is lower than the preset upper limit, update the preset order value based on the value of the derivative to be determined and perform iterative calculation until the harmonic significance meets the preset conditions or the value of the derivative to be determined reaches the preset upper limit.
[0123] In this step, the preset condition refers to the rate of change of harmonic significance being lower than the preset threshold, that is, the rate of change of harmonic significance being lower than the preset threshold set during the two iterations.
[0124] For example, the harmonic significance is 0.2 in the first iteration, 0.4 in the second iteration, and 0.41 in the third iteration. The rate of change of harmonic significance in the third and second iterations is 2.5%. If the rate of change of harmonic significance is lower than the preset threshold of 3%, then the value of the derivative to be determined in the third iteration can be determined as the target derivative value.
[0125] For example, if the current iteration number is 5, the rate of change of harmonic significance exceeds the preset threshold, the preset upper limit is 5, and the iteration number reaches the upper limit, then the value of the derivative to be determined calculated in the current iteration is determined as the target order value.
[0126] S103. Perform differentiation on the time series data based on the derivative order value to obtain the first differentiation result.
[0127] For example, Figure 2 A schematic diagram of a surge arrester circuit model is provided for an embodiment of this application, such as... Figure 2 As shown, the circuit model of the surge arrester can be equivalent to a parallel combination of a capacitor and a nonlinear resistor. Due to the presence of the nonlinear resistor, the leakage current waveform will be distorted into a spiked wave, thereby generating odd-order harmonic components in the leakage current. Therefore, under operating voltage, the time-domain expression of the surge arrester leakage current signal is shown in Equation 1:
[0128]
[0129] Where i(t) refers to the timing data corresponding to the leakage current signal; This refers to capacitive current, This refers to the resistive fundamental current. This refers to resistive harmonic current; For capacitive current phase; K c This refers to the peak value of the capacitive current; K1 is the peak value of the fundamental frequency of the resistive current; similarly, K... n The peak value of the nth harmonic. Let be the phase of the nth harmonic; w refers to the angular frequency. From Formula 1, the range of the leakage current is (-p, p), where p = K. c +K1+K3+...+K n .
[0130] It should be noted that the degradation process of a surge arrester is actually the degradation of its metal oxide resistive varistor. Because the varistor has varistor characteristics, it normally presents a high resistance state to the operating voltage. When the varistor begins to degrade, its equivalent resistance to the operating voltage is lower than normal, leading to a greater degree of waveform distortion in the leakage current. Therefore, the resistive current component of the surge arrester's leakage current, which is K1-K in the formula... nThe numerical value can characterize the degree of degradation of the surge arrester. During normal operation, the main component of the leakage current of the surge arrester is capacitive current, accounting for over 85%, which is K in Formula 1. c The value is the resistive current amplitude K1-K n The leakage current can be tens or even hundreds of times greater than the arrester's current. Therefore, the measured leakage current value is insufficient to reflect the degree of arrester degradation. This embodiment extracts degradation characteristics through differentiation calculation, that is, it performs differentiation calculation on the time-series data of the arrester's leakage current signal.
[0131] For example, when the derivative order is 1, the method for obtaining the first derivative result by performing differentiation on time series data is shown in Formula 2:
[0132]
[0133] in, This refers to the result of the first derivative operation; the remaining parameters are explained in Formula 1. This formula converts all components into sine functions through time-domain differentiation and introduces a linear amplification factor for the harmonic order n.
[0134] S104. Based on the result of the first derivative operation, perform data conversion to obtain the first derivative processing signal.
[0135] In this step, data conversion of the first derivative operation result means dividing the first derivative data processing signal by the m-th power of the angular frequency.
[0136] For example, when m=1, the calculation method for the first derivative signal is as shown in Formula 3:
[0137]
[0138] The parameters of this formula are explained in Equations 1 and 2 above. This formula eliminates frequency dependence by dividing by w, making the result independent of the grid frequency. The signal range after the first derivative is (-p, p), where p = K. c +K1+3K3+...+nK n Compared to the range of Equation 1, the amplitude of the nth harmonic in the maximum value p will be amplified by a factor of n, while the capacitive current K c There is no amplification effect on the maximum value. Therefore, the degradation characteristics of the surge arrester can be amplified by taking the first derivative of the leakage current. Among them, When the power frequency period f = 50Hz, .
