Surge arrester resistance current detection method and system under low resistance state

Abstract

This invention provides a method and system for detecting resistive current in surge arresters under low-resistivity conditions, belonging to the field of surge arrester technology. The method synchronously acquires reference voltage, total leakage current, and field electric field signals using a multi-channel synchronous acquisition card, with each channel sharing a clock and trigger to eliminate synchronization errors. Narrow-band filtering, covariance eigenvalue decomposition, and MUSIC spatial spectrum peak search are performed on the electric field signal using the reference voltage to extract the pure capacitive electric field signal of the surge arrester. Using this electric field signal as the amplitude and phase reference and the reference voltage as the verification reference, the resistive current component is separated from the total leakage current through segmented windowed FFT and power spectrum calculation, and the resistive current and its uncertainty are output. This invention effectively suppresses strong electromagnetic interference in the field, significantly improves the signal-to-noise ratio of weak signals, and solves the problems of large synchronization errors, low separation accuracy, and unevaluable results in traditional methods. It can achieve high-precision detection of resistive current in surge arresters under low-resistivity conditions and is suitable for online monitoring and condition assessment of surge arresters in power systems.

Description

Technical Field

[0001] This invention relates to the field of surge arrester technology, specifically to a method and system for detecting resistive current in surge arresters under low-resistivity conditions. Background Technology

[0002] Surge arresters, especially metal oxide surge arresters (MOAs), are crucial overvoltage protection devices in power systems. Their long-term reliability directly affects the safety and stability of the power grid. A small leakage current, typically 0.1 to 1 mA, flows through the MOA diaphragm under operating voltage. This leakage current consists of capacitive and resistive components, with the resistive current component being a key characteristic of insulation aging, moisture absorption, and performance degradation. Once initial degradation of the MOA occurs, its resistive current increases significantly. Therefore, accurate monitoring of the resistive current is the core means of achieving condition-based maintenance and fault early warning for MOAs.

[0003] However, actual on-site monitoring faces significant challenges. The resistive current component of the MOA is extremely weak, typically ranging from 0.01 microamps to 500 microamps (μA), and is submerged in a much larger background of capacitive leakage current. Even more serious is the complex electromagnetic environment at substation sites, characterized by various strong electromagnetic interferences such as primary system power frequency electromagnetic fields, transient interference from high-frequency switching operations, wireless communication signals, and coupling from other high-voltage equipment. The amplitudes of these interference signals are often far greater than the weak resistive current signal that needs to be extracted.

[0004] Currently, existing methods for sampling resistive current signals from surge arresters are mainly based on principles such as voltage divider sampling, feedthrough capacitance sensors, or wireless current sensors. While these methods are theoretically feasible, in the complex and highly interference-prone environments described above, the signal-to-noise ratio (SNR) of the sampled signals is excessively low. The main problems are as follows: The sensor itself and the signal transmission circuit are susceptible to interference from spatial electromagnetic field coupling, which introduces a large amount of noise. Harmonic analysis or compensation methods used to separate resistive components from total leakage current are highly dependent on the purity of the reference voltage signal for accuracy, and it is very difficult to obtain an undisturbed phase reference voltage signal on site.

[0005] Traditional PT reference method heavily relies on obtaining the voltage phase from the secondary side of the voltage transformer; the phase difference of the PT itself, the phase shift of the secondary circuit cable and the distributed capacitance effect will introduce phase errors that are difficult to calibrate accurately, and small phase errors will lead to huge deviations in resistive current calculation.

[0006] Due to the low signal-to-noise ratio, subsequent signal processing circuits and algorithms struggle to accurately extract true and effective resistive current information from the mixed, noisy sampling signals. This results in distorted monitoring results, false alarms, or an inability to effectively identify early faults, severely limiting the application effectiveness and reliability of online monitoring systems in practical engineering projects.

