Ground fault identification method based on zero-sequence voltage periodic differential energy

Abstract

The present invention provides a method, apparatus, device, and storage medium for identifying ground faults based on zero-sequence voltage periodic differential energy. The method comprises: collecting and processing the zero-sequence voltage signal of a busbar in a distribution network system to obtain a zero-sequence voltage data sequence; calculating the zero-sequence voltage periodic differential energy of the busbar based on the zero-sequence voltage data sequence to obtain a zero-sequence voltage periodic differential energy sequence; determining a zero-sequence voltage periodic differential energy threshold according to preset conditions, and comparing the zero-sequence voltage periodic differential energy threshold with the zero-sequence voltage periodic differential energy sequence to obtain a comparison result; and generating a ground fault identification result for the distribution network system based on the comparison result and the zero-sequence voltage signal. The present invention detects the voltage waveform changes caused by ground faults through periodic differential energy detection of zero-sequence voltage, and sets an energy threshold to effectively filter out transient disturbance signals. The periodic differential calculation is insensitive to noise interference and data loss, ensuring the stability and reliability of detection.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a ground fault identification method based on zero-sequence voltage periodic differential energy. Background Art

[0002] In distribution networks, ground faults are a significant issue affecting system safety and reliability, especially those occurring in low-resistance grounding systems, which often manifest as abnormalities such as voltage fluctuations and harmonic distortion. Rapid identification and location of ground faults are crucial for improving the safe operation of distribution networks. However, existing ground fault identification methods mostly rely on simple threshold judgments based on current and voltage, making them incapable of handling complex high-resistance ground faults or short-duration disturbances. Summary of the Invention

[0003] The main purpose of the present invention is to solve the technical problem that existing ground fault identification methods mostly rely on simple threshold judgments of current and voltage, and are difficult to deal with complex high-resistance ground faults or short-term disturbances;

[0004] A first aspect of the present invention provides a ground fault identification method based on zero-sequence voltage periodic differential energy, the ground fault identification method based on zero-sequence voltage periodic differential energy comprising:

[0005] Collect and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence;

[0006] Calculating the zero-sequence voltage period differential energy of the bus according to the zero-sequence voltage data sequence to obtain a zero-sequence voltage period differential energy sequence;

[0007] Determining a zero-sequence voltage cycle differential energy threshold according to a preset condition, and comparing the zero-sequence voltage cycle differential energy threshold with the zero-sequence voltage cycle differential energy sequence to obtain a comparison result;

[0008] A ground fault is identified on the distribution network system according to the comparison result and the zero-sequence voltage signal to obtain a ground fault identification result.

[0009] Optionally, in a first implementation of the first aspect of the present invention, collecting and processing the zero-sequence voltage signal of the busbar in the distribution network system to obtain the zero-sequence voltage data sequence includes:

[0010] Sampling the zero-sequence voltage signal of the busbar in the distribution network system to obtain original zero-sequence voltage sampling data;

[0011] Performing DC offset removal and high-frequency interference filtering processing on the original zero-sequence voltage sampling data to obtain zero-sequence voltage data;

[0012] detecting missing sampling points in the zero-sequence voltage data, and performing zero-value filling on the missing sampling points in the zero-sequence voltage data to obtain continuous zero-sequence voltage data;

[0013] Data formatting and time stamp alignment are performed on the continuous zero-sequence voltage data to obtain a zero-sequence voltage data sequence.

[0014] Optionally, in a second implementation of the first aspect of the present invention, calculating the zero-sequence voltage periodic differential energy of the bus according to the zero-sequence voltage data sequence to obtain the zero-sequence voltage periodic differential energy sequence includes:

[0015] Performing period segmentation processing on the zero-sequence voltage data sequence to obtain a plurality of single-period zero-sequence voltage data subsequences;

[0016] Calculating voltage differences of corresponding sampling points in the single-cycle zero-sequence voltage data subsequences of adjacent cycles to obtain a zero-sequence voltage cycle difference sequence;

[0017] A square operation is performed on the zero-sequence voltage period difference sequence to obtain a zero-sequence voltage period difference energy sequence.

[0018] Optionally, in a third implementation of the first aspect of the present invention, the preset condition includes a bus zero-sequence voltage during normal operation of the distribution network system;

[0019] The step of determining a zero-sequence voltage period differential energy threshold according to a preset condition, and comparing the zero-sequence voltage period differential energy threshold with the zero-sequence voltage period differential energy sequence to obtain a comparison result includes:

[0020] Performing peak detection on the busbar zero-sequence voltage during normal operation of the distribution network system to obtain the busbar zero-sequence voltage peak value, and performing superposition influence analysis of the noise signal on the zero-sequence voltage data sequence to obtain an analysis result;

[0021] Determine a margin coefficient of a zero-sequence voltage period differential energy threshold value according to the analysis result, and calculate the zero-sequence voltage period differential energy threshold value according to the bus zero-sequence voltage peak value and the margin coefficient;

[0022] Comparing each zero-sequence voltage cycle differential energy in the zero-sequence voltage cycle differential energy sequence with the zero-sequence voltage cycle differential energy threshold to obtain an element-level comparison result sequence;

[0023] Statistical analysis is performed on the element-level comparison result sequence, and the number and duration of zero-sequence voltage period differential energy exceeding the zero-sequence voltage period differential energy threshold are calculated to obtain a comparison result.

[0024] Optionally, in a fourth implementation of the first aspect of the present invention, performing noise signal superposition influence analysis on the zero-sequence voltage data sequence to obtain an analysis result includes:

[0025] Performing a time-frequency domain joint analysis on the zero-sequence voltage data sequence to obtain a time-frequency characteristic matrix, and separating and extracting noise components in the zero-sequence voltage data sequence based on the time-frequency characteristic matrix to obtain a noise signal sequence;

[0026] Performing statistical characteristic analysis on the noise signal sequence, calculating probability distribution parameters of the noise signal sequence, and obtaining noise statistical characteristics;

[0027] The noise statistical characteristics are quantitatively evaluated according to a preset noise impact evaluation index and the amplitude information of the zero-sequence voltage data sequence to obtain a quantitative evaluation result of the noise superposition impact;

[0028] The quantitative evaluation results are normalized and compared with the preset noise impact level to obtain analysis results.

