A metal oxide surge arrester leakage current decoupling method, system, electronic device, readable storage medium and arrester operation state evaluation and early warning method

Abstract

This invention relates to a method, system, electronic device, readable storage medium, and method for assessing and warning the operating status of a metal oxide surge arrester (MOA), belonging to the field of power equipment monitoring and maintenance technology. The leakage current decoupling method includes: synchronously acquiring the voltage signal and total leakage current signal across the surge arrester; extracting the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal based on discrete Fourier transform; obtaining the capacitive current component corresponding to each harmonic through orthogonal projection calculation and a temperature-frequency compensation model based on the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic; superimposing the capacitive current components corresponding to each harmonic to obtain the total capacitive current, and decoupling the resistive current component of the surge arrester from the total leakage current signal. This achieves high-precision decoupling of the resistive current component of the MOA.

Description

Technical Field

[0001] This invention relates to the field of power equipment monitoring and maintenance technology, and in particular to a method, system, electronic device, readable storage medium, and method for assessing and warning of the operating status of a metal oxide surge arrester. Background Technology

[0002] In power systems, metal oxide arresters (MOAs) are critical protective devices whose main function is to limit overvoltages and protect power equipment from damage caused by lightning strikes and switching overvoltages. However, with increasing operating time, the performance of MOAs gradually declines, and severe performance degradation can directly affect the reliability of the power grid. Currently, in engineering practice, the aging condition of arresters is generally assessed by monitoring the resistive component of the leakage current. However, since the total leakage current of an MOA simultaneously contains a resistive component in phase with the voltage and a capacitive component quadrature with it, the two components need to be decoupled to accurately obtain the resistive current component used for assessment.

[0003] Traditional decoupling methods are usually based on simplified circuit models, which treat the surge arrester as a parallel structure of a fixed capacitor and a nonlinear resistor. However, this simplified model is too idealistic and differs greatly from the actual working environment of the MOA, and has very obvious defects: (1) It ignores the frequency characteristics of the capacitor. The capacitance value of the MOA changes significantly with the frequency in actual operation. The fixed capacitor model will introduce errors under harmonic conditions; (2) It ignores the influence of temperature. The capacitance value of the MOA will change with the temperature. Traditional methods lack temperature compensation mechanisms, resulting in poor adaptability to operating conditions; (3) It does not make full use of harmonics. Existing decoupling methods mostly focus on fundamental or single harmonic analysis and fail to make full use of the deep correlation between the wide frequency domain harmonic distribution characteristics and the degree of aging.

[0004] Furthermore, most existing algorithms require complex iterative calculations or rely on the assumption of an ideal sine wave, making it difficult to meet the requirements of real-time performance, accuracy, and robustness for on-site online monitoring. Therefore, achieving high-precision decoupling and separation of the two components of MOA leakage current under actual operating conditions has become a technical challenge. With the advancement of smart grid construction, there is an urgent need to develop leakage current decoupling technology that combines high precision, strong adaptability, and clear aging indication functions. Summary of the Invention

[0005] Based on the above analysis, the embodiments of the present invention aim to provide a method, system, electronic device, readable storage medium, and method for assessing and warning of the operating status of a metal oxide surge arrester (MOA), in order to solve the problem that the prior art cannot accurately decouple the resistive current component of the MOA, and thus cannot accurately assess the operating status of the surge arrester.

[0006] On one hand, embodiments of the present invention provide a high-precision decoupling method for metal oxide surge arresters, the method comprising the following steps: Simultaneously acquire the voltage signal and total leakage current signal across the surge arrester; Based on the discrete Fourier transform, the voltage amplitude and voltage phase angle corresponding to each harmonic are extracted from the voltage signal, and the current amplitude and current phase angle corresponding to each harmonic are extracted from the total leakage current signal. Based on the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic, the capacitive current components corresponding to each harmonic are obtained through orthogonal projection calculation and temperature-frequency compensation model. The total capacitive current is obtained by superimposing the capacitive current components corresponding to each harmonic, and the resistive current component of the surge arrester is decoupled from the total leakage current signal based on the total capacitive current.

[0007] A further improvement to the above method is that the method also includes the step of: establishing the temperature-frequency compensation model based on the measured data of the reference surge arrester sample.

[0008] Based on a further improvement of the above method, the establishment of the temperature frequency compensation model further includes the following steps: Preset surge arrester equivalent capacitance C ( n , T With harmonic order n and ambient temperature T Changing nonlinear function: ; Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β For frequency response coefficients; Obtain the sinusoidal voltage signal of the reference surge arrester sample at multiple sampling temperature points, and calculate the equivalent capacitance value of the reference surge arrester sample under each temperature and voltage frequency combination; Based on the fundamental capacitance value at the selected reference temperature and multiple equivalent capacitance values, a global curve fitting of the nonlinear function is performed using the nonlinear least squares method to obtain the optimal value that satisfies the objective. α and β Parameter values; The optimal α and β The parameter values ​​are substituted into the nonlinear function to establish a stable temperature-frequency compensation model.

[0009] A further improvement to the above method includes the step of: calculating the theoretical amplitude of the capacitive current component corresponding to the nth harmonic based on the equivalent capacitance value obtained from the established temperature-frequency compensation model and the voltage amplitude corresponding to the nth harmonic. : ; Where ω is the angular frequency of the fundamental wave. It refers to the amplitude of the nth harmonic voltage.

[0010] Based on a further improvement to the above method, the orthogonal projection calculation also includes the following steps: Based on the current amplitude corresponding to the nth harmonic Current phase angle θ in and voltage phase angle θ vn Calculate the projected maximum value of the capacitive current component corresponding to the nth harmonic. : .

[0011] Based on a further improvement of the above method, the method further includes the step of: using the theoretical amplitude of the capacitive current component obtained through the temperature frequency compensation model to verify and correct the maximum projection value of the capacitive current component calculated by orthogonal projection.

[0012] Based on a further improvement to the above method, the verification and correction also includes the following steps: Compare the relative deviations between the theoretical amplitude and the projected maximum value; When the relative deviation is within a preset range, the maximum value of the projection is used as the actual value of the capacitive current component corresponding to the current nth harmonic; When the relative deviation exceeds the preset range, the theoretical amplitude is used as the actual value of the capacitive current component corresponding to the current nth harmonic, or a calibration alarm is issued for recalculation.

[0013] On the other hand, embodiments of the present invention provide a method for assessing and warning of the operating status of surge arresters, including: The resistive current component of the surge arrester is obtained based on the leakage current decoupling method of the metal oxide surge arrester described in any of the preceding claims; Based on the discrete Fourier transform, the resistive current amplitude and resistive current phase angle corresponding to each harmonic are extracted from the decoupled resistive current components. Multiple characteristic indicators are obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic. Based on threshold comparison and / or trend analysis of the aforementioned multiple characteristic indicators, the operating status of the surge arrester is evaluated and an early warning is issued.