[0007] The leakage current of surge arresters is very small, ranging from 0.1 to 1 mA, and even smaller for resistive single current, ranging from 0.01 to 500 μA. However, there is a lot of interference in the field. The existing sampling methods for resistive current signals of surge arresters have too low a signal-to-noise ratio, making it impossible to accurately extract effective information.

[0008] Therefore, there is an urgent need in this field for a novel method for sampling the resistive current signal of surge arresters, which can effectively suppress complex electromagnetic interference in the field, significantly improve the signal-to-noise ratio of the sampled signal, and lay a solid foundation for subsequent accurate analysis and diagnosis. Summary of the Invention

[0009] The technical problem to be solved by the present invention is to provide a method and system for detecting the resistive current of a surge arrester under low resistance conditions. It aims to overcome the defects of the prior art, such as strong on-site interference, large signal synchronization error, low resistive current separation accuracy, and inability to quantify and evaluate the detection results. The invention provides a method, system, and storage medium for detecting the resistive current of a surge arrester under low resistance conditions, which has strong anti-interference ability, high synchronization accuracy, accurate separation, and evaluable results.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for detecting the resistive current of a surge arrester under low-resistivity conditions includes the following steps: Step 1: Install a voltage sensor on the secondary side of the voltage transformer to collect the bus voltage as a reference voltage signal; install a current sensor on the grounding lead of the surge arrester to collect the current as the total leakage current signal; and install a triaxial electric field sensor array at a distance L from the grounding lead of the surge arrester to collect the field electric field signal. Simultaneously collect the reference voltage signal, total leakage current signal, and field electric field signal using a multi-channel synchronous data acquisition card. Each acquisition channel shares the same master clock crystal and the same trigger, with the trigger using a preset power frequency reference value as the trigger signal. Step 2: Perform spatial filtering analysis on the field electric field signal in conjunction with the reference voltage signal to extract the pure capacitive electric field signal generated by the surge arrester; Step 3: Using the pure capacitive electric field signal as the amplitude and phase reference and the reference voltage signal as the verification reference, separate the resistive current component from the total leakage current signal; Step 4: Calculate the resistive current of the surge arrester and its uncertainty based on the resistive current component.

[0011] In step one above, each analog input channel of the multi-channel synchronous data acquisition card is connected to a voltage sensor, a current sensor, and a triaxial electric field sensor; the master clock crystal oscillator is the unified sampling clock for the analog-to-digital converters of each channel, and the ADCs of channel 0 and channel 1 are driven by the same master clock to achieve synchronous sampling at equal intervals; the preset power frequency reference quantity is a standard sine wave or square wave generated from the system voltage by a high-precision phase-locked loop (PLL) to ensure that the acquisition starts at the same phase point of the voltage waveform.

[0012] The spatial filtering analysis in step two above specifically includes: S21, obtain the field electric field matrix and reference voltage sequence; S22, Perform a Fast Fourier Transform on the reference voltage sequence to extract the fundamental frequency of the power grid. f 0, perform on the field electric field matrix with f Narrowband filtering centered on 0; S23, calculate the covariance matrix of the field electric field matrix after narrowband filtering and perform eigenvalue decomposition to construct the signal subspace matrix U. S With the noise subspace matrix U N ; S24, based on the reference voltage sequence and the signal subspace matrix U S Identify the signal source of the surge arrester; S25, based on the noise subspace matrix U N Construct a MUSIC spatial spectrum function, perform a three-dimensional spatial spectrum peak search to determine the direction of incoming waves, and calculate the optimal weight vector; S26. The optimal weight vector is applied to the field electric field sequence to extract the time-domain signal as a pure capacitive electric field signal.

[0013] In step S23 above, the covariance matrix R of the field electric field sequence after narrowband filtering is... EE H Where E is the L×N field electric field matrix and H is the matrix conjugate transpose; eigenvalues ​​and corresponding eigenvectors are obtained by eigendecomposition of R in order of magnitude, and the eigenvectors corresponding to the first D eigenvalues ​​constitute the signal subspace matrix U. S The remaining eigenvectors form the noise subspace matrix U. N D represents the number of signal sources within the preset range of the surge arrester.