[0029] Optionally, in a fifth implementation of the first aspect of the present invention, performing ground fault identification on the distribution network system according to the comparison result and the zero-sequence voltage signal to obtain a ground fault identification result includes:

[0030] analyzing whether there is a zero-sequence voltage period differential energy exceeding a zero-sequence voltage period differential energy threshold in the zero-sequence voltage period differential energy sequence according to the comparison result;

[0031] If so, the zero-sequence voltage cycle differential energy exceeding the zero-sequence voltage cycle differential energy threshold is used as the target differential energy, and it is determined whether there is a zero-sequence current protection action state on the feeder of the distribution network system within the duration corresponding to the target differential energy;

[0032] If there is no zero-sequence current protection action state, the ground fault identification result is determined to be a low-resistance ground fault;

[0033] If there is a zero-sequence current protection action state, a statistical analysis is performed on the historical over-limit situations of the target differential energy to determine whether the over-limit situation of the target differential energy is the first over-limit situation within a preset time range;

[0034] If the target differential energy exceeds the limit for the first time within a preset time range, determining that the ground fault identification result is a high-resistance ground fault;

[0035] If the target differential energy exceeds the limit for the first time within the preset time range, the ground fault identification result is determined to be intermittent solitary light grounding.

[0036] Optionally, in a sixth implementation of the first aspect of the present invention, after taking the zero-sequence voltage period differential energy exceeding the zero-sequence voltage period differential energy threshold as the target differential energy, the method further includes:

[0037] Performing energy peak analysis on the target differential energy to obtain a differential energy peak, and identifying a start time of the differential energy peak;

[0038] The starting time of the differential energy peak is used as the fault moment of the ground fault, and the fault moment is added to the ground fault identification result.

[0039] A second aspect of the present invention provides a ground fault identification device based on zero-sequence voltage periodic differential energy, the ground fault identification device based on zero-sequence voltage periodic differential energy comprising:

[0040] The acquisition and processing module is used to acquire and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence;

[0041] A differential calculation module is used to calculate the zero-sequence voltage cycle differential energy of the bus according to the zero-sequence voltage data sequence to obtain a zero-sequence voltage cycle differential energy sequence;

[0042] a comparison module, configured to determine a zero-sequence voltage period differential energy threshold according to a preset condition, and compare the zero-sequence voltage period differential energy threshold with the zero-sequence voltage period differential energy sequence to obtain a comparison result;

[0043] An identification module is used to identify a ground fault in the distribution network system according to the comparison result and the zero-sequence voltage signal to obtain a ground fault identification result.

[0044] A third aspect of the present invention provides a ground fault identification device based on zero-sequence voltage periodic differential energy, comprising: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; the at least one processor calls the instructions in the memory to cause the ground fault identification device based on zero-sequence voltage periodic differential energy to perform the steps of the above-mentioned ground fault identification method based on zero-sequence voltage periodic differential energy.

[0045] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions which, when executed on a computer, enable the computer to execute the steps of the above-mentioned ground fault identification method based on zero-sequence voltage periodic differential energy.

[0046] The above-mentioned ground fault identification method, device, equipment and storage medium based on zero-sequence voltage periodic differential energy obtain a zero-sequence voltage data sequence by collecting and processing the zero-sequence voltage signal of the bus in the distribution network system; calculate the zero-sequence voltage periodic differential energy of the bus based on the zero-sequence voltage data sequence to obtain a zero-sequence voltage periodic differential energy sequence; determine the zero-sequence voltage periodic differential energy threshold according to preset conditions, and compare the zero-sequence voltage periodic differential energy threshold with the zero-sequence voltage periodic differential energy sequence to obtain a comparison result; generate a ground fault identification result of the distribution network system based on the comparison result and the zero-sequence voltage signal. The present invention captures the voltage waveform changes caused by the ground fault through periodic differential energy detection of the zero-sequence voltage, and sets the energy threshold to effectively filter the transient disturbance signal. The periodic differential calculation is insensitive to noise interference and data loss, ensuring the stability and reliability of the detection.

[0047] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of a first embodiment of a ground fault identification method based on zero-sequence voltage periodic differential energy according to an embodiment of the present invention;

[0050] Figure 2 Schematic diagram of an embodiment of a ground fault identification device based on zero-sequence voltage periodic differential energy according to an embodiment of the present invention;

[0051] Figure 3 Schematic diagram of an embodiment of a ground fault identification device based on zero-sequence voltage periodic differential energy in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] The terms "including," "having," and any variations thereof, as used in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or device.

[0054] To facilitate understanding of this embodiment, a ground fault identification method based on zero-sequence voltage period differential energy disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, this method includes the following steps:

[0055] 101. Collect and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence;

[0056] In one embodiment of the present invention, the collecting and processing of the zero-sequence voltage signal of the bus in the distribution network system to obtain a zero-sequence voltage data sequence includes: sampling the zero-sequence voltage signal of the bus in the distribution network system to obtain original zero-sequence voltage sampling data; performing DC offset removal and high-frequency interference filtering on the original zero-sequence voltage sampling data to obtain zero-sequence voltage data; detecting missing sampling points in the zero-sequence voltage data and filling the missing sampling points in the zero-sequence voltage data with zero values ​​to obtain continuous zero-sequence voltage data; and performing data formatting and timestamp alignment on the continuous zero-sequence voltage data to obtain a zero-sequence voltage data sequence.