[0139] When the derivative order is m, the first derivative result obtained by performing differentiation on time series data is shown in Equations 4 and 5:
[0140]
[0141]
[0142] Formula 3 refers to the result of the first derivative operation when m is even. Since the ranges of the sin and cos functions are the same, the range of either Formula 4 or Formula 5 on the right-hand side of the equation can be expressed as (-p, p). It can be seen that after taking the m-th derivative, the characteristics of the nth harmonic are amplified by a factor of n to m, while the amplification factor of the capacitive current component remains at 1.
[0143] S105. Extract degradation features from the maximum value of the signal based on the first derivative.
[0144] In this step, the degradation characteristic value is extracted by maximizing the value to obtain the maximum value of the first derivative processed signal. The maximum value amplifies the resistive fundamental frequency and harmonic components, and can directly reflect the degree of degradation.
[0145] For example, the peak value of the capacitive current is 1mA, the peak value of the fundamental frequency of the resistive current is 0.01mA, and the peak value of the third harmonic is 0.005mA. After taking the second derivative, the maximum value obtained is 1.055mA, that is, the degradation characteristic is represented by 1.055mA.
[0146] S106. When the degradation characteristics are valid and exceed the first preset threshold, the surge arrester is determined to be in a degraded state.
[0147] The first threshold in this step is set to 1mA. When the degradation characteristics are effective and exceed the first preset threshold, it can be determined that the surge arrester is in a degraded state.
[0148] For example, if the degradation characteristic is 1.32mA, and the first preset threshold is 120% of the peak value of the first derivative processed signal under normal conditions, i.e., 1mA, then the degradation characteristic exceeds the first preset threshold, and therefore the surge arrester is determined to be in a degraded state. If the degradation characteristic is 1.10mA, then the degradation characteristic does not exceed the first preset threshold, and therefore the surge arrester is determined not to be in a degraded state.
[0149] It should be noted that the method for determining the validity of degradation characteristics in this step is as follows: Figure 3 Further explanation will be provided in the embodiments shown, and will not be repeated here.
[0150] The surge arrester degradation detection method provided in this application involves collecting time-series data corresponding to the leakage current signal of the surge arrester, calculating the derivative order value based on the time-series data, performing derivative operations on the time-series data using the calculated derivative order value to obtain a first derivative operation result, performing data conversion based on the first derivative operation result to obtain a first derivative processing signal, extracting the degradation characteristics of the surge arrester based on the maximum value of the first derivative processing signal, and determining that the surge arrester is in a degraded state when the degradation characteristics are valid and exceed a first preset threshold. Compared with the prior art's method of using Fourier transform to achieve feature extraction, this application uses a multi-order derivative method to expand the feature confidence of the current signal, thereby enabling accurate acquisition of degradation characteristics and achieving the technical effect of improving detection accuracy.
[0151] Figure 3 Flowchart of the surge arrester degradation detection method provided in this application Figure 2 ,like Figure 3 As shown, the method includes:
[0152] S301. Based on the preset power frequency period, the time series data is divided into multiple time series subsequences.
[0153] In this step, the preset power frequency period refers to one sinusoidal signal period. The continuous leakage current signal corresponding to the time sequence data is divided into multiple power frequency period time sequence subsequences to capture periodic features.
[0154] For example, the seven points of the period can be determined by detecting the zero crossing point, and the length of the subsequence is the sampling frequency divided by the preset power frequency period.
[0155] For example, if the sampling frequency of the timing data corresponding to the leakage current signal is 10kHz and the preset power frequency period is 50Hz, then the length of the calculated timing subsequence is 200 points per cycle, and each point represents 1ms of data sampling; if the current timing data is a 6s long data, then 300 timing subsequences can be obtained by dividing the timing data.
[0156] S302. Based on the derivative order numerical value, perform derivative calculation for each time series subsequence to obtain the third derivative operation result corresponding to each time series subsequence.
[0157] In this step, the preset power frequency period is f, the sampling frequency is F, and the length of each time series subsequence is L=F / f. The final time series subsequence is denoted as the sequence set S={s1,s2,s3……,s k There are k time series subsequences in total. The derivative of each time series subsequence in the sequence set is calculated to obtain the third derivative result for each time series subsequence.
[0158] For example, suppose a period includes 4 data points, and there are two time series subsequences, namely s1 and s2, s1=[0.1,0.3,0.7,1.0] and s2=[0.2,0.6,0.8,0.9]; the derivative order is 1, the first derivative of s1 is [-,0.2,0.4,0.3], and the first derivative of s2 is [-,0.4,0.2,0.1].
[0159] S303. Perform signal conversion on each third derivative operation result to obtain the third derivative processing signal.
[0160] In this step, signal conversion refers to dividing the result after differentiation by the m-th power of the angular frequency, where m is the order of differentiation.