[0014] Based on a further improvement of the above method, the acquisition of multiple feature indicators includes the following steps: The fundamental amplitude of the resistive current and the odd-even harmonic ratio of the resistive current are extracted based on the resistive current amplitude corresponding to each harmonic. The approximate power consumption of the surge arrester is obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic.

[0015] Based on a further improvement to the above method, the method further includes the following steps: When conducting assessments and early warnings based on threshold comparisons, the thresholds for each characteristic indicator are set using a dynamic adaptive approach based on the health baseline. When conducting assessments and early warnings based on trend analysis, the time series change trends of various characteristic indicators are analyzed according to historical data. When the slope suddenly increases or an accelerating upward trend appears, an early warning is triggered.

[0016] Based on a further improvement to the above method, the method further includes the following steps: A classification model is trained using historical normal and fault data. The classification model identifies the categories corresponding to the multiple feature indicators in real time, and performs intelligent diagnosis of the operating status of the surge arrester.

[0017] Based on a further improvement to the above method, the method further includes the following steps: The classification model is a random forest model, which uses historical normal and fault data to train multiple decision trees and construct a random forest; Multiple feature indicators obtained through real-time decoupling are input into the random forest, and each decision tree outputs an independent prediction result. The prediction results of all decision trees are statistically analyzed, and the category with the most votes is taken as the final diagnostic result of the operating status of the surge arrester.

[0018] On the other hand, embodiments of the present invention provide a leakage current decoupling system for metal oxide surge arresters, the system comprising: The signal acquisition module is used to synchronously acquire the voltage signal and total leakage current signal across the surge arrester. The DFT module is used to extract the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal, respectively, based on the discrete Fourier transform. The capacitive component acquisition module is used to obtain the capacitive current component corresponding to each harmonic based on the voltage amplitude, voltage phase angle, current amplitude and current phase angle corresponding to each harmonic, through orthogonal projection calculation and temperature frequency compensation model. The resistive component decoupling module is used to superimpose the capacitive current components corresponding to each harmonic to obtain the total capacitive current, and decouple the resistive current component of the surge arrester from the total leakage current signal based on the total capacitive current.

[0019] Based on further improvements to the above system, the system also includes: a temperature and frequency compensation model training module, used to establish the temperature and frequency compensation model based on the measured data of the reference surge arrester sample.

[0020] Based on further improvements to the above system, the temperature frequency compensation model training module also includes: The function setting module is used to preset the equivalent capacitance of the surge arrester. C ( n , T With harmonic order n and ambient temperature T Changing nonlinear function: ; Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β For frequency response coefficients; The sampling module is used to acquire the sinusoidal voltage signal of the reference surge arrester sample at multiple sampling temperature points, and to calculate the equivalent capacitance value of the reference surge arrester sample under each combination of temperature and voltage frequency. The parameter solving module is used to perform global curve fitting on the nonlinear function using the nonlinear least squares method based on the fundamental capacitance value at the selected reference temperature and multiple equivalent capacitance values, to obtain the optimal solution that satisfies the objective. α and β Parameter values; The model building module is used to convert the optimal... α and β The parameter values ​​are substituted into the nonlinear function to establish a stable temperature-frequency compensation model.

[0021] On the other hand, embodiments of the present invention also provide an electronic device, the electronic device including a processor and a memory; wherein, the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the metal oxide surge arrester leakage current decoupling method as described above.

[0022] On the other hand, embodiments of the present invention also provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the metal oxide surge arrester leakage current decoupling method as described above.

[0023] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. Orthogonal Decomposition of Multiple Harmonic Phasors: This invention performs DFT analysis on synchronously acquired voltage and current signals to accurately extract the amplitude and phase information of the 1st to 7th harmonics. Subsequently, under each harmonic, utilizing the physical characteristics that resistive current and voltage are in phase and capacitive current and voltage are orthogonal, component decomposition is performed through geometric projection. This method overcomes the limitation of traditional methods that only analyze the fundamental wave, fully utilizes the wide frequency domain information of leakage current, and fundamentally solves the problem of low decoupling accuracy under harmonic voltage conditions.

[0024] 2. Dynamic Compensation of Temperature-Frequency Coupled Capacitor Parameters: This invention establishes a C(n,T) nonlinear model to describe the dynamic changes of capacitance with temperature and frequency, and uses real-time temperature data to calculate the theoretical capacitance current. This theoretical value serves as a benchmark to verify and correct the results of orthogonal projection, forming a closed-loop correction mechanism. This approach overcomes the model mismatch error caused by temperature and frequency changes in traditional fixed capacitance models, significantly improving the adaptability, accuracy, and reliability of the method under complex operating conditions.

[0025] 3. Multi-dimensional state feature extraction and intelligent early warning based on decoupling results: This invention extracts the fundamental amplitude I of the resistive current from the resistive current after high-precision decoupling. R1 Three key characteristics—even and odd harmonic ratio (K), approximate power consumption (P)—are used to construct a comprehensive feature vector, which is then integrated with threshold, trend, and machine learning algorithms for status assessment and early warning. This approach represents a leap from "monitoring" to "diagnosis." The K and P values ​​are more sensitive to early aging; combined with intelligent algorithms, a comprehensive assessment of the arrester's health status and early fault warning can be achieved, providing precise decision-making basis for condition-based maintenance.

[0026] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0028] Figure 1 This is a simplified equivalent model diagram of metal oxide surge arresters in the prior art; Figure 2 This is a schematic diagram illustrating the phasor relationship between the voltage and current of the surge arrester in an embodiment of the present invention; Figure 3 This is a schematic diagram of a high-precision decoupling method for metal oxide surge arresters in one embodiment of the present invention; Figure 4 This is a schematic diagram comparing the decoupling effect of a preferred embodiment of the present invention with that of the prior art; Figure 5 This is a schematic diagram of the system logic structure for monitoring the operating status of a surge arrester in a preferred embodiment of the present invention. Detailed Implementation

[0029] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0030] Under normal operating conditions, a small leakage current path exists in the surge arrester. When operating in the low current region, methods such as... Figure 1 The simplified model shown provides an electrical representation of the surge arrester. In this simplified model, the surge arrester is represented by a capacitor (C) connected in parallel with a nonlinear resistor (R). Figure 1 V is the voltage applied by the surge arrester, and I is... T It is the total leakage current, I R It is the resistive current component, I C It is the capacitive current component. Among them, the resistive current component and its harmonics are key indicators for assessing the aging of surge arresters. Extracting the resistive current component from the total leakage current is very important for surge arrester condition monitoring.