[0014] In step S24 above, the signal subspace U S To obtain the source signal estimation matrix, an inverse transformation is performed on the field electric field matrix E to obtain the unmixing matrix. Calculate the cross-correlation coefficient between the estimated source signal and the reference voltage sequence for each signal source, and determine the signal source with the largest cross-correlation coefficient as the surge arrester signal source.

[0015] In step S26 above, the optimal weight vector W is calculated based on the MVDR beamformer, using the following formula: W= ; in The array manifold vector represents the direction of incoming waves. W is the inverse of the covariance matrix; applying W to the field electric field sequence yields the pure capacitive electric field signal A: A = [A(1), A(2), ..., A(N)] T =W T E; Where A(N) represents the Nth time-domain signal.

[0016] Step three above, separating the resistive current component, specifically includes: The pure capacitive electric field signal, total leakage current signal, and reference voltage signal are each divided into K data segments; each data segment is windowed and subjected to a Fast Fourier Transform to extract the fundamental frequency. f The self-power spectral density and cross-power spectral density at 0 are calculated; the phase difference between the pure capacitive electric field signal and the reference voltage signal is calculated; when the phase difference meets the threshold, the frequency response function is calculated to estimate the fundamental capacitive current; and the resistive current is obtained by subtracting the capacitive current from the total leakage current.

[0017] The aforementioned fundamental frequency f The spectral estimation at 0 includes: The self-power spectral density G of a pure capacitive electric field signal AA ( f 0)= ; Pure capacitive electric field and total leakage current cross-power spectral density G Ai ( f 0) = ; The cross-power spectral density G between the pure capacitive electric field and the reference voltage Au ( f 0) = ; in , , These are the discrete Fourier transform coefficients of the signals corresponding to the k-th pure capacitive electric field data segment, the total leakage current data segment, and the reference voltage data segment at f0, respectively.

[0018] The frequency response function mentioned above = G Ai ( f 0) / G AA ( f 0); Fundamental capacitive current i c = Acos( ); resistive current i r =i total - i c , where i total Let A be the total leakage current and A be the amplitude of the pure capacitive electric field signal.

[0019] In step four above, based on the resistive current components measured multiple times, statistical methods are used to calculate the standard deviation and expanded uncertainty of the resistive current, thereby quantifying the reliability of the test results.

[0020] A system using the above-described method for detecting the resistive current of a surge arrester under low-resistivity conditions includes: The synchronous sampling module is used to synchronously acquire the reference voltage signal, total leakage current signal and field electric field signal of the surge arrester; The filtering analysis module is used to perform spatial filtering on the field electric field signal in conjunction with the reference voltage signal to extract a pure capacitive electric field signal. The separation module is used to separate the resistive current component from the total leakage current, using a pure capacitive electric field signal as the amplitude and phase reference and a reference voltage signal as the verification reference. The output module is used to calculate and output the resistive current of the surge arrester and the uncertainty of the resistive current.

[0021] The computer-readable storage medium of the above-described arrester resistive current detection system under low resistance state is used to execute the arrester resistive current detection method under low resistance state.

