Power distribution network high resistance ground fault detection method and device

Abstract

The application provides a power distribution network high-resistance grounding fault detection method and device, and the method comprises the following steps: carrying out variational mode decomposition calculation on the transient zero sequence current of the power distribution network to obtain a plurality of IMF; selecting the IMF with the largest kurtosis as the characteristic intrinsic mode function; obtaining the Teager energy operator of the characteristic intrinsic mode function, and converting the Teager energy operator into an integral histogram; based on the integral histogram, obtaining the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the deviation degree judgment coefficient, and then obtaining the comprehensive judgment coefficient; judging whether the comprehensive judgment coefficient is greater than the preset threshold value, if yes, it is determined that it is a high-resistance grounding fault; otherwise, it is determined that it is a capacitor switching or load switching. The application improves the decomposition accuracy, overcomes the mode aliasing and end effect problems of the traditional method, and can accurately identify the high-resistance grounding fault, the capacitor switching or the load switching through the comparison between the comprehensive judgment coefficient and the threshold value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network fault detection, and in particular to a power distribution network high-resistance ground fault detection method and device. BACKGROUND

[0002] With the continuous development of power distribution networks and the increasing complexity of power systems, traditional protection mechanisms face many challenges in dealing with high-resistance ground faults. High-resistance ground faults usually occur when a conductor comes into contact with the ground or other media (such as grass, sand, concrete, etc.), resulting in a significant reduction in conductor current. The current of this type of fault is often only a few hundredths to a few tenths of the normal operating current, making the traditional protection scheme commonly used in power distribution networks based on overcurrent relays, fuses, and reclosing appear inadequate in the face of high-resistance ground faults. Although the high-resistance ground fault current is relatively weak and does not immediately cause significant damage to the power distribution system, its potential dangers cannot be ignored. First, a live conductor on the ground can pose an electric shock hazard to people in contact, and in severe cases, it can endanger human life and safety. Second, due to the arc effect at the contact point, high-resistance ground faults can also cause fire hazards and other safety risks. In addition, with the expansion of the scale of power distribution networks and the increase in environmental complexity, the frequency of high-resistance ground faults in power distribution systems has been increasing year by year. Therefore, how to effectively detect and isolate high-resistance ground faults has become a problem that needs to be solved in the design of power distribution network protection systems.

[0003] In recent years, although some new protection schemes such as fault detection techniques based on signal processing methods and intelligent algorithms based on pattern recognition have been widely studied, due to the weak current waveform characteristics of high-resistance ground faults and the significant influence of fault environment and media, existing methods still face many challenges in practical applications.

[0004] The following are several existing technologies for fault detection:

[0005] 1. Finite impulse response filtering: By setting appropriate filter parameters in the time domain, the filter result can filter out interharmonics. For example, in related research, by setting reasonable parameters, the specific frequency components in the signal can be effectively removed, thereby highlighting the signal part related to fault characteristics.

[0006] 2. Mathematical morphology filtering: Use mathematical morphology to process current and estimate features to generate a decision tree model. By morphologically processing the current, key features are extracted to construct a decision tree model, enabling classification and judgment of fault types.

[0007] 3. Wavelet transform: using wavelet transform to decompose the signal into different frequency bands in time and frequency, using these frequency bands to extract high resistance grounding fault features and detect their occurrence. Multi-scale decomposition is performed on the signal, and the information of different frequency bands can reflect different degrees of fault features, which helps to more accurately locate and identify high resistance grounding faults.

[0008] However, the above prior art still has the following defects:

[0009] Feature extraction: existing methods such as fast Fourier transform, mathematical morphology, wavelet transform, and S transform mostly use fixed basis functions, resulting in low feature representation capability. These fixed basis functions cannot well adapt to the complex changes of different fault signals, and cannot accurately capture fault features. Lack of adaptability in the extraction process, prone to get no physical meaning components. For example, for some complex fault signals, traditional methods may not be able to accurately decompose and extract real features related to faults, resulting in extracted components that cannot reflect the actual physical process.

[0010] Empirical mode decomposition algorithm has adaptive characteristics, but its decomposition is prone to modal aliasing and end effect problems. Modal aliasing will interfere with different frequency modes, affecting accurate judgment of fault features; end effect will introduce errors at both ends of the signal, reducing the accuracy of feature extraction.

[0011] High resistance fault detection criterion: most existing methods are based on obtaining feature components to construct neural network, support vector machine, decision tree and other network detection methods, which have high accuracy, but require sampling library and pre-training. In actual application, this increases the complexity and cost of the system, and the detection time is long. For example, in scenarios that require fast response to faults, long training time and complex sampling library preparation may result in failure to detect faults in time, affecting the safe and stable operation of the power system.

[0012] Finally, existing methods often ignore the essential differences between high resistance grounding fault detection and capacitor switching and load switching in waveforms. High resistance fault current has intermittent reignition and extinction near the zero-crossing point, while capacitor switching and load switching are only transient disturbances, and the current shows an oscillation decay trend. This neglect of essential features makes it difficult for existing methods to distinguish between high resistance grounding fault detection and capacitor switching and load switching, and prone to misjudgment, reducing the accuracy and reliability of fault detection. SUMMARY

[0013] Therefore, the present application provides a power distribution network high resistance grounding fault detection method and device to solve at least one of the above problems.

[0014] In order to achieve the above purpose, the present application adopts the following scheme:

[0015] According to a first aspect of the present application, a flowchart of a high-resistance grounding fault detection method for a power distribution network is provided, the method comprising: performing variational mode decomposition calculation on transient zero sequence current of the power distribution network by using an improved variational mode decomposition algorithm with adaptive characteristics to obtain a plurality of intrinsic mode functions; obtaining kurtosis of each intrinsic mode function, and selecting an intrinsic mode function with maximum kurtosis as a characteristic intrinsic mode function; obtaining a Teager energy operator of the characteristic intrinsic mode function, and converting the Teager energy operator into an integral histogram; obtaining a kurtosis judgment coefficient, a Shannon entropy judgment coefficient and a skewness judgment coefficient based on the integral histogram; obtaining a comprehensive judgment coefficient according to the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the skewness judgment coefficient; and judging whether the comprehensive judgment coefficient is greater than a preset threshold value, if yes, determining that it is a high-resistance grounding fault, and if not, determining that it is a capacitor switching or load switching.