[0057] Specifically, high-precision voltage transformers (PTs) are first installed on the busbars of the distribution network system. These PTs are typically connected in an open-delta or star configuration to accurately measure zero-sequence voltage. Signal acquisition devices, such as intelligent electronic devices (IEDs) or digital fault recorders (DFRs), are connected to the PTs to perform high-speed sampling of the zero-sequence voltage signal. The sampling frequency is typically set to an integer multiple of the system power frequency, such as 10 kHz (for a 50 Hz system, 200 samples per cycle). This high sampling rate captures rapidly changing transient phenomena and is crucial for accurately identifying ground faults. During the sampling process, an analog-to-digital converter (ADC) converts the analog signal into a digital signal, generating raw zero-sequence voltage sampled data. For example, in a 10 kV distribution system, an IED with a sampling frequency of 10 kHz samples the zero-sequence voltage, acquiring 10,000 samples per second. These data constitute the raw zero-sequence voltage sampled data sequence.

[0058] Specifically, after obtaining the original zero-sequence voltage sampling data, signal preprocessing is required to improve data quality. First, DC offset removal is performed. This step aims to eliminate the DC component due to imperfect measurement equipment or the system itself, ensuring that the signal fluctuates around zero. DC offset removal is usually achieved using a moving average method or a high-pass filter. Next, a digital filter is used to remove high-frequency interference. Common filtering methods include Butterworth filters, Chebyshev filters, or finite impulse response (FIR) filters. The cutoff frequency of the filter is usually set to several times the power frequency to retain useful harmonic information while removing high-frequency noise. For example, for a 50Hz system, a low-pass filter with a cutoff frequency of 1000Hz can be set.

[0059] Specifically, in actual operation, sampling points may be lost due to communication failures or temporary equipment malfunctions. To ensure data continuity and integrity, missing sampling points must be detected and filled in the zero-sequence voltage data. This detection method typically analyzes the time interval between sampling points. For example, if the time interval between two adjacent sampling points is significantly longer than the expected sampling period (e.g., greater than 1.5 times the normal sampling period), it is considered that there is a missing sampling point. For detected missing sampling points, a zero-filling method is used. This method is simple and effective. While it may introduce some error, it has little impact on short-term data loss. In certain demanding scenarios, more complex methods such as linear interpolation or spline interpolation can also be used. For example, in a system with a 10kHz sampling rate, if the time interval between two adjacent sampling points is detected to be 300μs (normally 100μs), two zeros are inserted between these two points to maintain data continuity.

[0060] Specifically, the final step is to format and timestamp-align the processed continuous zero-sequence voltage data for subsequent analysis. Data formatting involves unifying data types (such as converting all data to 32-bit floating-point numbers) and adjusting data precision. Timestamp alignment ensures that each sampling point has accurate time information, which is crucial for subsequent fault location and various time-related analyses. Timestamp alignment can use interpolation techniques, such as linear interpolation or cubic spline interpolation, to obtain more accurate time information. Through this interpolation, a more precise timestamp can be assigned to each sampling point, improving the accuracy of subsequent analysis. For example, when processing 1 second of data, if the original timestamp accuracy is only at the millisecond level, interpolation can increase the timestamp accuracy to the microsecond level, thereby assigning a unique, high-precision time identifier to each sampling point.

[0061] 102. Calculate the zero-sequence voltage period differential energy of the bus according to the zero-sequence voltage data sequence to obtain a zero-sequence voltage period differential energy sequence;

[0062] In one embodiment of the present invention, calculating the zero-sequence voltage cycle differential energy of the bus based on the zero-sequence voltage data sequence to obtain a zero-sequence voltage cycle differential energy sequence includes: performing period segmentation processing on the zero-sequence voltage data sequence to obtain multiple single-cycle zero-sequence voltage data subsequences; calculating the voltage differences of corresponding sampling points in the single-cycle zero-sequence voltage data subsequences of adjacent cycles to obtain a zero-sequence voltage cycle difference sequence; and performing a square operation on the zero-sequence voltage cycle difference sequence to obtain a zero-sequence voltage cycle differential energy sequence.

[0063] Specifically, the acquired zero-sequence voltage data sequence is first subjected to cycle segmentation. This step aims to divide the continuous zero-sequence voltage data sequence into multiple single-cycle data subsequences, paving the way for subsequent cycle-differential energy calculation. Cycle segmentation requires precise identification of the start and end points of each power frequency cycle. Given the potential frequency fluctuations in actual systems, segmentation cannot be simply performed based on a fixed number of sampling points. Instead, zero-crossing detection or Fourier transform methods can be used to accurately identify cycles. Zero-crossing detection identifies cycle boundaries by finding the moment when the signal crosses zero from positive to negative (or vice versa). Fourier transforms analyze the signal's spectrum to determine the dominant frequency component, thereby inferring the exact cycle length. In practical applications, these two methods can be combined: first using the Fourier transform method to estimate the approximate cycle length, then using the zero-crossing detection method to precisely locate the cycle boundaries, for optimal segmentation. For example, for a 50Hz system with a sampling frequency of 10kHz, each cycle theoretically contains 200 sampling points. However, in practice, some cycles may contain 199 or 201 sampling points, reflecting small fluctuations in the system frequency. By accurately segmenting the cycle, we can obtain a series of single-cycle zero-sequence voltage data subsequences, each of which accurately corresponds to a complete power frequency cycle.