[0161] For example, there are two third derivative results: the first derivative of s1 is [-,0.2,0.4,0.3], and the first derivative of s2 is [-,0.4,0.2,0.1]. The angular frequency is 100π, the derivative order is 1, and the calculated third processed signals are: the third processed signal of s1 is [-,0.00064,0.00127,0.00095], and the third processed signal of s2 is [-,0.00127,0.00064,0.00032].
[0162] S304. Calculate the correlation coefficient for multiple third derivative processed signals to obtain the correlation coefficient matrix.
[0163] Alternatively, one possible way to calculate the correlation coefficient matrix is as follows:
[0164] S3041. Calculate the mean for each third derivative processed signal to obtain the signal mean of each third derivative processed signal.
[0165] In this step, the sequence set S = {s1, s2, s3, ..., s} k There are a total of k time series subsequences, and the corresponding signal set H = {h1, h2, h3, ..., h4} for the third derivative processing signals. k The mean of the third derivative processed signal is calculated to obtain the mean value of each third derivative processed signal, specifically the mean set E{e1,e2,e3……e}. k}
[0166] For example, the signal set is H={h1=[0.1,0.5,0.9,0.3],h2=[0.2,0.6,0.8,0.4],h3=[-0.3,0.1,0.4,-0.2]}. The mean set obtained by mean calculation is E{0.45,0.50,0.00}.
[0167] S3042. Based on the signal mean and the sub-signal values of each third derivative processed signal, the covariance matrix corresponding to multiple third derivative processed signals is calculated.
[0168] In this step, the signal set H = {h1, h2, h3, ..., h} k The corresponding covariance matrix is shown in Equations 6 and 7:
[0169]
[0170]
[0171] in, This represents the signal with index l corresponding to the i-th time-sequence subsequence in the signal set H. Similarly, The value of the signal corresponding to the j-th time-sequence subsequence in the signal set H, with index l. The signal h corresponding to the time series subsequence i The mean, The same principle applies; It refers to the covariance of the signals of time subsequence i and time subsequence j.
[0172] S3043. Calculate the correlation coefficient based on the covariance matrix to obtain the correlation coefficient matrix.
[0173] In this step, the correlation coefficient is calculated as shown in Formulas 8 and 9:
[0174]
[0175]
[0176] in, This refers to the correlation coefficient between the signals of time subsequence i and time subsequence j; This refers to the covariance of the signals of time subsequence i and time subsequence j; The signal h corresponding to the time series subsequence i standard deviation Similarly.
[0177] For example, the correlation coefficient matrix obtained by calculating the correlation coefficient of the signal set H={h1=[0.1,0.5,0.9,0.3],h2=[0.2,0.6,0.8,0.4],h3=[-0.3,0.1,0.4,-0.2]} corresponding to the sequence set is as follows: =[(1.0,1.0,-0.8),(1.0,1.0,-0.7),(-0.8,-0.7,1.0)].
[0178] S305. The effectiveness of determining the degradation characteristics of time series data based on the correlation coefficient matrix.
[0179] Alternatively, one possible way to determine the validity of degradation features is as follows:
[0180] S3051. Based on the correlation coefficient matrix, calculate the mean correlation coefficient of each row.
[0181] In this step, the mean correlation coefficient of each row in the correlation coefficient matrix is calculated, which means calculating the average similarity between a subsequence and all its other subsequences.
[0182] S3052. Sort the mean correlation coefficients of each row to obtain the median after sorting.
[0183] In this step, the median is the median obtained by taking the median value after sorting the correlation coefficients. Taking the median can resist the interference of outliers.
[0184] S3053. When the median is greater than or equal to the second preset threshold, the degradation characteristics of the time series data are determined to be valid.
[0185] In this step, if the median exceeds the second preset threshold, it indicates that the signal correlation coefficients of most time-series subsequences in the signal set are high, and the degradation characteristics are relatively consistent. Conversely, if the median does not exceed the threshold, it indicates that the correlation coefficients of most subsequences are low, which can be identified as interference.
[0186] For example, the calculated row mean is [0.92, 0.88, 0.90, 0.85, 0.93], the second preset threshold is 0.8, the sorted row mean is [0.85, 0.88, 0.90, 0.92, 0.93], the median is 0.90, which is greater than the second preset threshold, indicating that the degradation feature is effective.
[0187] S3054. When the median is less than the second preset threshold, the degradation characteristics of the time series data are determined to be invalid.
[0188] For example, the calculated row mean is [0.95, 0.32, 0.75, 0.28, 0.94], the second preset threshold is 0.8, and the row mean after sorting is [0.28, 0.32, 0.75, 0.94, 0.95]. The median of 0.75 is less than the second preset threshold, so the degradation feature is determined to be invalid.