[0031] The decoupling of the resistive current components depends primarily on the orthogonality between the two current components. Figure 1 In the simplified equivalent model, the total leakage current (I T ) is a nonlinear resistive current (I R ) and capacitive current (I C The vector sum of the components can be expressed as follows: Figure 2 The vector diagram shown. Figure 2 In, θ v It is the phase angle of the applied voltage, which lies between the voltage and the reference vector; θ i It is the phase angle of the total leakage current, which lies between the leakage current and the reference vector; the resistive current component is in phase with the applied voltage, but the capacitive current component is perpendicular to the applied voltage. According to Figure 2The phase angle between the total leakage current and the resistive current is (θ). i -θ v The resistive current component can be calculated from the total leakage current.

[0032] However, as is well known, any voltage source contains harmonics, so the actual applied voltage also includes multiple harmonic components. Existing technology extracts the resistive current component from the total leakage current based on a simplified equivalent model and using only a pure sine wave applied voltage, which significantly reduces the accuracy of the data. Therefore, to accurately obtain the resistive component from the total leakage current, the errors caused by voltage harmonics must be accurately eliminated.

[0033] In one specific embodiment of the present invention, a high-precision decoupling method for metal oxide surge arresters is disclosed, such as... Figure 3 As shown, the method includes the following steps: S11, synchronously acquire the voltage signal and total leakage current signal at both ends of the surge arrester; S12, based on discrete Fourier transform, extract the voltage amplitude, voltage phase angle, current amplitude and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal respectively; S13, based on the voltage amplitude, voltage phase angle, current amplitude and current phase angle corresponding to each harmonic, the capacitive current component corresponding to each harmonic is obtained by orthogonal projection calculation and temperature frequency compensation model. S14, the capacitive current components corresponding to each harmonic are superimposed to obtain the total capacitive current, and the resistive current component of the surge arrester is decoupled from the total leakage current signal based on the total capacitive current.

[0034] Compared with the prior art, the embodiments of the present invention provide a surge arrester leakage current decoupling method based on multi-harmonic phasor orthogonal decomposition and temperature-frequency coupling compensation. This method can accurately obtain the resistive component from the total leakage current through simple steps under voltage harmonics. It not only has high-precision decoupling capability, but also can work stably under complex conditions such as power grid harmonic distortion and ambient temperature changes. Furthermore, it extracts multi-dimensional feature quantities for comprehensive assessment and early warning of the surge arrester's health status.

[0035] In an embodiment of the present invention, the voltage signal V(V) across the surge arrester is first synchronously acquired. t ) and total leakage current signal i T ( t Among these measures, anti-aliasing filtering and synchronous sampling are performed on the signal. To ensure that subsequent signal processing can accurately separate the effects of voltage harmonics, the sampling frequency is [specified]. f s It must satisfy Nyquist's theorem, that is fs ≥2 N f 0, where f 0 represents the fundamental frequency of the voltage. N This represents the highest harmonic order for analysis. For power grid signals within my country, the fundamental voltage frequency... f 0 is typically 50Hz. Furthermore, based on experience, the amplitude of voltage harmonic components above the 7th order is negligible. Therefore, it is preferable to analyze only the influence of voltage harmonics up to the 7th order, i.e., the highest harmonic order to analyze. N The preferred value is 7.

[0036] Subsequently, the Discrete Fourier Transform (DFT) was used to extract the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal, respectively.

[0037] Specifically, the Discrete Fourier Transform (DFT) is a mathematical tool for transforming a finite-length discrete signal from the time domain to the frequency domain. It discretizes the spectrum by sampling the signal in the frequency domain, thus facilitating computer processing. A typical definition of the DFT is: ; in, X [ k The ] represents the complex spectrum at the k-th frequency point in the frequency domain, for example, for a voltage signal. V [ 1 ] can represent the fundamental voltage component. V [ 3 This can represent the third voltage harmonic component; for the total leakage current signal, I [ 5 This can represent the total leakage current component corresponding to the 5th voltage harmonic. I [ 7 This can represent the total leakage current component corresponding to the 7th voltage harmonic; x ( m ) represents the first discrete-time signal. m Each sampled value contains harmonic amplitude (mode) and phase (argument) information; it refers to the voltage V of the surge arrester. t or total leakage current i T ( t A sequentially arranged sampling sequence; m Indicates the index of the time-domain sampling point (0≤ m ≤ M 1) Corresponding sampling time t = m / f s Or, in other words, the index of the sampling point corresponding to time t. m = t·f s Therefore, the voltage V collected at time t can be obtained. t or total leakage current i T ( t ) corresponds to the DFT sampling sequence V( m )or i T ( m )middle; k Represents the frequency point index in the frequency domain (1≤ k ≤ N As mentioned above, the highest harmonic order analyzed in this embodiment of the invention is... N The preferred value is 7; M This indicates the total length of the sampling sequence, i.e., the number of samplings during synchronous signal acquisition; j It is the imaginary unit; 2π / M This represents the radian interval corresponding to the frequency resolution.

[0038] Therefore, by applying Discrete Fourier Transform (DFT) to the synchronously acquired voltage signal and total leakage current signal to obtain the corresponding harmonic components, and then calculating the amplitude and phase angle according to the polar coordinate representation corresponding to the complex form of each harmonic component, the amplitude V of the voltage harmonic components from the fundamental wave to the 7th harmonic can be obtained. max,k (1≤ k ≤ 7 ) and phase angle θ vk ; and the amplitude I of the current harmonic components corresponding to the fundamental frequency to the 7th harmonic. Tmax,k and phase angle θ ik .

[0039] When implemented, the plural X [ k Expressed in polar coordinates, let:

[0040] in Re{} represents taking the real part, and Im{} represents taking the imaginary part.

[0041] Amplitude:

[0042] Phase angle: .

[0043] Substituting the harmonic components of the voltage signal into the above calculation formula yields the amplitude and phase angle of each harmonic component. Similarly, substituting the harmonic components of the total leakage current signal into the above calculation formula yields the amplitude and phase angle of each harmonic component.

[0044] according to Figure 2 As illustrated, to accurately determine the resistive current component, the capacitive current component needs to be subtracted from the total leakage current. In the embodiments of the present invention, accurate calculation of the capacitive current component is achieved, wherein the theoretical capacitive current is first calculated through dynamic compensation of temperature-frequency coupling capacitor parameters.

[0045] In a preferred embodiment of the present invention, an equivalent capacitance of the surge arrester is established. C ( n , T With harmonic order n and ambient temperature T A variable nonlinear model (temperature-frequency compensation model): ; Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β These are frequency characteristic coefficients. The two coefficients are determined through a standardized offline calibration process for characteristic parameters of a specific type of surge arrester.

[0046] The specific implementation process is as follows: In the laboratory, a sample of the same model of surge arrester was placed in a temperature-controlled chamber and connected to a high-precision impedance analyzer and a programmable voltage source. Multiple temperature points T were selected within the typical operating temperature range of the surge arrester. i (e.g., -10°C, 10°C, 30°C, 50°C, 70°C, etc.). At each temperature point, a frequency covering the fundamental to the 7th harmonic is applied. f n Sinusoidal voltage signals at (50Hz, 100Hz, 150Hz, 200Hz, 250Hz, 300Hz, 350Hz).