[0016] As an embodiment of the present application, in the above method, the step of performing variational mode decomposition calculation on transient zero sequence current of the power distribution network by using an improved variational mode decomposition algorithm with adaptive characteristics to obtain a plurality of intrinsic mode functions comprises: step 1, converting the modal signal into an analytic signal by Hilbert transform, and obtaining a one-sided spectrum, and then converting the spectrum of the modal signal to a baseband region by exponential mixing tuned to the respective estimated center frequency; step 2, estimating the bandwidth of the modal signal by the Gaussian smoothness of the demodulated signal; step 3, after obtaining the bandwidth of each modal signal, iteratively updating the modal signal and the center frequency by a variational optimization method to minimize the bandwidth, the variational problem in the variational optimization method is converted into an unconstrained optimization problem by introducing a quadratic penalty term and a Lagrange multiplier, and is solved by an alternating direction multiplier method; step 4, setting an iteration termination condition, if the condition is met, stopping iteration and outputting the modal component; step 5, using sparrow algorithm to find the optimal solution of the variational mode decomposition parameters by taking sample entropy as the fitness function; step 6, repeating the above steps 1 to 5 until the sample entropy is minimum, outputting the variational mode decomposition parameters at this moment, and performing variational mode decomposition on the transient zero sequence current by using the output parameters, and decomposing the transient zero sequence current into a plurality of intrinsic mode functions.

[0017] As an embodiment of the present application, in the above method, the step of obtaining kurtosis of each intrinsic mode function and selecting an intrinsic mode function with maximum kurtosis as a characteristic intrinsic mode function comprises:

[0018] The kurtosis of each intrinsic mode function is calculated by using the following formula:

[0019]

[0020] In the formula, γ, μ and σ are the kurtosis, mean value and standard deviation of the intrinsic mode function signal x(n) respectively, and E is the expected value.

[0021] The kurtosis of each intrinsic mode function is compared, and the intrinsic mode function with the largest kurtosis is selected as the characteristic intrinsic mode function of the transient zero sequence current.

[0022] As an embodiment of the present application, the Teager energy operator of the characteristic intrinsic mode function in the above method is converted into an integral histogram, which includes:

[0023] The Teager energy operator waveform of the characteristic intrinsic mode function is calculated by using the following formula:

[0024] ψ[x(n)]=x 2 (n)-x(n+1)x(n-1);

[0025] In the formula, ψ[x(n)]= is the Teager energy operator, x(n-1), x(n) and x(n+1) are three adjacent sampling points;

[0026] The Teager energy operator waveform is normalized;

[0027] The Teager energy operator waveform after normalization is divided into intervals, the area surrounded by each interval and the X-axis is calculated, and the integral histogram is drawn by taking the area value of each interval as the height of the histogram.

[0028] As an embodiment of the present application, the peak kurtosis coefficient, the Shannon entropy coefficient and the deviation coefficient are obtained based on the integral histogram in the above method, which includes:

[0029] The kurtosis of the Teager energy operator waveform integral histogram is calculated, and is denoted as the peak kurtosis coefficient k1.

[0030] The Shannon entropy value SE of each interval of the integral histogram is calculated by using the following formula: i

[0031] SE i =-P i (Z l )ln[P i (Z l )];

[0032] In the formula, P m (Z l ) represents the probability of the Teager energy operator waveform falling in the interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram.​

[0033] Summing up the Shannon entropy of each interval of the integral histogram to obtain a Shannon entropy determination coefficient k2:

[0034] k2 =∑SE i ,n = 1, 2,..., N;

[0035] In the above formula, N is the number of intervals of the integral histogram;

[0036] The deviation degree of the maximum value and the mean value of the Teager energy operator waveform integral histogram is calculated, and is denoted as a deviation degree determination coefficient k3.

[0037] As an embodiment of the present application, the comprehensive determination coefficient obtained according to the kurtosis determination coefficient, the Shannon entropy determination coefficient and the deviation degree determination coefficient in the above method includes:

[0038] According to the kurtosis determination coefficient, the Shannon entropy determination coefficient and the deviation degree determination coefficient, the comprehensive determination coefficient is obtained by using the following formula:

[0039] k = k2(1 / k1 + 1 / k3).

[0040] According to a second aspect of the present application, a high-resistance grounding fault detection device for a power distribution network is provided, which comprises: a variational mode decomposition unit configured to perform variational mode decomposition calculation on transient zero sequence current of the power distribution network by using an improved variational mode decomposition algorithm with adaptive characteristics, to obtain a plurality of intrinsic mode functions; a characteristic mode selection unit configured to obtain kurtosis of each of the intrinsic mode functions, and select an intrinsic mode function with the largest kurtosis as a characteristic intrinsic mode function; a histogram acquisition unit configured to obtain a Teager energy operator of the characteristic intrinsic mode function, and convert the Teager energy operator into an integral histogram; a coefficient acquisition unit configured to obtain a kurtosis determination coefficient, a Shannon entropy determination coefficient and a deviation degree determination coefficient based on the integral histogram; a comprehensive coefficient acquisition unit configured to obtain a comprehensive determination coefficient according to the kurtosis determination coefficient, the Shannon entropy determination coefficient and the deviation degree determination coefficient; and a fault detection unit configured to judge whether the comprehensive determination coefficient is greater than a preset threshold value, and if yes, determine that it is a high-resistance grounding fault; otherwise, determine that it is a capacitor switching or load switching.

[0041] As an embodiment of the present invention, the variational mode decomposition unit includes: a conversion module, used to convert the mode signal into an analytic signal through Hilbert transform and obtain a one-sided spectrum, and then convert the spectrum of the mode signal to the baseband region by exponential mixing with each signal tuned to its respective estimated center frequency; a bandwidth estimation module, used to estimate the bandwidth of the mode signal by over-Gaussian smoothing of the demodulated signal; and a variational optimization module, used to minimize the bandwidth by iteratively updating the mode signal and center frequency through a variational optimization method after obtaining the bandwidth of each mode signal, wherein the variational problem in the variational optimization method is solved by... The problem is transformed into an unconstrained optimization problem by introducing a quadratic penalty term and Lagrange multipliers, and solved using the alternating direction multiplier method. An iteration termination module sets the iteration termination condition; if the condition is met, iteration stops and modal components are output. A parameter optimization module uses the sparrow algorithm to find the optimal solution for variational mode decomposition parameters, using sample entropy as the fitness function. A parameter output module outputs the variational mode decomposition parameters when the sample entropy is minimized. A variational mode decomposition module uses the output parameters to perform variational mode decomposition on the transient zero-sequence current, decomposing the transient zero-sequence current into several intrinsic mode functions.