[0064] Specifically, after obtaining a single-cycle zero-sequence voltage data subsequence, the next step is to calculate the voltage difference between the corresponding sampling points in adjacent cycles. This step is the core of the zero-sequence voltage cycle differential energy calculation. For each sampling point, we need to calculate the voltage difference between it and the sampling point at the corresponding moment in the next cycle. This difference calculation can effectively eliminate the periodic component under normal operating conditions and highlight the non-periodic changes caused by faults or disturbances. Under normal operating conditions, due to the periodic characteristics of the power system, Should be with is very close, so Δ It should be close to zero. However, when a ground fault or other disturbance occurs in the system, this periodicity will be broken, resulting in Δ For example, suppose that at a certain time t, = 100V, and at the corresponding moment after one cycle, due to the occurrence of a ground fault, = 150V, then Δ = 50V. This large difference indicates an abnormal change in the system state. By performing such a difference calculation on the entire data sequence, we can obtain a zero-sequence voltage cycle difference sequence. Each element in this sequence reflects the change in the system state at the corresponding moment. Finally, the zero-sequence voltage cycle difference sequence is converted into a zero-sequence voltage cycle differential energy sequence. The purpose of this step is to further amplify the signal changes caused by faults or disturbances and improve the sensitivity of detection. The calculation method is to perform a square operation on each element in the zero-sequence voltage cycle difference sequence. The square operation has two important functions: first, it eliminates the positive and negative signs of the difference, so that all changes are converted into positive values, which is convenient for subsequent threshold comparison; second, the square operation has an amplifying effect on large differences and has little effect on small differences, which helps to highlight significant system state changes. The calculation of the cycle differential energy of the above process can be expressed as:

[0065] ;

[0066] in, is the zero-sequence voltage cycle differential energy at time t, and are the zero-sequence voltage values ​​at time t and the corresponding time after one cycle. In practical applications, the calculation results can be normalized as needed to facilitate comparison between different systems. For example, assuming that at a certain time t, the calculated zero-sequence voltage cycle difference is = 50V, then the corresponding period differential energy E(t) = = 2500V. This large energy value clearly indicates a significant change in the system state. In contrast, the cycle-to-cycle differential energy under normal operating conditions is usually much smaller. For example, if = 1V (indicating a very small fluctuation), then E(t) = 1^2 = 1V. This small energy value can usually be considered as a normal system fluctuation.

[0067] 103. Determine a zero-sequence voltage cycle differential energy threshold according to a preset condition, and compare the zero-sequence voltage cycle differential energy threshold with the zero-sequence voltage cycle differential energy sequence to obtain a comparison result;

[0068] In one embodiment of the present invention, the preset condition includes the bus zero-sequence voltage during normal operation of the distribution network system; determining the zero-sequence voltage cycle differential energy threshold according to the preset condition, and comparing the zero-sequence voltage cycle differential energy threshold with the zero-sequence voltage cycle differential energy sequence to obtain a comparison result includes: performing peak detection on the bus zero-sequence voltage during normal operation of the distribution network system to obtain the bus zero-sequence voltage peak value, and performing noise signal superposition influence analysis on the zero-sequence voltage data sequence to obtain an analysis result; determining a margin coefficient of the zero-sequence voltage cycle differential energy threshold according to the analysis result, and calculating the zero-sequence voltage cycle differential energy threshold according to the bus zero-sequence voltage peak value and the margin coefficient; comparing each zero-sequence voltage cycle differential energy in the zero-sequence voltage cycle differential energy sequence with the zero-sequence voltage cycle differential energy threshold to obtain an element-level comparison result sequence; performing statistical analysis on the element-level comparison result sequence to calculate the number and duration of zero-sequence voltage cycle differential energies exceeding the zero-sequence voltage cycle differential energy threshold to obtain a comparison result.

[0069] Specifically, first, peak detection is performed on the busbar zero-sequence voltage during normal operation of the distribution network system. This step aims to obtain a baseline zero-sequence voltage value under normal system conditions, providing a basis for subsequent threshold calculation. Peak detection can employ a sliding window method, searching for the maximum value within a specific time window. Statistical analysis is then performed on the results from multiple windows to eliminate the influence of random factors. Simultaneously, the zero-sequence voltage data series is analyzed for the effects of noise signals. This analysis can employ joint time-frequency domain analysis methods, such as wavelet packet transform or Hilbert-Huang transform. These methods provide information in both the time and frequency domains, helping to isolate noise components from the signal. By analyzing wavelet packet coefficients at different scales and frequency bands, the energy distribution of the noise and its impact on the signal can be estimated. For example, suppose that in a 10kV distribution system, peak detection determines a busbar zero-sequence voltage peak value of 100V during normal operation. However, wavelet packet analysis reveals significant noise energy in high-frequency bands (e.g., above 500Hz), accounting for 5% of the total signal energy. This information is used in subsequent margin factor determination and threshold calculation.

[0070] Specifically, the margin factor is introduced to account for the system's noise level and possible normal fluctuations, avoiding false fault alarms due to minor disturbances. The margin factor can be determined using an adaptive method and dynamically adjusted based on the noise analysis results. For example, the margin factor can be calculated using a nonlinear mapping relationship between the noise energy ratio and the margin factor, such as the following formula:

[0071] ;

[0072] Where k is the margin coefficient, is the estimated noise energy, is the total signal energy, and α and β are pre-set parameters based on system characteristics. This formula reflects the relationship that higher noise levels require a larger margin factor, while also limiting the rate of increase of the margin factor through a logarithmic function. After determining the margin factor, the zero-sequence voltage cycle differential energy threshold can be calculated using the following formula:

[0073] ;

[0074] in, is the zero-sequence voltage cycle differential energy threshold, k is the margin coefficient, is the bus zero-sequence voltage peak value. This formula takes into account the maximum zero-sequence voltage variation that may occur under normal conditions and appropriately amplifies it using the margin factor. Once the zero-sequence voltage cycle-differential energy threshold is determined, the next step is to compare it with each element in the zero-sequence voltage cycle-differential energy sequence. This process generates a Boolean element-by-element comparison result sequence, where each element indicates whether the cycle-differential energy at the corresponding moment exceeds the threshold. After obtaining the element-by-element comparison result sequence, statistical analysis is performed to calculate the number of elements exceeding the threshold and the duration of their occurrence. This analysis can be performed using a sliding window method, which counts the number of "1"s and the longest consecutive occurrence within a specified time window. For example, suppose that within a window of 1000 sampling points (corresponding to 100ms), the cycle-differential energy of 50 points exceeds the threshold, with the longest consecutive occurrence lasting 20 points (2ms). These statistical results constitute the final comparison result.