[0189] Figure 4 Flowchart of the surge arrester degradation detection method provided in this application Figure 3 ,like Figure 4 As shown, the method includes:
[0190] B1. Collect the leakage current of the surge arrester.
[0191] B2. The derivative order m is obtained based on the leakage current calculation.
[0192] B3. Divide the leakage current into k subsequences.
[0193] B4. Calculate the m-th derivative of each subsequence.
[0194] B5. Calculate the processed signal based on the m-th derivative of each subsequence.
[0195] B6. Determine if the maximum value of the processed signal for each subsequence is greater than 1mA. If so, proceed to B7.
[0196] B7. Calculate the correlation coefficient for each processed signal.
[0197] B8. Calculate the mean of each correlation coefficient and sort them.
[0198] B9. Determine if the median of the sorted data is greater than 0.8. If yes, proceed to step 10; otherwise, proceed to step 11.
[0199] B10. Determine the deterioration of the surge arrester.
[0200] B11. Interference signal is confirmed.
[0201] Figure 5 This is a schematic diagram of the structure of the surge arrester deterioration detection device provided in this application, as shown below. Figure 5 As shown, the surge arrester degradation detection device provided in this embodiment includes:
[0202] The acquisition module 501 is used to acquire the timing data corresponding to the leakage current signal of the surge arrester;
[0203] The first processing module 502 is used to calculate the derivative order value based on the timing data corresponding to the current signal.
[0204] The second processing module 503 is used to perform derivative operations on the time series data based on the derivative order value to obtain the first derivative operation result.
[0205] The third processing module 504 is used to perform data conversion based on the first derivative operation result to obtain the first derivative processing signal.
[0206] The fourth processing module 505 is used to extract degradation features from the maximum value of the processed signal based on the first derivative.
[0207] The fifth processing module 506 is used to determine that the surge arrester is in a deteriorated state when the deterioration feature is valid and the deterioration feature exceeds the first preset threshold.
[0208] In one possible implementation, the first processing module 502 is further configured to:
[0209] The harmonic significance and noise level of the current signal are calculated based on preset order values and time series data.
[0210] Based on the harmonic significance and noise level, the value of the derivative order to be determined is calculated;
[0211] If the harmonic significance does not meet the preset conditions and the value of the derivative to be determined is lower than the preset upper limit, the preset order value is updated based on the value of the derivative to be determined, and iterative calculation is performed until the harmonic significance meets the preset conditions or the value of the derivative to be determined reaches the preset upper limit.
[0212] In one possible implementation, the first processing module 502 is further configured to:
[0213] The time series data is differentiated based on a preset order value to obtain the second derivative result.
[0214] Based on the result of the second derivative operation and the preset angular frequency, the second derivative processing signal is obtained.
[0215] The harmonic significance is calculated based on the signal peak value of the signal processed by the second derivative and the preset capacitive current reference value.
[0216] Current signal data within a preset voltage fluctuation period is obtained based on time-series data.
[0217] The noise level is obtained by calculating the standard deviation based on the current signal data.
[0218] In one possible implementation, the fourth processing module 505 is further configured to:
[0219] Based on a preset power frequency period, the time series data is divided into multiple time series subsequences.
[0220] Based on the numerical derivative order, the derivative is calculated for each time series subsequence to obtain the third derivative result for each time series subsequence.
[0221] For each third derivative operation result, a signal conversion is performed to obtain the third derivative processed signal.
[0222] The correlation coefficient matrix is obtained by calculating the correlation coefficients of multiple third derivative processed signals.
[0223] The effectiveness of determining the degradation characteristics of time series data based on the correlation coefficient matrix.
[0224] In one possible implementation, the fourth processing module 505 is further configured to:
[0225] The mean value of each third derivative processed signal is calculated.
[0226] Based on the signal mean and the sub-signal values of each third derivative processed signal, the covariance matrix corresponding to multiple third derivative processed signals is calculated.
[0227] The correlation coefficient matrix is obtained by calculating the correlation coefficient based on the covariance matrix.
[0228] In one possible implementation, the fourth processing module 505 is further configured to:
[0229] Based on the correlation coefficient matrix, calculate the mean correlation coefficient for each row.
[0230] Sort the mean correlation coefficients of each row to obtain the median after sorting.
[0231] When the median is greater than or equal to the second preset threshold, the degradation characteristics of the time series data are determined to be valid.
[0232] When the median is less than the second preset threshold, the degradation characteristics of the time series data are invalidated.