[0042] As an embodiment of the present invention, the above-mentioned feature mode selection unit is specifically used for:

[0043] The kurtosis of each of the intrinsic mode functions is calculated using the following formula;

[0044]

[0045] In the above formula: γ, μ and σ are the kurtosis, mean and standard deviation of the intrinsic mode function signal x(n), respectively, and E is the expected value.

[0046] Compare the kurtosis of each intrinsic mode function, and select the intrinsic mode function with the largest kurtosis as the characteristic intrinsic mode function of the transient zero-sequence current.

[0047] As an embodiment of the present invention, the above-mentioned histogram acquisition unit includes:

[0048] The waveform calculation module is used to calculate the Teager energy operator waveform of the characteristic eigenmode functions using the following formula:

[0049] ψ[x(n)]=x 2 (n)-x(n+1)x(n-1);

[0050] In the above formula: ψ[x(n)] = Teager energy operator, x(n-1), x(n) and x(n+1) are three adjacent sampling points;

[0051] The normalization processing module is configured to normalize the Teager energy operator waveform diagram.

[0052] The histogram drawing module is configured to divide the normalized Teager energy operator waveform diagram into intervals, calculate the area surrounded by each interval and the X axis, and draw an integral histogram by taking the area value of each interval as the height of the histogram.

[0053] As an embodiment of the present application, the coefficient obtaining unit comprises:

[0054] The kurtosis coefficient obtaining module is configured to calculate the kurtosis of the Teager energy operator waveform integral histogram, and record the kurtosis as a kurtosis determination coefficient k1.

[0055] The Shannon coefficient obtaining module is configured to calculate the Shannon entropy value SE of each interval of the integral histogram by using the following formula: i :

[0056] SE i = -P i (Z l )ln[P i (Z l )];

[0057] In the above formula, P m (Z l ) represents the probability of the Teager energy operator waveform falling in the interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram;

[0058] The sum of the Shannon entropy of each interval of the integral histogram is taken to obtain a Shannon entropy determination coefficient k2:

[0059] k2 = ∑SE i , n = 1, 2,..., N

[0060] In the above formula, N is the number of intervals of the integral histogram;

[0061] The deviation coefficient obtaining module is configured to calculate the deviation degree of the maximum value and the average value of the Teager energy operator waveform integral histogram, and record the deviation degree as a deviation degree determination coefficient k3.

[0062] As an embodiment of the present application, the comprehensive coefficient obtaining unit is specifically configured to:

[0063] According to the kurtosis determination coefficient, the Shannon entropy determination coefficient, and the deviation degree determination coefficient, a comprehensive determination coefficient is obtained by using the following formula:

[0064] k = k2(1 / k1 + 1 / k3).

[0065] According to a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0066] According to a fourth aspect of the present application, a computer readable storage medium is provided, which stores a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0067] The power distribution network high-resistance grounding fault detection method and device provided by the application can more accurately obtain the intrinsic mode function of the transient zero sequence current, and improves the credibility and representativeness of the feature by selecting the characteristic intrinsic mode function through kurtosis, overcomes the shortcomings of the existing fixed basis feature extraction method, and eliminates the mode aliasing and end effect problems of the traditional adaptive decomposition method. In addition, the detection criterion constructed based on the Teager energy operator, integral histogram and comprehensive criterion in the application has small calculation amount and does not need a complex training process, and can quickly and accurately judge the fault type. The application can still work effectively under 1dB strong noise interference, has high accuracy in judging the power distribution network high-resistance grounding fault, capacitor switching and load switching, and effectively solves the problems of long detection time and high misjudgment rate of the existing detection method. Finally, the application fully utilizes the essential difference between the intermittent reignition and extinction characteristics of the power distribution network high-resistance grounding fault and the capacitor switching and load switching, the detection criterion constructed by the application is highly targeted, and can accurately distinguish different fault types, thereby providing a more effective solution for the power distribution network fault detection. BRIEF DESCRIPTION OF DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings. In the drawings:

[0069] Figure 1 is a flowchart of a power distribution network high-resistance grounding fault detection method provided by the embodiment of the application;

[0070] Figure 2 is a flowchart of a variational mode decomposition calculation on the transient zero sequence current of the power distribution network provided by the embodiment of the application;

[0071] Figure 3 is a flowchart of converting the Teager energy operator into an integral histogram provided by the embodiment of the application;

[0072] Figure 4is a structural schematic diagram of a power distribution network high-resistance grounding fault detection device provided by an embodiment of the present application.

[0073] Figure 5 is a structural schematic diagram of a variational mode decomposition unit provided by an embodiment of the present application.

[0074] Figure 6 is a structural schematic diagram of a histogram acquisition unit provided by an embodiment of the present application.

[0075] Figure 7 is a structural schematic diagram of a coefficient acquisition unit provided by an embodiment of the present application.

[0076] Figure 8 is a structural schematic diagram of a power distribution network simulation model provided by an embodiment of the present application.

[0077] Figure 9 is a structural schematic diagram of a power distribution network high-resistance grounding fault model provided by an embodiment of the present application.

[0078] Figure 10 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0079] To make the objectives, technical solutions and advantages of embodiments of the present application clearer, further detailed descriptions will be made to the embodiments of the present application with reference to the accompanying drawings. Herein, the schematic embodiments of the present application and their descriptions are used to explain the present application, but not as a limitation to the present application.

[0080] As shown in Figure 1 is a flow schematic diagram of a power distribution network high-resistance grounding fault detection method provided by an embodiment of the present application, which comprises the following steps:

[0081] Step S101: using an improved variational mode decomposition algorithm with adaptive characteristics to perform variational mode decomposition calculation on the transient zero sequence current of the power distribution network, to obtain a plurality of intrinsic mode functions (IMF).