[0075] Furthermore, the performing of the noise signal superposition impact analysis on the zero-sequence voltage data sequence to obtain the analysis result includes: performing a joint time-frequency domain analysis on the zero-sequence voltage data sequence to obtain a time-frequency feature matrix, and separating and extracting the noise components in the zero-sequence voltage data sequence based on the time-frequency feature matrix to obtain a noise signal sequence; performing a statistical characteristic analysis on the noise signal sequence to calculate the probability distribution parameters of the noise signal sequence to obtain noise statistical characteristics; performing a quantitative evaluation of the noise statistical characteristics based on a preset noise impact evaluation index and amplitude information of the zero-sequence voltage data sequence to obtain a quantitative evaluation result of the noise superposition impact; and normalizing the quantitative evaluation result and comparing it with a preset noise impact level to obtain an analysis result.

[0076] Specifically, the zero-sequence voltage data series is first analyzed in the time-frequency domain to obtain the complete signal characteristics in both the time and frequency dimensions. This step utilizes the Wavelet Packet Transform (WPT) method, which provides multi-resolution signal analysis and is suitable for processing non-stationary signals. WPT decomposes the signal into sub-signals of different frequency bands and time scales, forming a time-frequency feature matrix. The mathematical expression of WPT is as follows:

[0077] ;

[0078] in, are the wavelet packet coefficients, is the input zero-sequence voltage signal, is the wavelet packet basis function, denote scale, translation, and oscillation parameters, respectively. This formula describes the signal decomposition process at different time, frequency, and scales. After obtaining the time-frequency feature matrix, noise components can be identified by analyzing the energy distribution of each frequency band. Typically, noise is concentrated in the high-frequency region, while the useful signal is mainly distributed in the low-frequency region. Therefore, a frequency threshold can be set, and components above this threshold are considered noise. Through inverse transformation, the noise signal sequence can be reconstructed. For example, suppose a zero-sequence voltage signal with a sampling frequency of 10 kHz is subjected to a 6-layer wavelet packet decomposition. The analysis results show that the energy of the 5th and 6th layers (corresponding to frequencies above 250 Hz) accounts for 5% of the total energy and is evenly distributed over time. This component can be considered as noise, and the noise signal sequence is obtained through inverse transformation.

[0079] Specifically, after obtaining the noise signal sequence, the next step is to perform statistical analysis to quantify the characteristics of the noise. This step mainly involves calculating the probability distribution parameters of the noise signal. Assuming that the noise follows a generalized Gaussian distribution (GGD), its probability density function can be expressed as:

[0080] ;

[0081] Here, α is the scale parameter, β is the shape parameter, μ is the location parameter, Γ(·) is the gamma function, and x represents the amplitude of each sampling point in the noise signal sequence. This distribution function can describe probability distributions of various shapes, from Laplace distribution (β = 1) to Gaussian distribution (β = 2). These parameters can be estimated from the noise signal sequence using maximum likelihood estimation or moment estimation methods to obtain the statistical characteristics of the noise. For example, for the noise signal sequence obtained in the previous step, the fitting results may yield the following parameters: α = 0.5, β = 1.8, and μ = 0. This indicates that the noise distribution is close to Gaussian, but with slight deviations, possibly indicating the presence of some impulsive interference.

[0082] Specifically, based on the noise statistical characteristics and preset noise impact assessment indicators, combined with the amplitude information of the zero-sequence voltage data series, a quantitative assessment of the noise superposition impact is performed. The assessment indicators may include signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), etc. A comprehensive assessment indicator I is introduced here, and its calculation formula is:

[0083] ;

[0084] Among them, SNR is the signal-to-noise ratio, σs is the signal standard deviation, w1, w2, w3 are weight coefficients. This indicator takes into account the signal-to-noise ratio, noise intensity (through reflection) and the shape of the noise distribution (via For example, if the calculated SNR=20dB, =0.1, β=1.8, and weights w1=0.5, w2=0.3, and w3=0.2, respectively. The comprehensive index I = 0.520 + 0.30.1 + 0.2*(1 / 1.8) ≈ 10.11. This value reflects the comprehensive impact of noise on the signal.

[0085] Specifically, finally, the quantitative evaluation results are normalized to fall into the [0,1] interval, which is convenient for comparison with the preset noise impact level. Normalization can use the min-max method:

[0086] ;

[0087] in, is the normalized index value, and are the expected minimum and maximum indicator values, respectively. After normalization, Compare with the preset noise impact level threshold to get the final analysis result. For example, assuming the preset noise impact level is: low impact (0-0.3), medium impact (0.3-0.6), high impact (0.6-1). If =0, =20, then the normalized value corresponding to I=10.11 calculated in the previous step is ≈ 0.51. This falls into the "moderate impact" range, indicating that noise has some impact on the zero-sequence voltage signal, but has not yet reached the level of serious interference.

[0088] 104. Perform ground fault identification on the distribution network system according to the comparison result and the zero-sequence voltage signal to obtain a ground fault identification result.

[0089] In one embodiment of the present invention, the performing ground fault identification on the distribution network system based on the comparison result and the zero-sequence voltage signal to obtain the ground fault identification result includes: analyzing whether there is a zero-sequence voltage cycle differential energy exceeding a zero-sequence voltage cycle differential energy threshold in the zero-sequence voltage cycle differential energy sequence based on the comparison result; if so, using the zero-sequence voltage cycle differential energy exceeding the zero-sequence voltage cycle differential energy threshold as a target differential energy, and determining whether there is a zero-sequence current protection action state on the feeder of the distribution network system within a duration corresponding to the target differential energy; if there is no zero-sequence current protection action state, determining that the ground fault identification result is a low-resistance ground fault; if there is a zero-sequence current protection action state, performing statistical analysis on historical limit violations of the target differential energy to determine whether the target differential energy exceeds the limit for the first time within a preset time range; if the target differential energy exceeds the limit for the first time within the preset time range, determining that the ground fault identification result is a high-resistance ground fault; if the target differential energy does not exceed the limit for the first time within the preset time range, determining that the ground fault identification result is an intermittent solitary light ground fault.