[0047] In each (temperature T) i ,frequency f n Under the combined conditions, a high-precision impedance analyzer is used to directly measure the complex admittance of the surge arrester sample and accurately calculate its equivalent capacitance value C. measured (n, T i ).

[0048] Specify a reference temperature T0 (typically 25°C), and the fundamental (50Hz) capacitance measurement at that temperature is used as the reference capacitance. C 0.

[0049] All measured C values ​​in the entire test matrix measured (n, T i )and C Substituting 0 into the above nonlinear model yields an overdetermined system of equations.

[0050] A nonlinear least squares method is used to perform global curve fitting on this system of equations. The goal of the fitting algorithm is to find an optimal set of curves. α and β The parameter values ​​are such that all capacitance values ​​C calculated by the nonlinear model are... calculated (n, T i ) and measured value C measured (n, T i The overall error between them is the smallest.

[0051] Substituting the optimal parameters into the nonlinear model yields a stable model output, i.e., the output is determined by any given harmonic order. n and ambient temperature T The equivalent capacitance value of this type of surge arrester can be obtained directly. C ( n , T ).

[0052] Based on the equivalent capacitance value obtained from this model, the theoretical amplitude of the capacitive current under each harmonic can be calculated. This theoretical value will be used for subsequent verification and correction. ; Where n is the harmonic order; ω refers to the angular frequency of the fundamental wave, ω=2. πf 0, f 0 represents the fundamental frequency of the power system; V max,n It refers to the amplitude (peak value) of the nth harmonic voltage.

[0053] The actual values ​​of the capacitive current components under each harmonic are calculated and extracted based on orthogonal projection, and then corrected using the theoretical values ​​obtained from the aforementioned temperature-frequency compensation. Specifically, the difference in phase angle between the current and voltage signals of each harmonic component is calculated based on the results obtained after the aforementioned discrete Fourier transform. θ in - θ vn ); θ in This indicates the phase of the nth harmonic current. θ vn This represents the phase of the nth harmonic voltage.

[0054] Based on the difference in phase angles, the geometric projection method is applied (see principle). Figure 2 (Illustration), from which the resistive current component under the nth harmonic can be obtained. capacitive current component and total leakage current The relationship between them: ; ; From the above relationship, the maximum projected value of the capacitive current component under the nth harmonic can be calculated. : .

[0055] The theoretical value was then calculated using the aforementioned temperature frequency compensation model. For the orthogonal decomposition results Verification and correction: When the system is running normally and the data is highly reliable, the two values ​​should be within the allowable error range (e.g., the relative deviation between the two is 1%~5%), proving that the projection result is accurate. The orthogonal projection value can be directly used as the actual value of the capacitive current component under the current nth harmonic. If the deviation is large, it indicates that under the current temperature and harmonic environment, the pure geometric projection result may produce errors due to model mismatch. In this case, it is preferable to use the theoretical calculation value for correction, that is, to use the theoretical calculation value as the actual value of the capacitive current component under the current nth harmonic. Alternatively, a calibration alarm may be issued for recalculation.

[0056] Furthermore, based on the waveform correspondence between capacitance and voltage (the phase always leads the voltage by π / 2), the waveform function of the capacitive current component under the nth harmonic with respect to time t is obtained: ; Here, n and ω have the same meaning as before; n is the harmonic order; ω refers to the angular frequency of the fundamental wave, ω=2. πf 0, f 0 represents the fundamental frequency of the power system; θ vn Indicates the phase of the nth harmonic voltage; The total capacitive current is obtained by superimposing the capacitive current components under each harmonic: ; N The highest harmonic order for analysis is preferably 7.

[0057] Finally, subtracting the total capacitive current from the total leakage current yields the highly accurate resistive current component: .

[0058] Through the above method, the embodiments of the present invention achieve high-precision decoupling extraction of the resistive component in the total leakage current, which can be used to accurately monitor and evaluate the operating status of the surge arrester. Figure 4 The comparative effects of the methods in the embodiments of the present invention are shown, from... Figure 4 As can be seen from the decoupling effect comparison diagram, the resistive current obtained by the phasor analysis method proposed in this invention (black solid line in the third figure) closely matches the actual resistive current (gray dashed line in the third figure), accurately restoring the third harmonic characteristics. In contrast, the traditional fixed capacitance method (black dotted line in the third figure) exhibits significant deviations due to neglecting harmonics and frequency characteristics. Furthermore, the capacitive current decoupled by the phasor method of this invention (black dotted line in the second figure) also perfectly exhibits the orthogonal characteristics of a 90° phase shift, demonstrating that the method of this embodiment maintains superiority in harmonic processing accuracy and phase.

[0059] A method for assessing and providing early warning of surge arrester operating status, the method comprising: The resistive current component of the surge arrester is obtained based on the aforementioned method for decoupling leakage current in metal oxide surge arresters. Based on the discrete Fourier transform, the resistive current amplitude and resistive current phase angle corresponding to each harmonic are extracted from the decoupled resistive current components. Multiple characteristic indicators are obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic. Based on threshold comparison and / or trend analysis of the aforementioned multiple characteristic indicators, the operating status of the surge arrester is evaluated and an early warning is issued.

[0060] Furthermore, in the extracted high-precision resistive current time-domain signal Subsequently, through in-depth analysis of this signal, the operating status of the surge arrester can be effectively monitored in real time. Typically, based on the resistive current component, the aging status of the surge arrester can be monitored and evaluated in real time, thereby diagnosing and predicting potential equipment failures in advance, and enabling early warning and maintenance.

[0061] To achieve the relevant evaluation and prediction functions, we first start with high-precision resistive current time-domain signals. Multi-dimensional features characterizing the aging state of surge arresters can be extracted from this data. Also based on the discrete Fourier transform, the spectral information of the resistive current can be obtained. ; Among them, as explained in the previous text regarding DFT, I R [k] is the resistive current. i R The discrete Fourier transform result of (t) is a complex array.

[0062] i R ( m ) represents the discrete-time signal of resistive current. m Each value contains information about the harmonic amplitude (mode) and phase (argument). m Represents the time-domain index (0≤ m ≤ M 1) Corresponding time domain time point t = m / f s Or, in other words, the corresponding time-domain index can be determined from the time-domain time point t. m = t·f s , f s This refers to the sampling frequency for signal analysis in this embodiment of the invention. k Represents the frequency point index in the frequency domain (1≤ k ≤ N As mentioned above, the highest harmonic order analyzed in this embodiment of the invention is... N The preferred value is 7; M Indicates the total length of the time series considered in the analysis; j It is the imaginary unit; 2π / M This represents the radian interval corresponding to the frequency resolution.

[0063] From this, the amplitude I of each harmonic of the resistive current can be obtained. Rn and phase angle θ iRn (1≤ n ≤7). Multiple characteristic indicators are obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic.