[0082] In this step, first, the transient zero sequence current signal of the power distribution network is collected. Then, the collected signal can be preprocessed, such as removing the direct current component, filtering, etc., to eliminate noise and interference and improve the accuracy of subsequent decomposition. The preprocessing method can be selected according to the actual situation, such as wavelet filtering, mean filtering, etc., which is not limited by the present application. The preprocessed signal is then input into the improved variational mode decomposition algorithm for decomposition.

[0083] Preferably, as shown in Figure 2 , the step S101 can further comprise the following sub-steps:

[0084] Step 1, by Hilbert transform, the modal signal uk transformed into analytic signals and the single-sided spectrum is obtained, and the spectrum of the modal signals u k is converted to the baseband region by exponential mixing tuned to the respective estimated center frequencies.

[0085] The Hilbert transform is shown in the following formula:

[0086]

[0087] In the above formula (1), δ(t) is the Dirac function; V k and ω k are the Kth IMF component and its center frequency, respectively; * is the convolution calculation symbol; j is the imaginary unit; and t is the time.

[0088] Step 2, the bandwidth of the modal signal u k is estimated by Gaussian smoothing of the demodulated signal.

[0089] Step 3, after obtaining the bandwidth of each modal signal, the modal signal u k and the center frequency ω k are iteratively updated to minimize the bandwidth by a variational optimization method, and the variational problem in the variational optimization method is converted into an unconstrained optimization problem by introducing a quadratic penalty term and a Lagrange multiplier, and is solved by an alternating direction multiplier method.

[0090] wherein the constrained variational problem is represented by the following formula (2) (L2-norm of the square of the gradient):

[0091]

[0092] In the formula, δ is the Dirac distribution; * represents convolution; {u k} represents the modal function {u1, u2,..., u k}, {ω k} represents the center frequency {ω1, ω2,..., ω k}; j is the imaginary unit; and t is the time.

[0093] And by introducing a quadratic penalty term and a Lagrange multiplier, the above constrained variational problem is converted into an unconstrained optimization problem, which can be represented by the following formula (3):

[0094]

[0095] In the above formula (3), a is a quadratic penalty factor, and λ is a Lagrange multiplier.

[0096] The solution of the above formula (3) is obtained by an alternating direction multiplier method, and the solutions of u k and ω k are represented as:

[0097]

[0098] wherein: and represent the Fourier transform of u(t), u i (t), λ(t) and , n is the iteration number.

[0099] Step 4, setting the iteration termination condition, if the condition is met, stop iteration and output the modal component.

[0100] The iteration termination condition is determined. If the following formula (6) is established, the iteration is stopped and the modal component is output; if not, it is returned to step 3 to obtain the solution of formula (3) by the alternating direction multiplier method.

[0101]

[0102] In the above formula (6), ε is the determination accuracy, represents the modal function after the n th iteration, represents the modal function after the n+1 th iteration, and K represents the number of modal functions.

[0103] Step 5, optimization is performed by using the sparrow algorithm, and the sample entropy is taken as the fitness function to find the optimal solution of the variational modal decomposition parameters [K, a].

[0104] Step 6, repeating the above steps 1 to 5 until the sample entropy is minimum, outputting the variational modal decomposition parameters [K, a] at this moment, and using the output parameters [K, a] to perform variational modal decomposition on the transient zero sequence current, and the transient zero sequence current is decomposed into several intrinsic modal functions IMF1, IMF2, …, IMF n .

[0105] Step S102: obtaining the kurtosis of each intrinsic modal function, and selecting the intrinsic modal function with the maximum kurtosis as the characteristic intrinsic modal function.

[0106] The kurtosis is a statistical quantity describing the sharpness of data distribution. The greater the kurtosis value, the sharper the data distribution, and the stronger the pulse characteristics. The transient zero sequence current signal of the high-impedance grounding fault usually has sharp pulse characteristics, so the kurtosis can be used as the basis for feature extraction. The present application selects the IMF with the maximum kurtosis as the characteristic intrinsic modal function by comparing the kurtosis values of all IMFs, that is, the IMF is considered to be the most characteristic of the high-impedance grounding fault.

[0107] Preferably, the kurtosis of each intrinsic modal function can be calculated by the following formula in this step S102;

[0108]

[0109] In the above formula, γ, μ and σ are the kurtosis, mean value and standard deviation of the intrinsic modal function signal x(n) respectively, and E is the expected value.

[0110] Then, the kurtosis of each of the intrinsic modal functions is compared, and the intrinsic modal function with the largest kurtosis is selected as a characteristic intrinsic modal function of the transient zero sequence current.

[0111] Step S103: Obtain a Teager energy operator of the characteristic intrinsic modal function, and convert the Teager energy operator into an integral histogram.

[0112] This step first calculates the Teager energy operator of the characteristic intrinsic modal function selected in step S102. The Teager energy operator is a kind of nonlinear energy operator, which can effectively extract the instantaneous energy information of the signal. The calculated Teager energy operator reflects the energy change of the characteristic IMF. Then, the Teager energy operator is converted into an integral histogram, because the integral histogram can more clearly show the distribution characteristics of the Teager energy operator.

[0113] Preferably, as shown in Figure 3 This step can include the following sub-steps:

[0114] Step S1031: Calculate the Teager energy operator waveform of the characteristic intrinsic modal function by using the following formula:

[0115] ψ[x(n)]=x 2 (n)-x(n+1)x(n-1);

[0116] In the above formula, ψ[x(n)] is the Teager energy operator, and x(n-1), x(n) and x(n+1) are three adjacent sampling points.

[0117] Step S1032: Perform normalization processing on the Teager energy operator waveform.

[0118] Step S1033: Divide the Teager energy operator waveform after the normalization processing into intervals, calculate the area surrounded by each interval and the X-axis, and draw an integral histogram by taking the area value of each interval as the height of the histogram.

[0119] Step S104: Obtain a kurtosis determination coefficient, a Shannon entropy determination coefficient and a deviation determination coefficient based on the integral histogram.

[0120] This step extracts three decision coefficients, kurtosis decision coefficient, Shannon entropy decision coefficient and deviation decision coefficient, based on the integral histogram obtained in step S103. These three coefficients reflect the characteristics of the integral histogram from different angles, for example, the kurtosis decision coefficient reflects the sharpness of the distribution, the Shannon entropy decision coefficient reflects the randomness of the distribution, and the deviation decision coefficient reflects the difference between the distribution and an ideal distribution.