[0090] Specifically, first, the zero-sequence voltage cycle differential energy sequence is analyzed based on the comparison results to identify energy values ​​exceeding a threshold. The core of this step is to traverse the entire cycle differential energy sequence, comparing each energy value with a pre-set threshold to identify the target differential energy. After identifying the target differential energy, the next step is to determine whether zero-sequence current protection has operated on the distribution network feeder during the target differential energy period. This requires access to the distribution system's protective device status information. Typically, the zero-sequence current protection operation threshold is set low to detect low-resistance ground faults. When the zero-sequence voltage cycle differential energy exceeds the threshold but the zero-sequence current protection does not operate, it is determined to be a low-resistance ground fault. This is because low-resistance ground faults typically cause large zero-sequence voltage variations. However, due to the low fault impedance, the zero-sequence current may have triggered faster protection operations (such as instantaneous tripping), resulting in the zero-sequence current protection clearing the fault before it can operate. If the zero-sequence current protection does not operate, further analysis of the target differential energy's historical violations is required. This step involves statistical analysis of the zero-sequence voltage cycle differential energy over a period of time (typically several minutes to several tens of minutes). This analysis can be implemented using a sliding time window method. When the zero-sequence voltage cycle differential energy exceeds the threshold, the zero-sequence current protection is activated, and it is the first time that the limit is exceeded, it is determined to be a high-resistance grounding fault. The characteristic of a high-resistance grounding fault is that the zero-sequence voltage changes significantly, but due to the presence of high impedance, the zero-sequence current is relatively small, which may just reach the action threshold of the zero-sequence current protection. The judgment of the first limit exceeding is to distinguish between continuous high-resistance grounding and intermittent arc grounding. When the zero-sequence voltage cycle differential energy exceeds the threshold, the zero-sequence current protection is activated, and it is not the first time that the limit is exceeded, it is determined to be intermittent arc grounding. The characteristic of intermittent arc grounding is that the fault will appear and disappear periodically, so multiple limit exceeding situations will occur within a certain time range. This type of fault is usually caused by insulation aging or contamination, and is self-extinguishing and repetitive.

[0091] Furthermore, after taking the zero-sequence voltage cycle differential energy exceeding the zero-sequence voltage cycle differential energy threshold as the target differential energy, the method further includes: performing energy peak analysis on the target differential energy to obtain a differential energy peak, and identifying the start time of the differential energy peak; taking the start time of the differential energy peak as the fault moment of the ground fault, and adding the fault moment to the ground fault identification result.

[0092] Specifically, after identifying the target differential energy, energy peak analysis is first performed. This process aims to accurately locate the critical moment of energy mutation from the target differential energy sequence, which usually corresponds to the moment of ground fault occurrence. Energy peak analysis can be achieved by calculating the first-order derivative of the target differential energy sequence. Specifically, the energy change rate can be calculated using the following formula:

[0093] ;

[0094] in, is the rate of change of energy at time t, is the zero-sequence voltage cycle differential energy at time t, is the sampling interval. This formula approximately calculates the instantaneous rate of change of the energy sequence. The value of can identify the moment when energy rises rapidly, that is, the starting point of the energy peak. For example, assuming that in a 10kV distribution system, the sampling interval Δt is 1ms, at t=100ms, the calculated Suddenly jumping from the average value of 50V^2 / ms to 500V^2 / ms, this mutation point may be the starting moment of the energy peak.

[0095] Specifically, after obtaining the energy change rate sequence, the next step is to accurately identify the start time of the differential energy peak. This can be achieved by setting a change rate threshold and finding the first moment that exceeds the threshold. The following mathematical expression can be used to describe this process:

[0096] ;

[0097] in, is the identified peak start time, k is a preset multiple, is the standard deviation of the energy change rate. This expression looks for the first point where the rate of change is significantly higher than the normal fluctuation. The introduction of the standard deviation takes into account the normal fluctuation range of the system, making the threshold setting more adaptive. For example, if the standard deviation of the energy change rate is calculated If the energy peak is 20V² / ms and k is set to 4, the rate of change threshold is 80V² / ms. When the rate of change at t=99.8ms first exceeds this threshold and reaches 85V² / ms, this moment is determined as the peak start time. Finally, after identifying the start time of the differential energy peak, this is directly used as the fault moment of the ground fault. This is based on the assumption that the onset of an energy peak typically marks a sudden change in system state. In the case of a ground fault, this sudden change is likely the moment of fault occurrence. After determining the fault moment, it needs to be added to the ground fault identification results.

[0098] In this embodiment, the zero-sequence voltage signal of the busbar in the distribution network system is collected and processed to obtain a zero-sequence voltage data sequence; based on the zero-sequence voltage data sequence, the zero-sequence voltage periodic differential energy of the busbar is calculated to obtain a zero-sequence voltage periodic differential energy sequence; the zero-sequence voltage periodic differential energy threshold is determined according to a preset condition, and the zero-sequence voltage periodic differential energy threshold is compared with the zero-sequence voltage periodic differential energy sequence to obtain a comparison result; based on the comparison result and the zero-sequence voltage signal, a ground fault identification result of the distribution network system is generated. The present invention captures the voltage waveform changes caused by the ground fault through periodic differential energy detection of the zero-sequence voltage, and sets an energy threshold to effectively filter the transient disturbance signal. The periodic differential calculation is insensitive to noise interference and data loss, ensuring the stability and reliability of the detection.