[0233] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.
[0234] Figure 6 A schematic diagram of the structure of the electronic device provided in this application. Figure 6 As shown, the electronic device provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device further includes a communication component 603. The processor 601, memory 602, and communication component 603 are connected via a bus 604.
[0235] In the specific implementation process, at least one processor 601 executes computer execution instructions stored in memory 602, causing at least one processor 601 to execute the above-mentioned surge arrester deterioration detection method or approach.
[0236] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.
[0237] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.
[0238] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.
[0239] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.
[0240] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0241] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.
[0242] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.
[0243] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.
[0244] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.
[0245] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0246] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0247] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0248] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.
[0249] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for detecting the deterioration of a surge arrester, characterized in that, include: Collect timing data corresponding to the leakage current signal of the surge arrester; Based on the time-series data corresponding to the current signal, the derivative order is calculated. Based on the derivative order value, the time series data is differentiated to obtain the first derivative result; Based on the result of the first derivative operation, data conversion is performed to obtain the first derivative processed signal; Degradation features are extracted based on the maximum value of the signal processed by the first derivative; When the degradation feature is valid and the degradation feature exceeds a first preset threshold, the surge arrester is determined to be in a degraded state.
2. The method according to claim 1, characterized in that, The step of calculating the derivative order based on the time-series data corresponding to the current signal includes: The harmonic significance and noise level of the current signal are calculated based on the preset order value and the time series data. Based on the harmonic significance and the noise level, the value of the derivative order to be determined is calculated. When the harmonic significance does not meet the preset condition and the value of the derivative order to be determined is lower than the preset upper limit, the preset order value is updated based on the value of the derivative order to be determined, and iterative calculation is performed until the harmonic significance meets the preset condition or the value of the derivative order to be determined reaches the preset upper limit.
3. The method according to claim 2, characterized in that, The calculation of the harmonic significance and noise level values of the current signal based on the time-series data includes: Based on the preset order value, the time series data is differentiated to obtain a second differentiation result; Based on the result of the second derivative operation and the preset angular frequency, the second derivative processing signal is obtained; The harmonic significance is calculated based on the signal peak value of the second derivative processed signal and the preset capacitive current reference value. Based on the timing data, current signal data within a preset voltage fluctuation period is obtained; The noise level value is obtained by calculating the standard deviation based on the current signal data.
4. The method according to claim 1, characterized in that, After extracting degradation features from the maximum value of the signal processed based on the first derivative, the method further includes: Based on a preset power frequency period, the time-series data is divided into multiple time-series subsequences; Based on the derivative order value, the derivative is calculated for each time series subsequence to obtain the third derivative operation result corresponding to each time series subsequence; For each result of the third derivative operation, a signal conversion is performed to obtain the third derivative processed signal; The correlation coefficient matrix is obtained by calculating the correlation coefficients of multiple third derivative processed signals. The effectiveness of determining the degradation characteristics of the time series data based on the correlation coefficient matrix.
5. The method according to claim 4, characterized in that, The step of calculating the correlation coefficient for multiple third derivative processed signals to obtain a correlation coefficient matrix includes: The mean value of each third derivative processed signal is calculated. Based on the signal mean and the sub-signal value of each third derivative processed signal, the covariance matrix corresponding to multiple third derivative processed signals is calculated. The correlation coefficient matrix is obtained by calculating the correlation coefficient based on the covariance matrix.
6. The method according to claim 4, characterized in that, The effectiveness of determining the degradation characteristics of the time series data based on the correlation coefficient matrix includes: Based on the correlation coefficient matrix, calculate the mean correlation coefficient for each row; Sort the mean correlation coefficients of each row to obtain the median after sorting; When the median is greater than or equal to the second preset threshold, the degradation characteristics of the time series data are determined to be valid; When the median is less than the second preset threshold, the degradation characteristics of the time series data are determined to be invalid.
7. A surge arrester deterioration detection device, characterized in that, include: The acquisition module is used to collect the timing data corresponding to the leakage current signal of the surge arrester; The first processing module is used to calculate the derivative order value based on the time-series data corresponding to the current signal; The second processing module is used to perform differentiation on the time series data based on the derivative order value to obtain the first differentiation result. The third processing module is used to perform data conversion based on the first derivative calculation result to obtain the first derivative processing signal; The fourth processing module is used to extract degradation features based on the maximum value of the signal processed by the first derivative; The fifth processing module is used to determine that the surge arrester is in a deteriorated state when the deterioration feature is valid and the deterioration feature exceeds a first preset threshold.
8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.
10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.