[0121] Preferably, this step can specifically include:

[0122] Calculate the kurtosis of the integral histogram of the Teager energy operator waveform, denoted as kurtosis decision coefficient k1.

[0123] Calculate the Shannon entropy value SE of each interval of the integral histogram using the following formula i :

[0124] SE i =-P i (Z l )ln[P i (Z l )];

[0125] In the above formula, P m (Z l ) represents the probability of the Teager energy operator waveform falling in interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram;

[0126] Sum the Shannon entropy of each interval of the integral histogram to obtain the Shannon entropy decision coefficient k2:

[0127] k2=∑SE i ,n=1,2,...,N

[0128] In the above formula, N is the number of intervals of the integral histogram;

[0129] Calculate the deviation of the maximum value and the mean value of the integral histogram of the Teager energy operator waveform, denoted as deviation decision coefficient k3.

[0130] Through the above steps, the kurtosis decision coefficient k1, the Shannon entropy decision coefficient k2 and the deviation decision coefficient k3 can be obtained.

[0131] Step S105: Obtain a comprehensive decision coefficient according to the kurtosis decision coefficient, the Shannon entropy decision coefficient and the deviation decision coefficient.

[0132] Preferably, the comprehensive decision coefficient k can be obtained by the following formula:

[0133] k=k2(1 / k1+1 / k3).

[0134] Step S106: judging whether the comprehensive determination coefficient is greater than a preset threshold, if yes, determining as a high resistance grounding fault; otherwise, determining as a capacitor switching or load switching.

[0135] This step compares the comprehensive determination coefficient obtained in step S105 with a preset threshold. The preset threshold needs to be determined according to a large amount of experimental data to ensure a high detection accuracy and a low misjudgment rate. If the comprehensive determination coefficient is greater than the preset threshold, it is determined as a high resistance grounding fault; otherwise, it is determined as a capacitor switching or load switching.

[0136] As can be seen from the above, the power distribution network high resistance grounding fault detection method provided by the application can more accurately obtain the intrinsic mode function of the transient zero sequence current, and by selecting the characteristic intrinsic mode function through kurtosis, the credibility and representativeness of the characteristic are improved, the shortcomings of the existing fixed basis feature extraction method are overcome, and the mode aliasing and end effect problems of the traditional adaptive decomposition method are eliminated. In addition, the detection criterion constructed based on the Teager energy operator, integral histogram and comprehensive criterion in the application has a small amount of calculation and does not require a complex training process, and can quickly and accurately determine the fault type. It can still work effectively under 1dB strong noise interference, has a high accuracy in judging the power distribution network high resistance grounding fault, capacitor switching and load switching, and effectively solves the problems of long detection time and high misjudgment rate of the existing detection method. Finally, the application fully utilizes the essential difference in the intermittent reignition and extinction characteristics of the current waveform between the power distribution network high resistance grounding fault and the capacitor switching and load switching, the detection criterion constructed has strong pertinence, can accurately distinguish different fault types, and provides a more effective solution for power distribution network fault detection.

[0137] As Figure 4 shown is a structure schematic diagram of a power distribution network high resistance grounding fault detection device provided by an embodiment of the application, the device comprises a variational mode decomposition unit 410, a characteristic mode selection unit 420, a histogram acquisition unit 430, a coefficient acquisition unit 440, a comprehensive coefficient acquisition unit 450 and a fault detection unit 460, which are sequentially connected. Wherein:

[0138] The variational mode decomposition unit 410 is used for calculating the variational mode decomposition of the transient zero sequence current of the power distribution network by using the improved variational mode decomposition algorithm with adaptive characteristics, to obtain a plurality of intrinsic mode functions.

[0139] The characteristic mode selection unit 420 is used for acquiring the kurtosis of each intrinsic mode function, and selecting the intrinsic mode function with the maximum kurtosis as the characteristic intrinsic mode function.

[0140] The histogram acquisition unit 430 is configured to acquire a Teager energy operator of the characteristic eigenmode function, and convert the Teager energy operator into an integral histogram.

[0141] The coefficient acquisition unit 440 is configured to acquire a kurtosis determination coefficient, a Shannon entropy determination coefficient, and a skewness determination coefficient based on the integral histogram.

[0142] The comprehensive coefficient acquisition unit 450 is configured to obtain a comprehensive determination coefficient according to the kurtosis determination coefficient, the Shannon entropy determination coefficient, and the skewness determination coefficient.

[0143] The fault detection unit 460 is configured to determine whether the comprehensive determination coefficient is greater than a preset threshold value, and if yes, determine that it is a high-resistance grounding fault, and if not, determine that it is a capacitor switching or load switching.

[0144] Preferably, as shown in the figure, Figure 5 The variational mode decomposition unit 410 includes:

[0145] The conversion module 411 is configured to convert the modal signal into an analytic signal by Hilbert transform, obtain a one-sided spectrum, and convert the spectrum of the modal signal to a baseband region by exponential mixing tuned to respective estimated center frequencies.

[0146] The bandwidth estimation module 412 is configured to estimate the bandwidth of the modal signal by Gaussian smoothness of the demodulated signal.

[0147] The variational optimization module 413 is configured to, after obtaining the bandwidth of each modal signal, update the modal signal and the center frequency by a variational optimization method to minimize the bandwidth, a variational problem in the variational optimization method is converted into an unconstrained optimization problem by introducing a quadratic penalty term and a Lagrange multiplier, and is solved by an alternating direction multiplier method.

[0148] The iteration termination module 414 is configured to set an iteration termination condition, and stop iteration and output the modal component when the condition is met.

[0149] The parameter optimization module 415 is configured to perform optimization by using a sparrow algorithm, and take sample entropy as a fitness function to find an optimal solution of the variational mode decomposition parameter.

[0150] The parameter output module 416 is configured to output the variational mode decomposition parameter at the moment when the sample entropy is minimum.

[0151] The variational mode decomposition module 417 is configured to perform variational mode decomposition on the transient zero sequence current by using the output parameter, and decompose the transient zero sequence current into a plurality of eigenmode functions.

[0152] Preferably, the characteristic mode selection unit 420 is specifically configured to:

[0153] The kurtosis of each of the intrinsic modal functions is calculated by the following formula:

[0154]

[0155] In the above formula, γ, μ and σ are the kurtosis, mean value and standard deviation of the intrinsic modal function signal x(n) respectively, and E is the expected value.