[0099] The above describes the ground fault identification method based on zero-sequence voltage cycle differential energy in the embodiment of the present invention. The following describes the ground fault identification device based on zero-sequence voltage cycle differential energy in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a ground fault identification device based on zero-sequence voltage periodic differential energy includes:

[0100] The acquisition and processing module 201 is used to acquire and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence;

[0101] A differential calculation module 202 is configured to calculate the zero-sequence voltage period differential energy of the bus according to the zero-sequence voltage data sequence to obtain a zero-sequence voltage period differential energy sequence;

[0102] The comparison module 203 is configured to determine a zero-sequence voltage cycle differential energy threshold according to a preset condition, and compare the zero-sequence voltage cycle differential energy threshold with the zero-sequence voltage cycle differential energy sequence to obtain a comparison result;

[0103] The identification module 204 is configured to identify a ground fault in the distribution network system according to the comparison result and the zero-sequence voltage signal to obtain a ground fault identification result.

[0104] In an embodiment of the present invention, the ground fault identification device based on zero-sequence voltage cycle differential energy operates the above-mentioned ground fault identification method based on zero-sequence voltage cycle differential energy. The ground fault identification device based on zero-sequence voltage cycle differential energy acquires and processes the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence; based on the zero-sequence voltage data sequence, the zero-sequence voltage cycle differential energy of the busbar is calculated to obtain a zero-sequence voltage cycle differential energy sequence; a zero-sequence voltage cycle differential energy threshold is determined according to preset conditions, and the zero-sequence voltage cycle differential energy threshold is compared with the zero-sequence voltage cycle differential energy sequence to obtain a comparison result; and a ground fault identification result for the distribution network system is generated based on the comparison result and the zero-sequence voltage signal. The present invention captures the voltage waveform changes caused by the ground fault through zero-sequence voltage cycle differential energy detection, and sets an energy threshold to effectively filter out transient disturbance signals. The cycle differential calculation is insensitive to noise interference and data loss, ensuring the stability and reliability of the detection.

[0105] above Figure 2 The ground fault identification device based on zero-sequence voltage periodic differential energy in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The ground fault identification device based on zero-sequence voltage periodic differential energy in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0106] Figure 3 This is a schematic diagram of the structure of a ground fault identification device based on zero-sequence voltage periodic differential energy, provided by an embodiment of the present invention. The ground fault identification device 300 based on zero-sequence voltage periodic differential energy may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 310 (e.g., one or more processors), a memory 320, and one or more storage media 330 (e.g., one or more mass storage devices) storing application programs 333 or data 332. The memory 320 and storage medium 330 may be either transient or persistent storage. The program stored in the storage medium 330 may include one or more modules (not shown), each of which may include a series of instructions for operating the ground fault identification device 300 based on zero-sequence voltage periodic differential energy. Furthermore, the processor 310 may be configured to communicate with the storage medium 330, allowing the ground fault identification device 300 based on zero-sequence voltage periodic differential energy to execute the series of instructions stored in the storage medium 330 to implement the steps of the above-described ground fault identification method based on zero-sequence voltage periodic differential energy.

[0107] The ground fault identification device 300 based on zero-sequence voltage periodic differential energy may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3 The illustrated structure of the ground fault identification device based on zero-sequence voltage periodic differential energy does not limit the ground fault identification device based on zero-sequence voltage periodic differential energy provided by the present invention, and may include more or fewer components than illustrated, or combine certain components, or arrange the components differently.

[0108] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the ground fault identification method based on zero-sequence voltage periodic differential energy.

[0109] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0111] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A ground fault identification method based on zero-sequence voltage periodic differential energy, characterized in that: The ground fault identification method based on zero-sequence voltage period differential energy includes: Collect and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence; Performing period segmentation processing on the zero-sequence voltage data sequence to obtain multiple single-cycle zero-sequence voltage data subsequences; calculating voltage differences between corresponding sampling points in the single-cycle zero-sequence voltage data subsequences of adjacent cycles to obtain a zero-sequence voltage period difference sequence; performing a square operation on the zero-sequence voltage period difference sequence to obtain a zero-sequence voltage period difference energy sequence; The busbar zero-sequence voltage during normal operation of the distribution network system is detected to obtain the busbar zero-sequence voltage peak value, and the zero-sequence voltage data sequence is jointly analyzed in the time-frequency domain to obtain a time-frequency feature matrix, and the noise components in the zero-sequence voltage data sequence are separated and extracted according to the time-frequency feature matrix to obtain a noise signal sequence; the noise signal sequence is statistically analyzed to calculate the probability distribution parameters of the noise signal sequence to obtain noise statistical characteristics; the noise statistical characteristics are quantitatively evaluated according to the preset noise impact evaluation index and the amplitude information of the zero-sequence voltage data sequence to obtain a quantitative evaluation result of the noise superposition impact; the quantitative evaluation index is used to determine the noise signal sequence. Normalizing the evaluation results and comparing them with the preset noise impact level to obtain an analysis result; determining a margin coefficient of the zero-sequence voltage cycle differential energy threshold according to the analysis result, and calculating the zero-sequence voltage cycle differential energy threshold according to the bus zero-sequence voltage peak value and the margin coefficient; comparing each zero-sequence voltage cycle differential energy in the zero-sequence voltage cycle differential energy sequence with the zero-sequence voltage cycle differential energy threshold to obtain an element-level comparison result sequence; performing statistical analysis on the element-level comparison result sequence, calculating the number and duration of zero-sequence voltage cycle differential energies exceeding the zero-sequence voltage cycle differential energy threshold, and obtaining a comparison result; According to the comparison result, it is analyzed whether there is a zero-sequence voltage cycle differential energy exceeding the zero-sequence voltage cycle differential energy threshold in the zero-sequence voltage cycle differential energy sequence; if so, the zero-sequence voltage cycle differential energy exceeding the zero-sequence voltage cycle differential energy threshold is used as the target differential energy, and it is determined whether there is a zero-sequence current protection action state on the feeder of the distribution network system within the duration corresponding to the target differential energy; if there is no zero-sequence current protection action state, the ground fault identification result is determined to be a low-resistance ground fault; if there is a zero-sequence current protection action state, a statistical analysis is performed on the historical limit-exceeding conditions of the target differential energy to determine whether the limit-exceeding condition of the target differential energy is the first limit-exceeding condition within a preset time range; if the limit-exceeding condition of the target differential energy is the first limit-exceeding condition within the preset time range, the ground fault identification result is determined to be a high-resistance ground fault; if the limit-exceeding condition of the target differential energy is not the first limit-exceeding condition within the preset time range, the ground fault identification result is determined to be an intermittent solitary light ground fault.