[0022] The resistive current detection method and system for surge arresters under low-resistivity conditions mentioned in this invention have the following beneficial effects: 1. High synchronous sampling accuracy: The shared master clock crystal and trigger of the multi-channel synchronous data acquisition card eliminate the relative delay of multiple signals. Combined with the power frequency trigger signal generated by the high-precision PLL, periodic synchronization is achieved, avoiding spectrum leakage and providing reliable raw data for subsequent analysis. 2. Strong anti-interference capability: after initial screening of fundamental frequency clutter by narrowband filtering, separation of signal and noise subspace by covariance matrix eigenvalue decomposition, and MUSIC peak search combined with MVDR beamforming, it can accurately extract the pure capacitive electric field signal of the surge arrester from the complex field electric field. 3. Precise resistive current separation: Using a pure capacitive electric field as the amplitude and phase reference and a reference voltage as the verification reference (phase difference threshold to screen reliable signals), the fundamental capacitive current is precisely separated by segmented windowed FFT and mutual / self power spectral density calculation, thereby obtaining a reliable resistive current; 4. The results are evaluable. The innovative calculation of resistive current uncertainty quantifies the reliability of the results, making it easier for engineers to judge whether the test results meet the operation and maintenance standards and enhancing the value of engineering guidance. 5. Highly practical, adaptable to surge arresters of different voltage levels and installation environments. It can achieve fully automated detection through computer programs to reduce human error, and its modular architecture supports subsequent expansion and optimization to adapt to complex on-site conditions and technical upgrade needs. Attached Figure Description

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the surge arrester resistive current detection method under low-resistivity conditions according to the present invention; Figure 2 This is a structural diagram of the surge arrester resistive current detection system under low-resistivity conditions according to the present invention. Detailed Implementation

[0024] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0025] Example 1: Methods for detecting the resistive current of surge arresters under low-resistivity conditions include: Step 1: Synchronously sample the field electric field signal, reference voltage signal, and total leakage current signal of the surge arrester; this step specifically includes the following methods: A voltage sensor is installed on the secondary side of the voltage transformer to collect the bus voltage as a reference voltage signal. A current sensor is installed on the grounding lead of the surge arrester to collect the current as the total leakage current signal. A triaxial electric field sensor array is installed at a distance L from the grounding lead of the surge arrester to collect the field electric field signal. The system uses a multi-channel synchronous data acquisition card to synchronously acquire signals from a voltage sensor, a current sensor, and a triaxial electric field sensor array. The multi-channel synchronous data acquisition card has multiple analog input channels, with each of the voltage sensor, current sensor, and triaxial electric field sensor corresponding to one analog input channel. Each analog input channel shares the same master clock crystal and the same trigger. The trigger uses a preset power frequency reference quantity as its trigger signal.

[0026] The master clock crystal oscillator serves as the sampling clock for the analog-to-digital converters (ADCs) in each sensor. Inside the acquisition card, the ADC converters of channel 0 and channel 1 are driven by the same master clock, sampling at exactly the same time intervals. This ensures that the time axes of the two signals are completely synchronized, eliminating relative delay. The preset power frequency reference can be a standard sine wave or square wave generated from the system voltage by a high-precision phase-locked loop (PLL), ensuring that each acquisition begins at exactly the same phase point of the voltage waveform, achieving synchronization between cycles. This is crucial for subsequent spatial filtering analysis and the separation of resistive current components, preventing spectral leakage. This solution uses a multi-channel synchronous acquisition card, relying on its hardware-level shared clock and triggers to achieve perfect synchronization.

[0027] Step 2: Combine the reference voltage signal with spatial filtering analysis of the field electric field signal to extract the pure capacitive electric field signal generated by the surge arrester; This step specifically includes the following methods: S21, obtain the field electric field matrix and reference voltage sequence; S22, Perform a Fast Fourier Transform on the reference voltage sequence to extract the fundamental frequency of the power grid. f 0, perform on the field electric field matrix with f Narrowband filtering centered on 0; S23, calculate the covariance matrix of the field electric field matrix after narrowband filtering, and construct the signal subspace matrix U by eigenvalue decomposition of the covariance matrix. S and noise subspace matrix U N ; Signal subspace matrix U S and noise subspace matrix U N The construction methods include: The covariance matrix R of the field electric field sequence after narrowband filtering is: R = EE H Where H represents the transpose of the matrix; E represents the L×N field electric field matrix; Eigendecomposition of the covariance matrix R yields: R = ;in, Represents eigenvalues. ≥ ≥… ≥ ; Let l represent the eigenvector corresponding to the eigenvalue l; T represents the transpose of the vector. Determine the number of signal sources D within the preset range of the surge arrester, 0≤D≤L, and construct the signal subspace matrix U from the eigenvectors corresponding to the first D eigenvalues. S The noise subspace matrix U is constructed using the eigenvectors corresponding to the (D+1)th to the Lth eigenvalues. N .