[0156] The kurtosis of each of the intrinsic modal functions is compared, and the intrinsic modal function with the largest kurtosis is selected as the characteristic intrinsic modal function of the transient zero sequence current.

[0157] Preferably, as shown in Figure 6 the histogram obtaining unit 430 comprises:

[0158] The waveform graph calculation module 431 is configured to calculate the Teager energy operator waveform graph of the characteristic intrinsic modal function by the following formula:

[0159] ψ[x(n)] = x 2 (n)-x(n+1)x(n-1);

[0160] In the above formula, ψ[x(n)] = is the Teager energy operator, x(n-1), x(n) and x(n+1) are three adjacent sampling points.

[0161] The normalization processing module 432 is configured to perform normalization processing on the Teager energy operator waveform graph.

[0162] The histogram drawing module 433 is configured to divide the Teager energy operator waveform graph after normalization into intervals, calculate the area surrounded by each interval and the X axis, and draw an integral histogram by taking the area value of each interval as the height of the histogram.

[0163] Preferably, as shown in Figure 7 the coefficient obtaining unit 440 comprises:

[0164] The kurtosis coefficient obtaining module 441 is configured to calculate the kurtosis of the Teager energy operator waveform integral histogram, denoted as the kurtosis determination coefficient k1.

[0165] The Shannon coefficient obtaining module 442 is configured to calculate the Shannon entropy value SE i of each interval of the integral histogram by the following formula:

[0166] SE i = -P i (Z l )ln[P i (Z l )];

[0167] In the above formula, P m (Z l ) represents the probability of Teager energy operator waveform falling in the interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram;

[0168] The sum of the Shannon entropy of each interval of the integral histogram is obtained to obtain the Shannon entropy determination coefficient k2:

[0169] k2 = ∑SE i , n = 1, 2,..., N

[0170] In the above formula, N is the number of intervals of the integral histogram.

[0171] The deviation coefficient acquisition module 443 is configured to calculate the deviation degree of the maximum value and the average value of the integral histogram of the Teager energy operator waveform, and the deviation degree determination coefficient k3 is recorded.

[0172] Preferably, the comprehensive coefficient acquisition unit 450 is specifically configured to:

[0173] According to the kurtosis determination coefficient, the Shannon entropy determination coefficient and the deviation degree determination coefficient, a comprehensive determination coefficient is obtained by using the following formula:

[0174] k = k2(1 / k1 + 1 / k3).

[0175] The detailed description of each unit and module can be referred to the corresponding description in the foregoing method embodiments, which will not be described here.

[0176] As can be seen from the above, the power distribution network high resistance ground fault detection device provided by the application can more accurately obtain the intrinsic mode function of the transient zero sequence current, and the characteristic intrinsic mode function is selected through the kurtosis, which improves the credibility and representativeness of the characteristic, overcomes the shortcomings of the existing fixed basis feature extraction method, and eliminates the mode aliasing and end effect problems of the traditional adaptive decomposition method. In addition, the detection criterion constructed based on the Teager energy operator, the integral histogram and the comprehensive criterion has small calculation amount, does not need a complex training process, and can quickly and accurately judge the fault type. It can still work effectively under 1dB strong noise interference, has high accuracy in judging the power distribution network high resistance ground fault, capacitor switching and load switching, and effectively solves the problems of long detection time and high misjudgment rate of the existing detection method. Finally, the application fully utilizes the essential difference between the intermittent reignition and extinction characteristics of the power distribution network high resistance ground fault and the capacitor switching and load switching, and the constructed detection criterion has strong pertinence and can accurately distinguish different fault types, thereby providing a more effective solution for power distribution network fault detection.

[0177] To verify the effectiveness and adaptability of the above proposed power distribution network high resistance ground fault detection method and device, the embodiment is based on PSCAD / EMTDC simulation platform to build an IEEE-33 node power distribution network simulation model, as shown in the accompanying drawings. Figure 8

[0178] The power distribution network high resistance ground fault model is based on Figure 9 , which is composed of two DC sources V p , V n and corresponding diodes D P , D n to form the positive and negative half-cycle current path. Two DC sources V p , V n simulate the voltage from the arc, the value of which depends on the voltage level and asymmetric modeling of the system, and is randomly and independently changed every 0.1ms. When the instantaneous value V ph >V p , the current flows to the ground; when V ph <V n , the current flows to the ground. During V n <V ph <V p , no current flows. Changing the size of V p and V n increases the randomness of asymmetric faults and arc suppression time. In order to simulate the arc resistance that leads to asymmetric current, R P and R h take different values and are randomly and independently changed every 0.1ms. The switching capacitance is 5 / 3kVar, and the active power of the switching load is 15kW and the reactive power is 5.5kVar.

[0179] Then the flow of the above power distribution network high resistance ground fault detection method is used for fault detection, as follows:

[0180] S1, using the improved variational mode decomposition algorithm with adaptive characteristics to calculate the variational mode decomposition of the transient zero sequence current of the power distribution network, and obtaining the intrinsic mode function thereof.

[0181] S2, calculate the kurtosis of each transient zero sequence current intrinsic mode function, and select the intrinsic mode function with the maximum kurtosis value as the characteristic intrinsic mode function.

[0182] S2.1, calculate the kurtosis of the intrinsic mode function;

[0183]

[0184] In the formula: γ, μ and σ are the kurtosis, mean and standard deviation of the signal x(n), respectively.

[0185] ​S2.2, compare the kurtosis of each eigenmodal function, and select the eigenmodal function with the maximum kurtosis as the characteristic eigenmodal function of the original transient zero sequence current.

[0186] S3, calculate the Teager energy operator of the characteristic eigenmodal function, obtain the waveform diagram of the Teager energy operator of the characteristic eigenmodal function, and draw its area histogram.

[0187] S3.1, calculate the Teager energy operator waveform diagram of the characteristic eigenmodal function, Teager energy operator:

[0188] ψ[x(n)]=x 2 (n)-x(n+1)x(n-1)

[0189] In the formula: x(n-1), x(n) and x(n+1) are three adjacent sampling points.

[0190] S3.2, normalize the Teager energy operator waveform diagram.

[0191] S3.3, divide the Teager energy operator waveform diagram after normalization into intervals, calculate the area surrounded by each interval and the X axis, and take the area value of each interval as the height of the histogram to draw the integral histogram.