[0064] Obtaining multiple feature metrics involves the following steps: The fundamental amplitude of the resistive current and the odd-even harmonic ratio of the resistive current are extracted based on the resistive current amplitude corresponding to each harmonic. The approximate power consumption of the surge arrester is obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic.

[0065] Based on the extracted resistive current harmonic amplitude, the following key characteristic indicators are calculated: ①Amplitude of the fundamental resistive current (I) R1 ): This value is the most direct and crucial indicator for assessing the aging condition of the MOA (Metal Oxide Aerator). An increase in this value directly indicates deterioration in the insulation performance of the valve plate and an increase in leakage current.

[0066] ② Resistive current odd-even harmonic ratio (W): During the aging process, the odd harmonics (especially the 3rd, 5th, and 7th harmonics) of the resistive current typically increase at a higher rate than the even harmonics. This ratio effectively reflects this trend and avoids random interference that a single harmonic might be affected by (Note: A minimum value σ is added to the denominator to avoid division by zero errors, while ensuring that the ratio is still meaningful when the even harmonics are close to 0).

[0067] .

[0068] in, This represents the amplitude of the second harmonic of the resistive current. This represents the amplitude of the third harmonic of the resistive current. This represents the amplitude of the fourth harmonic of the resistive current. This represents the amplitude of the fifth harmonic of the resistive current. This represents the amplitude of the 6th harmonic of the resistive current. This represents the amplitude of the 7th harmonic of the resistive current.

[0069] ③ Approximate power consumption estimation (P): The power consumption of the MOA is approximately calculated by summing the active power generated by each harmonic. Increased power consumption is an important indicator of equipment heating and aging.

[0070] ; in It is the amplitude of the nth harmonic voltage obtained earlier. It is its phase angle. It is the power factor of the nth harmonic; for the resistive component, this value should be close to 1.

[0071] Based on the obtained key feature indicators, a feature vector F=[I] containing the above three feature quantities is constructed. R1 [,W, P], thereby assessing and providing early warning of the operating status of surge arresters.

[0072] Optionally, assessment and early warning can be based on threshold comparisons, and thresholds for various characteristic indicators can be set using a dynamic adaptive approach based on a health baseline. For example, early warning thresholds and alarm thresholds can be set for each characteristic quantity or its rate of change, and when any characteristic quantity exceeds the threshold, an alarm of the corresponding level is triggered.

[0073] Alternatively, assessment and early warning can be based on trend analysis. By analyzing the time series trends of various characteristic indicators based on historical data, an early warning can be triggered when the slope suddenly increases or an accelerating upward trend appears. For example, based on historical data, the time series trends of various characteristic quantities can be analyzed, and an early warning can be triggered when the slope suddenly increases or an accelerating upward trend appears.

[0074] Alternatively, a classification model can be trained using historical normal and fault data. This model can then identify the categories corresponding to the multiple feature indicators in real time, enabling intelligent diagnosis of the surge arrester's operating status. Intelligent assessment and early warning can be achieved based on machine learning classification. For example, a classification model can be trained on the cloud or main site using historical normal and fault data, and the real-time uploaded feature vector F=[I R1 The input model is W, P], which identifies the category corresponding to the feature vector (resulting in categories such as "normal", "attention", and "abnormal"), thereby enabling intelligent diagnosis of the operating status.

[0075] In a preferred embodiment of the present invention, when evaluating and issuing warnings based on threshold comparison, the thresholds for each state characteristic can be set by a dynamic adaptive method based on a health baseline; this method can establish personalized thresholds for each surge arrester device to ensure its scientific validity and accuracy.

[0076] The specific setup logic and process are as follows: First, the system needs to continuously collect normal operation data of the surge arrester during its initial operation or when it is confirmed to be in good condition. This data should cover different seasons and typical operating conditions to form a baseline dataset that can comprehensively represent the "health status" of the equipment. Subsequently, the statistical modeling and initial threshold calculation stage begins. The system extracts feature vectors F=[I] from the health baseline data on a daily or weekly basis. R1 The system generates historical sequences based on [W, P], and performs statistical analysis on each feature sequence to calculate its mean (μ) and standard deviation (σ). Based on this, a warning threshold is set at μ+2σ, meaning that when a feature exceeds approximately 95% confidence level within its normal fluctuation range, the system will alert maintenance personnel. An alarm threshold is set at μ+3σ, corresponding to approximately 99.7% confidence level; once a feature exceeds this range, it indicates that the equipment status is highly likely abnormal and immediate intervention is required.

[0077] In another preferred embodiment of the present invention, when performing evaluation and early warning based on trend analysis, the system not only focuses on the absolute value of the feature quantity, but also monitors its changing trend simultaneously. The early warning capability of the system is further improved by introducing an adaptive correction and fusion verification mechanism.

[0078] Typically, if the fundamental amplitude of the resistive current is I R1If the monthly growth rate consistently exceeds 5%, even if its absolute value does not break through the static threshold, it will trigger an early warning, enabling the system to keenly detect the slow degradation process. Simultaneously, the system cross-validates and comprehensively adjusts the aforementioned statistically based thresholds with the technical indicators provided by equipment manufacturers and the universally applicable "attention values" in industry regulations (such as DL / T596), ensuring that the threshold settings reflect both individual characteristics and industry consensus. Furthermore, the system has dynamic update capabilities, allowing for periodic or trigger-based fine-tuning of the health baseline model and various threshold levels based on the normal aging process of the equipment and changes in the actual operating environment, thereby achieving adaptive optimization of the thresholds. The significant advantage of this method is that it completely avoids the limitations of traditional "one-size-fits-all" fixed thresholds, enabling the thresholds to be highly matched to the specific characteristics and actual operating environment of each device, thus significantly improving the accuracy, reliability, and practicality of status warnings.

[0079] In another preferred embodiment of the present invention, an intelligent diagnostic scheme based on the random forest algorithm is provided. The classification model is a random forest model, which uses historical normal and fault data to train multiple decision trees to construct a random forest; Multiple feature indicators obtained through real-time decoupling are input into the random forest, and each decision tree outputs an independent prediction result. The prediction results of all decision trees are statistically analyzed, and the category with the most votes is taken as the final diagnostic result of the operating status of the surge arrester.

[0080] This scheme uses ensemble learning to vote on the predictions from multiple decision trees, thereby obtaining stable and accurate classification results.

[0081] 1. Data preparation and feature engineering: Historical data, archived over a long period, was collected from a widely deployed monitoring system of the same type of surge arrester. A training sample set was constructed from the historical database. : ; Where H is the number of samples collected from historical data, for the i-th (1≤ i ≤ H ) samples, It is an enhanced feature vector that contains not only core features. (Fundamental amplitude of resistive current), W (Odd-even harmonic ratio), (Approximate power consumption) also incorporates ambient temperature. and voltage total harmonic distortion As an important feature for compensation under operating conditions. These are the corresponding expert-annotated status labels.