2. The ground fault identification method based on zero-sequence voltage periodic differential energy according to claim 1, characterized in that: The collecting and processing of the zero-sequence voltage signal of the busbar in the distribution network system to obtain the zero-sequence voltage data sequence includes: Sampling the zero-sequence voltage signal of the busbar in the distribution network system to obtain original zero-sequence voltage sampling data; Performing DC offset removal and high-frequency interference filtering processing on the original zero-sequence voltage sampling data to obtain zero-sequence voltage data; detecting missing sampling points in the zero-sequence voltage data, and performing zero-value filling on the missing sampling points in the zero-sequence voltage data to obtain continuous zero-sequence voltage data; Data formatting and time stamp alignment are performed on the continuous zero-sequence voltage data to obtain a zero-sequence voltage data sequence.

3. The ground fault identification method based on zero-sequence voltage periodic differential energy according to claim 1, characterized in that: After the step of taking the zero-sequence voltage period differential energy exceeding the zero-sequence voltage period differential energy threshold as the target differential energy, the method further includes: Performing energy peak analysis on the target differential energy to obtain a differential energy peak, and identifying a start time of the differential energy peak; The starting time of the differential energy peak is used as the fault moment of the ground fault, and the fault moment is added to the ground fault identification result.

4. A ground fault identification device based on zero-sequence voltage periodic differential energy, characterized in that: The ground fault identification device based on zero-sequence voltage periodic differential energy includes: The acquisition and processing module is used to acquire and process the zero-sequence voltage signal of the busbar in the distribution network system to obtain a zero-sequence voltage data sequence; a differential calculation module, configured to perform period segmentation processing on the zero-sequence voltage data sequence to obtain a plurality of single-cycle zero-sequence voltage data subsequences; calculate voltage differences between corresponding sampling points in the single-cycle zero-sequence voltage data subsequences of adjacent cycles to obtain a zero-sequence voltage period difference sequence; and perform a square operation on the zero-sequence voltage period difference sequence to obtain a zero-sequence voltage period difference energy sequence; A comparison module is configured to perform peak detection on the bus zero-sequence voltage during normal operation of the distribution network system to obtain a bus zero-sequence voltage peak value, perform a joint time-frequency domain analysis on the zero-sequence voltage data sequence to obtain a time-frequency feature matrix, and separate and extract noise components in the zero-sequence voltage data sequence based on the time-frequency feature matrix to obtain a noise signal sequence; perform statistical characteristic analysis on the noise signal sequence to calculate probability distribution parameters of the noise signal sequence to obtain noise statistical characteristics; and perform a quantitative evaluation of the noise statistical characteristics based on a preset noise impact evaluation index and amplitude information of the zero-sequence voltage data sequence to obtain a quantitative evaluation result of the noise superposition impact; The quantitative evaluation results are normalized and compared with a preset noise impact level to obtain an analysis result; a margin coefficient of a zero-sequence voltage cycle differential energy threshold is determined based on the analysis result, and the zero-sequence voltage cycle differential energy threshold is calculated based on the bus zero-sequence voltage peak value and the margin coefficient; each zero-sequence voltage cycle differential energy in the zero-sequence voltage cycle differential energy sequence is compared with the zero-sequence voltage cycle differential energy threshold to obtain an element-level comparison result sequence; a statistical analysis is performed on the element-level comparison result sequence to calculate the number and duration of zero-sequence voltage cycle differential energies exceeding the zero-sequence voltage cycle differential energy threshold to obtain a comparison result; An identification module is configured to analyze, based on the comparison result, whether there is a zero-sequence voltage cycle differential energy in the zero-sequence voltage cycle differential energy sequence that exceeds a zero-sequence voltage cycle differential energy threshold; if so, use the zero-sequence voltage cycle differential energy that exceeds the zero-sequence voltage cycle differential energy threshold as the target differential energy, and determine whether there is a zero-sequence current protection action state on the feeder of the distribution network system within the duration corresponding to the target differential energy; if there is no zero-sequence current protection action state, determine that the ground fault identification result is a low-resistance ground fault; if there is a zero-sequence current protection action state, perform statistical analysis on the historical limit-exceeding conditions of the target differential energy to determine whether the limit-exceeding condition of the target differential energy is the first time within a preset time range; if the limit-exceeding condition of the target differential energy is the first time within the preset time range, determine that the ground fault identification result is a high-resistance ground fault; if the limit-exceeding condition of the target differential energy is not the first time within the preset time range, determine that the ground fault identification result is an intermittent solitary light ground fault.

5. A ground fault identification device based on zero-sequence voltage periodic differential energy, characterized in that: The ground fault identification device based on zero-sequence voltage periodic differential energy includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory to enable the ground fault identification device based on zero-sequence voltage periodic differential energy to perform the steps of the ground fault identification method based on zero-sequence voltage periodic differential energy according to any one of claims 1 to 3.

6. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the ground fault identification method based on zero-sequence voltage periodic differential energy as claimed in any one of claims 1 to 3 are implemented.

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