[0192] Take the waveform of the normalized Teager energy operator waveform diagram for 0.4s simulation time, divide it into eight intervals, and calculate the area integral of each interval as shown in Table 1:

[0193] Table 1

[0194]

[0195] Then draw the integral histogram of the Teager energy operator waveform from Table 1, as shown in Figs. 1, 2 and 3. Figure 4 、 Figure 5 and Figure 6

[0196] S4, calculate the kurtosis determination coefficient k1, the Shannon entropy determination coefficient k2 and the variance determination coefficient k3 of the Teager energy operator waveform integral histogram;

[0197] S4.1, calculate the kurtosis of the Teager energy operator waveform integral histogram, denoted as k1.

[0198] S4.2, calculate the Shannon entropy value SE i of each interval of the integral histogram.

[0199] P m (Z l ​) represents the probability of Teager energy operator waveform falling in the interval Z l , which is the ratio of the area of histogram falling in Z l to the total area of histogram.

[0200] SE i = -P i (Z l ) ln[P i (Z l )]

[0201] S4.3, summing the Shannon entropy of each interval of integral histogram to obtain k2;

[0202] k2 =∑SE i , n = 1, 2, …, N

[0203] where N is the number of intervals of integral histogram.

[0204] S4.4, calculating the deviation degree of the maximum value and the mean value of the integral histogram of Teager energy operator waveform, denoted as k3.

[0205] S5, using the kurtosis determination coefficient k1, the Shannon entropy determination coefficient k2 and the variance determination coefficient k3 to define the comprehensive determination coefficient k, and setting the determination threshold k set .

[0206] S5.1, defining the comprehensive determination coefficient as follows:

[0207] k = k2(1 / k1 + 1 / k3)

[0208] The values of the determination coefficient of the integral histogram under the high resistance grounding fault, capacitor switching and load switching of the power distribution network are shown in Table 2:

[0209] Table 2

[0210]

[0211] S5.2, setting the determination threshold k set to 0.3, identifying the high resistance grounding fault when the comprehensive determination coefficient is greater than k set , and identifying the capacitor switching or load switching when the comprehensive determination coefficient is less than k set .

[0212] From Table 2, it can be seen that the integral histogram of Teager energy operator of the transient zero sequence current characteristic eigenmode function of the high-resistance grounding fault, the capacitor switching and the load switching of the power distribution network shows obvious different characteristics in calculating the comprehensive determination coefficient, and the value of the comprehensive determination coefficient of the high-resistance grounding fault is much greater than that of the capacitor switching and the load switching, so that the determination threshold is easy to select. Therefore, it can be known that the method provided in the application can accurately determine the high-resistance grounding fault and the capacitor (or load) switching of the power distribution network.

[0213] Figure 10 is a schematic diagram of an electronic device provided by an embodiment of the application. Figure 10 The electronic device shown is a general-purpose data processing device, which includes a general-purpose computer hardware structure, and at least includes a processor 801 and a memory 802. The processor 801 and the memory 802 are connected through a bus 803. The memory 802 is suitable for storing one or more instructions or programs executable by the processor 801. The one or more instructions or programs are executed by the processor 801 to implement the steps in the high-resistance grounding fault detection method of the power distribution network.

[0214] The processor 801 described above can be an independent microprocessor, or a set of one or more microprocessors. Thus, the processor 801 performs the processing of data and the control of other devices by executing the commands stored in the memory 802, thereby implementing the method flow of the embodiment of the application as described above. The bus 803 connects the above-mentioned components together, and connects the above-mentioned components to a display controller 804 and a display device, and an input / output (I / O) device 805. The input / output (I / O) device 805 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a body sense input device, a printer and other devices known in the art. Typically, the input / output (I / O) device 805 is connected to the system through an input / output (I / O) controller 806.

[0215] The memory 802 can store software components, such as an operating system, a communication module, an interaction module and an application program. Each of the above-mentioned modules and application programs corresponds to a set of executable program instructions for completing one or more functions and the method described in the embodiment of the application.

[0216] The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the high-resistance grounding fault detection method of the power distribution network.

[0217] The power distribution network high-resistance grounding fault detection method and device can more accurately obtain the intrinsic mode function of the transient zero sequence current, and improves the credibility and representativeness of the feature by selecting the characteristic intrinsic mode function through kurtosis, overcomes the shortcomings of the existing fixed basis feature extraction method, and eliminates the mode aliasing and end effect problems of the traditional adaptive decomposition method. In addition, the detection criterion constructed based on the Teager energy operator, integral histogram and comprehensive criterion has small calculation amount, does not need a complex training process, and can quickly and accurately judge the fault type. The detection criterion can still work effectively under 1dB strong noise interference, has high accuracy in judging the power distribution network high-resistance grounding fault, capacitor switching and load switching, and effectively solves the problems of long detection time and high misjudgment rate of the existing detection method. Finally, the detection criterion constructed by fully utilizing the essential difference in intermittent reignition and extinction characteristics of the current waveform of the power distribution network high-resistance grounding fault, capacitor switching and load switching has strong pertinence, and can accurately distinguish different fault types, thereby providing a more effective solution for the power distribution network fault detection.

[0218] Preferred embodiments of the application are described herein with reference to the accompanying drawings. Numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the application. However, embodiments of the application can be practiced without the specific details (e.g., an actual working tool can include a controller to control the motor and / or a power source other than a battery) and it is understood that the scope of the application is not limited to the

[0219] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, embodiments of the application can be embodied in a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, embodiments of the application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code thereon for use by or in connection with an instruction execution system.

[0220] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flow Figure 1 one or more flows and / or blocks Figure 1apparatuses that carry out functions specified in one or more blocks or multiple blocks.

[0221] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 the functions specified in one or more blocks.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 the functions specified in one or more blocks.