[0082] Before model training, each feature is Z-score standardized to eliminate the influence of unit weight. ; here, It is the eigenvector of the eigenvector. One portion, and These are features Mean and standard deviation on the training set.

[0083] The prepared historical dataset was randomly divided into training and test sets in a 7:3 ratio.

[0084] 2. Construction and training of the random forest model: Random forest is an ensemble learning model consisting of multiple decision trees. constitute.

[0085] Construction of a single decision tree: training each tree At that time, from the original training set A subset of training data is generated using Bootstrap sampling (sampling with replacement). When splitting at each node of the tree, instead of using all features, a subset of features is randomly selected from all features. Then, the optimal feature in that subset and its splitting threshold are chosen to maximize the reduction in Gini impurity of the split subset.

[0086] For classification problems, Gini impurity is defined as: ; in, Represents the number of categories (normal, attention, abnormal). It is a node that belongs to the category The sample proportion.

[0087] Model Training and Optimization: Z decision trees are independently trained using the methods described above, forming a random forest. Hyperparameters such as the number of decision trees Z, the maximum tree depth, and the minimum number of samples required for node splits are optimized using multi-fold cross-validation and grid search to obtain the highest macro F1 score on the test set. ; Precision measures the proportion of cases where the model predicts a positive class but the actual class is positive.

[0088] Recall measures how many of the actual positive samples are correctly predicted by the model.

[0089] 3. Online intelligent diagnosis and confidence output: When a new feature vector When inputting data into a trained random forest model for diagnostics, the workflow is as follows: ① Data preprocessing: First, utilize the data obtained during the training phase... right Standardize to obtain .

[0090] ② Collective decision-making: Each decision tree in the forest will be input as an input, and each tree will output an independent prediction. .

[0091] ③ Voting and Final Classification: Random forest uses the voting results of all trees to determine the category with the most votes as the final diagnosis. : ; in, It is an indicator function that returns 1 if the condition inside the parentheses is true, and 0 otherwise.

[0092] ④ Confidence Assessment: The model also outputs the confidence level of the classification result, i.e., the highest vote percentage: ; This confidence level intuitively reflects the degree of certainty with which the model makes this judgment.

[0093] The beneficial effect of this example is that it concretizes abstract intelligent diagnosis into a stable and interpretable process through the random forest algorithm. This method not only comprehensively utilizes multiple features to achieve accurate state classification, but its output confidence score also provides crucial quantitative evidence for operational decisions. When the diagnostic result is "attention" or "abnormal" but the confidence score is low, it can prompt manual intervention for review, thereby further improving the reliability of system decisions on the basis of automation. Through a complete and implementable machine learning application process, it demonstrates how to transform abstract algorithms into concrete industrial diagnostic tools. This method not only achieves accurate state classification, but its output confidence score also provides important quantitative references for operational decisions, thus realizing the leap from "monitoring" to "intelligent diagnosis."

[0094] Finally, see Figure 5 This invention also provides a system for monitoring the operating status of surge arresters, comprising: High-precision synchronous sampling module: This module is the system's sensing front end, responsible for high-fidelity synchronous capture of voltage and current signals. Its specific implementation includes: Voltage acquisition channel: Employing a high-impedance differential amplifier circuit and a precision resistor voltage divider network, connected in parallel across the grounding lead of the surge arrester, this channel is used to acquire the voltage drop signal across the surge arrester. This channel features high input impedance (typically >1MΩ) and a wide dynamic range to ensure that the measurement of system voltage does not affect the existing circuitry and can withstand transient overvoltages.

[0095] Current acquisition channel: Employs a high-precision, low-phase-shift Rogowski coil or miniature hollow coil sensor, which is connected in a through-hole to the grounding wire of the surge arrester to acquire the total leakage current signal. i T (t). This type of sensor has the advantages of no magnetic saturation, good linearity, and wide measurement range, and is especially suitable for scenarios containing a large number of harmonics and possible transient currents.

[0096] Synchronization mechanism: The sample-and-hold (S / H) circuits of the two channels are driven by the same clock source, ensuring that the sampling time of the voltage and current signals is strictly synchronized. This eliminates the phase error caused by the sampling time difference at the hardware level, providing a foundation for subsequent accurate phasor analysis.

[0097] Signal conditioning and ADC module: This module is responsible for converting the raw signals output by the sensor into high-quality digital signals suitable for digital processing.

[0098] Anti-aliasing filter (AAF): Employs a multi-order low-pass Butterworth or Chebyshev active filter with a cutoff frequency of... f c Strictly set according to the Nyquist theorem and the highest analytical harmonic order N (e.g., the 7th harmonic is 350Hz) (e.g.) f c = 400Hz). Its function is to filter out more than half of the high-frequency noise and interference at the sampling frequency, and to prevent spectral aliasing.

[0099] Analog-to-digital converter (ADC): Employs a high-precision Σ-Δ ADC with at least 16 bits of resolution (such as the ADS131 series). Its high resolution ensures quantization accuracy for small-amplitude leakage current signals, while the inherent oversampling characteristics of the Σ-Δ architecture result in excellent noise performance and a higher effective number of bits (ENOB). The ADC's sampling rate is precisely controlled by the core processing unit, strictly meeting synchronous sampling requirements.

[0100] Temperature sensing module: This module is used to obtain the operating temperature of the surge arrester body in real time, providing key parameters for the temperature-frequency coupling model.

[0101] Sensor selection: Use digital temperature sensors (such as DS18B20) or analog PT100 platinum resistance thermometers, which are characterized by high accuracy and good long-term stability.

[0102] Installation method: The sensor is tightly attached to the flange base of the high-voltage end of the surge arrester with thermally conductive silicone grease, or a non-contact infrared temperature sensor is aimed at the valve plate area to indirectly but effectively monitor the core temperature change.

[0103] Signal transmission: An isolation circuit is used to couple the sensor signal to the low-voltage side processing system to ensure electrical isolation and safety between the high-voltage side and the low-voltage side.

[0104] Core processing unit: This module is the computing hub of the system, responsible for executing all algorithms and logic control.

[0105] Hardware selection: FPGA (Field Programmable Gate Array): As a preferred solution, it is suitable for scenarios requiring extremely high-speed parallel processing. It can be used to implement high-speed DFT calculations (such as through multiple parallel FFT IP cores), synchronous sampling control logic, and decoupling algorithms with the highest real-time requirements, ensuring system determinism and low latency.

[0106] DSP (Digital Signal Processor): As a classic solution, its powerful multiply-accumulate operation capabilities and targeted instruction set (such as the TI C2000 series) make it very suitable for performing a large number of complex multiply-accumulate operations (DFT, filtering, etc.).