[0028] S24, based on the reference voltage sequence and the signal subspace matrix U S Identify the surge arrester signal source; this step specifically includes the following methods: Signal subspace U S As the unmixing matrix, the source signal estimation matrix S is obtained by reversing the field electric field matrix E: Each row s of the source signal estimation matrix S d =[s1, s2, ..., s N ] represents the estimated source signal of the d-th signal source, where d = 1, 2, ..., D; Calculate the cross-correlation coefficient between the estimated source signal of each signal source and the reference voltage sequence, and use the signal source with the largest cross-correlation coefficient as the surge arrester signal source.

[0029] S25, based on the noise subspace matrix U N Construct a MUSIC spatial spectrum function, use the MUSIC spatial spectrum function to perform a three-dimensional spatial spectrum peak search on the surge arrester signal source, and calculate the optimal weight vector with the spectrum peak direction as the incoming wave direction. S26, apply the optimal weight vector to the in-situ electric field sequence to extract the time-domain signal as a pure capacitive electric field signal. This step specifically includes the following methods: Calculate the optimal weight vector W based on the MVDR beamformer: W= ; in, The array manifold vector represents the direction of incoming waves; T represents the transpose of the vector. The matrix representing the inverse of the covariance matrix R; Applying the optimal weight vector to the in-situ electric field sequence to extract the time-domain signal A yields: A = [A(1), A(2), ..., A(N)] T =W T E; Where A(N) represents the Nth time-domain signal; The time-domain signal A is used as the pure capacitive electric field signal.

[0030] Step 3: Using the pure capacitive electric field signal as the amplitude and phase reference and the reference voltage signal as the verification reference, the resistive current component is separated from the total leakage current signal. This step specifically includes the following method: the pure capacitive electric field signal, the total leakage current signal, and the reference voltage signal are simultaneously divided into K data segments. Windowing and Fast Fourier Transform are applied to each data segment to extract the fundamental frequency. f Spectral estimation at 0: G AA ( f0)= ; G Ai ( f 0) = ; G Au ( f 0) = ; Among them, G AA ( f 0) represents the self-power spectral density of a pure capacitive electric field signal at the fundamental frequency. f The value at 0; G Ai ( f 0) represents the cross-power spectral density of the pure capacitive electric field signal and the total leakage current signal at the fundamental frequency. f The value at 0; G Au ( f 0) represents the cross-power spectral density of the pure capacitive electric field signal and the reference voltage signal at the fundamental frequency. f The value at 0; This indicates that the k-th pure capacitive electric field data segment is at the fundamental frequency. f Discrete Fourier transform coefficients at point 0; This indicates the k-th total leakage current data segment at the fundamental frequency. f Discrete Fourier transform coefficients at point 0; This indicates that the k-th reference voltage data segment is at the fundamental frequency. f Discrete Fourier transform coefficients at point 0; ; ; ;in, Represents the window function.

[0031] Calculate the phase difference φ = ∠G between the pure capacitive electric field signal and the reference voltage signal. Au ( f 0); (The phase difference φ should be close to zero. If the phase difference φ deviates too much, the pure capacitive electric field signal is unreliable, so the following calculations are not performed.) When the phase difference meets the threshold range, the fundamental capacitive current can be estimated by calculating the frequency response function: i c = Acos( ); in, Represents the frequency response function. = G Ai ( f 0) / G AA ( f 0); Indicates calculation Angle; Extract the resistive current from the fundamental capacitive current: i r =i total - i c .

[0032] Step 4: Obtain the resistive current of the surge arrester and its uncertainty based on the resistive current component.