[0223] The above specific embodiments are described to further explain the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

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2. The method of claim 1, wherein the step of detecting a high impedance ground fault in the electrical distribution network further comprises the step of: The method comprises: ​ The method comprises: The method comprises: The method comprises: The method comprises: The method comprises: The method comprises:

3. The method of claim 1, wherein the step of detecting the high impedance ground fault in the electrical distribution network further comprises: determining a fault type of the high impedance ground fault in the electrical distribution network. The method comprises: The method comprises: The method comprises: The method comprises:

4. 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5. The method of claim 1, wherein, The obtaining of the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the deviation judgment coefficient based on the integral histogram comprises: The kurtosis of the Teager energy operator waveform integral histogram is calculated, and is denoted as a kurtosis judgment coefficient k1; The Shannon entropy value SE of each interval of the integral histogram is calculated using the following formula i : SE i = -P i (Z l ) ln[P i (Z l )]; In the above formula, P m (Z l ) represents the probability that the Teager energy operator waveform falls in the interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram. The Shannon entropy of each interval of the integral histogram is summed to obtain a Shannon entropy judgment coefficient k2: k2 = ∑SE i n = 1, 2,..., N In the above formula, N is the number of intervals of the integral histogram; The deviation of the maximum value and the average value of the Teager energy operator waveform integral histogram is calculated, and is denoted as a deviation judgment coefficient k3.

6. The method of claim 5, wherein, The obtaining of the comprehensive judgment coefficient according to the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the deviation judgment coefficient comprises: The comprehensive judgment coefficient is obtained according to the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the deviation judgment coefficient by using the following formula: The device comprises:

7. A device for detecting high impedance ground faults in a power distribution network, characterized by, A variational mode decomposition unit is configured to calculate the variational mode decomposition of the transient zero sequence current of the power distribution network by using an improved variational mode decomposition algorithm with adaptive characteristics, to obtain a plurality of intrinsic mode functions; A characteristic mode selection unit is configured to obtain the kurtosis of each intrinsic mode function, and select the intrinsic mode function with the maximum kurtosis as a characteristic intrinsic mode function; A histogram acquisition unit is configured to acquire a Teager energy operator of the characteristic intrinsic mode function, and convert the Teager energy operator into an integral histogram; A coefficient acquisition unit is configured to obtain a kurtosis judgment coefficient, a Shannon entropy judgment coefficient and a deviation judgment coefficient based on the integral histogram; A comprehensive coefficient acquisition unit is configured to obtain a comprehensive judgment coefficient according to the kurtosis judgment coefficient, the Shannon entropy judgment coefficient and the deviation judgment coefficient; A fault detection unit is configured to judge whether the comprehensive judgment coefficient is greater than a preset threshold value, and if yes, determine that it is a high-resistance grounding fault, and if not, determine that it is a capacitor switching or load switching. The variational mode decomposition unit comprises:

8. The power distribution network high resistance ground fault detection device of claim 7, wherein, A conversion module is configured to convert the modal signal into an analytic signal by Hilbert transform, obtain a single-sided spectrum, and convert the spectrum of the modal signal to a baseband region by mixing with an index tuned to the respective estimated center frequency; A bandwidth estimation module is configured to estimate the bandwidth of the modal signal by the Gaussian smoothness of the demodulated signal; A variational optimization module is configured to, after obtaining the bandwidth of each modal signal, update the modal signal and the center frequency by a variational optimization method to minimize the bandwidth, the variational problem in the variational optimization method is converted into an unconstrained optimization problem by introducing a quadratic penalty term and a Lagrange multiplier, and is solved by an alternating direction multiplier method; An iteration termination module is configured to set an iteration termination condition, and stop iteration and output the modal component when the condition is met; A parameter optimization module is configured to perform optimization by using a sparrow algorithm, and find the optimal solution of the variational mode decomposition parameter by taking the sample entropy as a fitness function. ​ a parameter output module, configured to output the variational modal decomposition parameter at the moment when the sample entropy is minimum; a variational modal decomposition module, configured to perform variational modal decomposition on the transient zero sequence current by using the output parameter, and decompose the transient zero sequence current into a plurality of intrinsic modal functions.

9. The power distribution network high resistance ground fault detection apparatus of claim 7, wherein, The characteristic modal selection unit is specifically configured to: calculate the kurtosis of each intrinsic modal function by using the following formula: In the formula, γ, μ and σ are the kurtosis, mean value and standard deviation of the intrinsic modal function signal x(n), respectively, and E is the expected value. compare the kurtosis of each intrinsic modal function, and select the intrinsic modal function with the maximum kurtosis as the characteristic intrinsic modal function of the transient zero sequence current.

10. The power distribution network high resistance ground fault detection apparatus of claim 7, wherein, The histogram acquisition unit includes: a waveform graph calculation module, configured to calculate the Teager energy operator waveform graph of the characteristic intrinsic modal function by using the following formula: ψ [x(n)] = x 2 (n) - x(n+1) x(n-1); In the formula, ψ[x(n)]= is the Teager energy operator, x(n-1), x(n) and x(n+1) are three adjacent sampling points; a normalization processing module, configured to perform normalization processing on the Teager energy operator waveform graph; a histogram drawing module, configured to divide the Teager energy operator waveform graph after normalization processing into intervals, calculate the area surrounded by each interval and the X-axis, and draw an integral histogram by taking the area value of each interval as the height of the histogram.

11. The power distribution network high resistance ground fault detection apparatus of claim 7, wherein, The coefficient acquisition unit includes: a kurtosis coefficient acquisition module, configured to calculate the kurtosis of the Teager energy operator waveform integral histogram, denoted as the kurtosis determination coefficient k1; The Shannon coefficient obtaining module is configured to calculate the Shannon entropy SE of each interval of the integral histogram by using the following formula i : SE i = -P i (Z l ) ln[P i (Z l )]; In the above formula, P m (Z l ) represents the probability that the Teager energy operator waveform falls in the interval Z l , which is the ratio of the area of the histogram falling in Z l to the total area of the histogram. sum the Shannon entropy of each interval of the integral histogram to obtain the Shannon entropy determination coefficient k2: k2 = ∑SE i n = 1, 2,..., N In the formula, N is the number of intervals of the integral histogram; a deviation coefficient acquisition module, configured to calculate the deviation degree of the maximum value and the mean value of the Teager energy operator waveform integral histogram, denoted as the deviation degree determination coefficient k3.

12. The power distribution network high resistance ground fault detection apparatus of claim 11, wherein, The comprehensive coefficient acquisition unit is specifically configured to: obtain the comprehensive determination coefficient k by using the following formula according to the kurtosis determination coefficient, the Shannon entropy determination coefficient and the deviation degree determination coefficient: k=k2(1 / k1+1 / k3).

13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the method of any one of claims 1 to 6.

14. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method of any one of claims 1 to 6.

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