[0107] High-performance ARM Cortex-M7 / M33 MCU: As a cost-effective integrated solution, it integrates a high-speed CPU, FPU and DSP extended instruction set, which can handle complex floating-point arithmetic operations and is easy to integrate with rich communication peripherals.

[0108] Software Functions: This unit contains all the algorithm programs from S1 to S6, including synchronous sampling control, DFT / FFT operation, temperature-frequency coupling model calculation, orthogonal projection decoupling, feature extraction, and state evaluation logic.

[0109] Communication interface module: This module is responsible for data interaction with the upper-level system and remote operation and maintenance.

[0110] Wired communication: Integrated RS-485 interface (using Modbus RTU protocol), suitable for industrial fieldbus networking; integrated Ethernet interface (using TCP / IP protocol, and integrating IEC 61850 MMS or GOOSE, MQTT and other protocols), suitable for accessing substation automation systems or IoT cloud platforms.

[0111] Wireless communication: It integrates 4G Cat.1 / 4G, NB-IoT or 5G modules to directly upload data to the cloud platform through the network, which is suitable for monitoring distributed, unattended sites; it also integrates LoRa modules, which are suitable for building localized low power wide area networks (LPWAN).

[0112] Data content: The uploaded data package includes, but is not limited to: timestamp, three-phase voltage / current fundamental and harmonic phasors, decoupled resistive / capacitive current values, multi-dimensional characteristic index F, temperature data, equipment status code and warning / alarm information.

[0113] ⑥ Power Management Module: This module is the cornerstone of the system's stable operation, responsible for acquiring and managing electrical energy from harsh industrial environments.

[0114] Power supply method: CT power supply: Energy is obtained from the grounding wire of the surge arrester or the station service transformer through the current transformer (CT). It is a self-powered method, but its output power is affected by the magnitude of the primary current.

[0115] AC / DC conversion: Power is drawn from the station's AC 220V or DC 110V / 220V power supply and converted to the low-voltage DC power such as 3.3V or 5V required by the system through industrial-grade AC / DC or DC / DC power chips with a wide input range, ensuring the highest reliability.

[0116] Battery backup: Configure a rechargeable lithium battery as a backup power source to maintain system operation for a short period of time and report a power failure alarm when the main power supply fails.

[0117] Power protection: The module has built-in overvoltage, overcurrent, reverse connection and surge protection circuits (such as TVS diode) to ensure that the system can work reliably in complex electromagnetic environments such as lightning overvoltage and operational overvoltage.

[0118] On the other hand, embodiments of the present invention provide a leakage current decoupling system for metal oxide surge arresters, the system comprising: The signal acquisition module is used to synchronously acquire the voltage signal and total leakage current signal across the surge arrester. The DFT module is used to extract the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal, respectively, based on the discrete Fourier transform. The capacitive component acquisition module is used to obtain the capacitive current component corresponding to each harmonic based on the voltage amplitude, voltage phase angle, current amplitude and current phase angle corresponding to each harmonic, through orthogonal projection calculation and temperature frequency compensation model. The resistive component decoupling module is used to superimpose the capacitive current components corresponding to each harmonic to obtain the total capacitive current, and decouple the resistive current component of the surge arrester from the total leakage current signal based on the total capacitive current.

[0119] Based on further improvements to the above system, the system also includes: a temperature and frequency compensation model training module, used to establish the temperature and frequency compensation model based on the measured data of the reference surge arrester sample.

[0120] Based on further improvements to the above system, the temperature frequency compensation model training module also includes: The function setting module is used to preset the equivalent capacitance of the surge arrester. C ( n , T With harmonic order n and ambient temperature T Changing nonlinear function: ; Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β For frequency response coefficients; The sampling module is used to acquire the sinusoidal voltage signal of the reference surge arrester sample at multiple sampling temperature points, and to calculate the equivalent capacitance value of the reference surge arrester sample under each combination of temperature and voltage frequency. The parameter solving module is used to perform global curve fitting on the nonlinear function using the nonlinear least squares method based on the fundamental capacitance value at the selected reference temperature and multiple equivalent capacitance values, to obtain the optimal solution that satisfies the objective. α and β Parameter values; The model building module is used to convert the optimal... α and β The parameter values ​​are substituted into the nonlinear function to establish a stable temperature-frequency compensation model.

[0121] On the other hand, embodiments of the present invention also provide an electronic device, the electronic device including a processor and a memory; wherein, the memory stores a computer program, and when the computer program is executed by the processor, it implements the steps of the metal oxide surge arrester leakage current decoupling method as described above.

[0122] On the other hand, embodiments of the present invention also provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the metal oxide surge arrester leakage current decoupling method as described above.

[0123] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. Orthogonal Decomposition of Multiple Harmonic Phasors: This invention performs DFT analysis on synchronously acquired voltage and current signals to accurately extract the amplitude and phase information of the 1st to 7th harmonics. Subsequently, under each harmonic, utilizing the physical characteristics that resistive current and voltage are in phase and capacitive current and voltage are orthogonal, component decomposition is performed through geometric projection. This method overcomes the limitation of traditional methods that only analyze the fundamental wave, fully utilizes the wide frequency domain information of leakage current, and fundamentally solves the problem of low decoupling accuracy under harmonic voltage conditions.

[0124] 2. Dynamic Compensation of Temperature-Frequency Coupled Capacitor Parameters: This invention establishes a C(n,T) nonlinear model to describe the dynamic changes of capacitance with temperature and frequency, and uses real-time temperature data to calculate the theoretical capacitance current. This theoretical value serves as a benchmark to verify and correct the results of orthogonal projection, forming a closed-loop correction mechanism. This approach overcomes the model mismatch error caused by temperature and frequency changes in traditional fixed capacitance models, significantly improving the adaptability, accuracy, and reliability of the method under complex operating conditions.

[0125] 3. Multi-dimensional state feature extraction and intelligent early warning based on decoupling results: This invention extracts the fundamental amplitude I of the resistive current from the resistive current after high-precision decoupling. R1 Three key characteristics—even and odd harmonic ratio (K), approximate power consumption (P)—are used to construct a comprehensive feature vector, which is then integrated with threshold, trend, and machine learning algorithms for status assessment and early warning. This approach represents a leap from "monitoring" to "diagnosis." The K and P values ​​are more sensitive to early aging; combined with intelligent algorithms, a comprehensive assessment of the arrester's health status and early fault warning can be achieved, providing precise decision-making basis for condition-based maintenance.

[0126] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0127] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for decoupling leakage current in a metal oxide surge arrester, characterized in that, The method includes the following steps: Simultaneously acquire the voltage signal and total leakage current signal across the surge arrester; Based on the discrete Fourier transform, the voltage amplitude and voltage phase angle corresponding to each harmonic are extracted from the voltage signal, and the current amplitude and current phase angle corresponding to each harmonic are extracted from the total leakage current signal. Based on the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic, the capacitive current components corresponding to each harmonic are obtained through orthogonal projection calculation and temperature-frequency compensation model. The total capacitive current is obtained by superimposing the capacitive current components corresponding to each harmonic, and the resistive current component of the surge arrester is decoupled from the total leakage current signal based on the total capacitive current.