[0033] This solution also provides a surge arrester resistive current detection system to implement the aforementioned surge arrester resistive current acquisition method; such as Figure 2 As shown, the system includes: The synchronous sampling module is used to synchronously sample the field electric field signal, reference voltage signal, and total leakage current signal of the surge arrester. The filtering analysis module is used to perform spatial filtering analysis on the field electric field signal in conjunction with the reference voltage signal to extract the pure capacitive electric field signal generated by the surge arrester. The separation module is used to separate the resistive current component from the total leakage current signal, using a pure capacitive electric field signal as the amplitude and phase reference and a reference voltage signal as the verification reference. The output module is used to obtain the resistive current of the surge arrester and the uncertainty of the resistive current based on the resistive current component.

[0034] This solution also provides a computer-readable medium storing a computer program, which, when executed by a processor, can implement the above-described method for obtaining the resistive current of a surge arrester; specifically, it performs the following steps: Step 1: Simultaneously sample the field electric field signal, reference voltage signal, and total leakage current signal of the surge arrester; Step 2: Combine the reference voltage signal with spatial filtering analysis of the field electric field signal to extract the pure capacitive electric field signal generated by the surge arrester; Step 3: Using the pure capacitive electric field signal as the amplitude and phase reference and the reference voltage signal as the verification reference, separate the resistive current component from the total leakage current signal. Step 4: Obtain the resistive current of the surge arrester and its uncertainty based on the resistive current component.

Claims

1. A method for detecting the resistive current of a surge arrester under low-resistivity conditions, characterized in that, Includes the following steps: Step 1: Set up a voltage sensor on the secondary side of the voltage transformer to collect the bus voltage as a reference voltage signal, set up a current sensor on the grounding lead of the surge arrester to collect the current as the total leakage current signal, and set up a triaxial electric field sensor array at a distance L from the grounding lead of the surge arrester to collect the field electric field signal. The reference voltage signal, total leakage current signal and field electric field signal are synchronously acquired based on a multi-channel synchronous data acquisition card. Each acquisition channel shares the same master clock crystal and the same trigger. The trigger uses a preset power frequency reference quantity as the trigger signal. Step 2: Perform spatial filtering analysis on the field electric field signal in conjunction with the reference voltage signal to extract the pure capacitive electric field signal generated by the surge arrester; Step 3: Using the pure capacitive electric field signal as the amplitude and phase reference and the reference voltage signal as the verification reference, separate the resistive current component from the total leakage current signal; Step 4: Calculate the resistive current of the surge arrester and its uncertainty based on the resistive current component.

2. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 1, characterized in that, In step one, each analog input channel of the multi-channel synchronous data acquisition card is connected to a voltage sensor, a current sensor, and a triaxial electric field sensor; the master clock crystal oscillator is the unified sampling clock for the analog-to-digital converters of each channel, and the ADCs of channel 0 and channel 1 are driven by the same master clock to achieve synchronous sampling at equal intervals; the preset power frequency reference quantity is a standard sine wave or square wave generated from the system voltage by a high-precision phase-locked loop (PLL) to ensure that the acquisition starts at the same phase point of the voltage waveform.

3. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 1, characterized in that, The spatial filtering analysis in step two specifically includes: S21, obtain the field electric field matrix and reference voltage sequence; S22, Perform a Fast Fourier Transform on the reference voltage sequence to extract the fundamental frequency of the power grid. f 0, perform on the field electric field matrix with f Narrowband filtering centered on 0; S23, calculate the covariance matrix of the field electric field matrix after narrowband filtering and perform eigenvalue decomposition to construct the signal subspace matrix U. S With the noise subspace matrix U N ; S24, based on the reference voltage sequence and the signal subspace matrix U S Identify the signal source of the surge arrester; S25, based on the noise subspace matrix U N Construct a MUSIC spatial spectrum function, perform a three-dimensional spatial spectrum peak search to determine the direction of incoming waves, and calculate the optimal weight vector; S26. The optimal weight vector is applied to the field electric field sequence to extract the time-domain signal as a pure capacitive electric field signal.

4. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 3, characterized in that, In step S23, the covariance matrix R of the narrowband filtered field electric field sequence is... EE H Where E is the L×N field electric field matrix and H is the matrix conjugate transpose; eigenvalues ​​and corresponding eigenvectors are obtained by eigendecomposition of R in order of magnitude, and the eigenvectors corresponding to the first D eigenvalues ​​constitute the signal subspace matrix U. S The remaining eigenvectors form the noise subspace matrix U. N D represents the number of signal sources within the preset range of the surge arrester.

5. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 3, characterized in that, In step S24, the signal subspace U S To obtain the source signal estimation matrix, an inverse transformation is performed on the field electric field matrix E to obtain the unmixing matrix. ; Calculate the cross-correlation coefficient between the estimated source signal and the reference voltage sequence for each signal source, and identify the signal source with the largest cross-correlation coefficient as the surge arrester signal source.

6. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 3, characterized in that, In step S26, the optimal weight vector W is calculated based on the MVDR beamformer, using the following formula: W= ； in The array manifold vector represents the direction of incoming waves. W is the inverse of the covariance matrix; applying W to the field electric field sequence yields the pure capacitive electric field signal A: A=[A(1),A(2),…,A(N)] T =W T E; Where A(N) represents the Nth time-domain signal.

7. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 1, characterized in that, Step three, separating the resistive current component, specifically includes: The pure capacitive electric field signal, total leakage current signal, and reference voltage signal are each divided into K data segments; each data segment is windowed and subjected to a Fast Fourier Transform to extract the fundamental frequency. f The self-power spectral density and cross-power spectral density at 0 are calculated; the phase difference between the pure capacitive electric field signal and the reference voltage signal is calculated; when the phase difference meets the threshold, the frequency response function is calculated to estimate the fundamental capacitive current; and the resistive current is obtained by subtracting the capacitive current from the total leakage current.

8. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 7, characterized in that, The fundamental frequency f The spectral estimation at 0 includes: The self-power spectral density G of a pure capacitive electric field signal AA ( f 0)= ; Pure capacitive electric field and total leakage current cross-power spectral density G Ai ( f 0) = ; The cross-power spectral density G between the pure capacitive electric field and the reference voltage Au ( f 0) = ; in , , These are the discrete Fourier transform coefficients of the signals corresponding to the k-th pure capacitive electric field data segment, the total leakage current data segment, and the reference voltage data segment at f0, respectively.

9. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 8, characterized in that, The frequency response function = G Ai ( f 0) / G AA ( f 0); Fundamental capacitive current i c = Acos( ); resistive current i r =i total - i c , where i total Let A be the total leakage current and A be the amplitude of the pure capacitive electric field signal.

10. The method for detecting the resistive current of a surge arrester under low-resistivity conditions according to claim 1, characterized in that, In step four, based on the resistive current components measured multiple times, statistical methods are used to calculate the standard deviation and expanded uncertainty of the resistive current, thereby quantifying the reliability of the test results.

11. A system using the resistive current detection method for surge arresters in a low-resistivity state according to any one of claims 1-10, characterized in that, include: The synchronous sampling module is used to synchronously acquire the reference voltage signal, total leakage current signal and field electric field signal of the surge arrester; The filtering analysis module is used to perform spatial filtering on the field electric field signal in conjunction with the reference voltage signal to extract a pure capacitive electric field signal. The separation module is used to separate the resistive current component from the total leakage current, using a pure capacitive electric field signal as the amplitude and phase reference and a reference voltage signal as the verification reference. The output module is used to calculate and output the resistive current of the surge arrester and the uncertainty of the resistive current.

12. A computer-readable storage medium using the resistive current detection system for surge arresters in a low-resistivity state as described in claim 11, characterized in that, A computer-readable storage medium is used to perform a method for detecting the resistive current of a surge arrester under low-resistivity conditions.

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