2. The method according to claim 1, characterized in that, The method further includes the following steps: The temperature and frequency compensation model is established based on the measured data of the benchmark surge arrester sample.

3. The method according to claim 2, characterized in that, The establishment of the temperature frequency compensation model also includes the following steps: Preset surge arrester equivalent capacitance C ( n , T With harmonic order n and ambient temperature T Changing nonlinear function: ； Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β For frequency response coefficients; Obtain the sinusoidal voltage signal of the reference surge arrester sample at multiple sampling temperature points, and calculate the equivalent capacitance value of the reference surge arrester sample under each temperature and voltage frequency combination; Based on the fundamental capacitance value at the selected reference temperature and multiple equivalent capacitance values, a global curve fitting of the nonlinear function is performed using the nonlinear least squares method to obtain the optimal value that satisfies the objective. α and β Parameter values; The optimal α and β The parameter values ​​are substituted into the nonlinear function to establish a stable temperature-frequency compensation model.

4. The method according to claim 3, characterized in that, The method further includes the following steps: Based on the equivalent capacitance value obtained from the established temperature-frequency compensation model and the voltage amplitude corresponding to the nth harmonic, the theoretical amplitude of the capacitive current component corresponding to the nth harmonic is calculated. : ； Where ω is the angular frequency of the fundamental wave. This represents the amplitude of the nth harmonic voltage.

5. The method according to claim 1, characterized in that, The orthogonal projection calculation also includes the following steps: Based on the current amplitude corresponding to the nth harmonic Current phase angle θ in and voltage phase angle θ vn Calculate the projected maximum value of the capacitive current component corresponding to the nth harmonic. : 。 6. The method according to claim 5, characterized in that, The method further includes the following steps: The theoretical amplitude of the capacitive current component obtained through the temperature-frequency compensation model is used to verify and correct the maximum projected value of the capacitive current component calculated by orthogonal projection.

7. The method according to claim 6, characterized in that, The inspection and correction also include the following steps: Compare the relative deviations between the theoretical amplitude and the projected maximum value; When the relative deviation is within a preset range, the maximum value of the projection is used as the actual value of the capacitive current component corresponding to the current nth harmonic; When the relative deviation exceeds the preset range, the theoretical amplitude is used as the actual value of the capacitive current component corresponding to the current nth harmonic, or a calibration alarm is issued for recalculation.

8. A method for assessing and providing early warning of the operating status of surge arresters, characterized in that, The method includes: The resistive current component of the surge arrester is obtained based on the leakage current decoupling method of the metal oxide surge arrester according to any one of claims 1-7; Based on the discrete Fourier transform, the resistive current amplitude and resistive current phase angle corresponding to each harmonic are extracted from the decoupled resistive current components. Multiple characteristic indicators are obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic. Based on threshold comparison and / or trend analysis of the aforementioned multiple characteristic indicators, the operating status of the surge arrester is evaluated and an early warning is issued.

9. The method according to claim 8, characterized in that, The process of obtaining multiple feature indicators includes the following steps: The fundamental amplitude of the resistive current and the odd-even harmonic ratio of the resistive current are extracted based on the resistive current amplitude corresponding to each harmonic. The approximate power consumption of the surge arrester is obtained based on the resistive current amplitude, resistive current phase angle, voltage amplitude, and voltage phase angle corresponding to each harmonic.

10. The method according to claim 8, characterized in that, The method further includes the following steps: When conducting assessments and early warnings based on threshold comparisons, the thresholds for each characteristic indicator are set using a dynamic adaptive approach based on the health baseline. When conducting assessments and early warnings based on trend analysis, the time series change trends of various characteristic indicators are analyzed according to historical data. When the slope suddenly increases or an accelerating upward trend appears, an early warning is triggered.

11. The method according to claim 9, characterized in that, The method further includes the following steps: A classification model is trained using historical normal and fault data. The classification model identifies the categories corresponding to the multiple feature indicators in real time, and performs intelligent diagnosis of the operating status of the surge arrester.

12. The method according to claim 11, characterized in that, The method further includes the following steps: The classification model is a random forest model, which uses historical normal and fault data to train multiple decision trees and construct a random forest; Multiple feature indicators obtained through real-time decoupling are input into the random forest, and each decision tree outputs an independent prediction result. The prediction results of all decision trees are statistically analyzed, and the category with the most votes is taken as the final diagnostic result of the operating status of the surge arrester.

13. A leakage current decoupling system for a metal oxide surge arrester, characterized in that, The system includes: The signal acquisition module is used to synchronously acquire the voltage signal and total leakage current signal across the surge arrester. The DFT module is used to extract the voltage amplitude, voltage phase angle, current amplitude, and current phase angle corresponding to each harmonic from the voltage signal and the total leakage current signal, respectively, based on the discrete Fourier transform. The capacitive component acquisition module is used to obtain the capacitive current component corresponding to each harmonic based on the voltage amplitude, voltage phase angle, current amplitude and current phase angle corresponding to each harmonic, through orthogonal projection calculation and temperature frequency compensation model. The resistive component decoupling module is used to superimpose the capacitive current components corresponding to each harmonic to obtain the total capacitive current, and decouple the resistive current component of the surge arrester from the total leakage current signal based on the total capacitive current.

14. The system according to claim 13, characterized in that, The system also includes: The temperature and frequency compensation model training module is used to establish the temperature and frequency compensation model based on the measured data of the reference surge arrester sample.

15. The system according to claim 14, characterized in that, The temperature frequency compensation model training module also includes: The function setting module is used to preset the equivalent capacitance of the surge arrester. C ( n , T With harmonic order n and ambient temperature T Changing nonlinear function: ； Among them, temperature is acquired in real time through a temperature sensor. T , C 0 is the reference temperature T The fundamental capacitance value at 0 α For temperature coefficient, β For frequency response coefficients; The sampling module is used to acquire the sinusoidal voltage signal of the reference surge arrester sample at multiple sampling temperature points, and to calculate the equivalent capacitance value of the reference surge arrester sample under each combination of temperature and voltage frequency. The parameter solving module is used to perform global curve fitting on the nonlinear function using the nonlinear least squares method based on the fundamental capacitance value at the selected reference temperature and multiple equivalent capacitance values, to obtain the optimal solution that satisfies the objective. α and β Parameter values; The model building module is used to convert the optimal... α and β The parameter values ​​are substituted into the nonlinear function to establish a stable temperature-frequency compensation model.

16. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory stores a computer program, which, when executed by the processor, implements the steps of the metal oxide surge arrester leakage current decoupling method as described in any one of claims 1-7.

17. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the metal oxide surge arrester leakage current decoupling method as described in any one of claims 1-7.

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