A method and system for judging a fault type of a power distribution network

Abstract

The application provides a power distribution network fault type judgment method and system, comprising: obtaining a fault voltage signal, a standard comprehensive feature matrix and a standard voltage signal characteristic value; based on the fault voltage signal and the standard voltage signal characteristic value, using Hilbert transform, discrete Fourier transform and Hilbert-Huang transform to generate a comprehensive feature matrix corresponding to the fault voltage signal; comparing the comprehensive feature matrix with the standard comprehensive feature matrix to obtain a power grid fault type, the application uses three algorithms of Hilbert transform, discrete Fourier transform and Hilbert-Huang transform to determine the fault type, improves the accuracy of short-circuit fault discrimination, especially improves the discrimination accuracy of long-distance power transmission faults, nonlinear load cut-in interference, noise interference faults and high-impedance grounding faults which are difficult to identify in the power distribution network, thereby ensuring the safety and reliability of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of fault identification technology for power distribution network relay protection, and specifically relates to a method and system for judging the type of power distribution network fault. Background Technology

[0002] Statistics show that most power outages in daily life are caused by short-circuit faults in the distribution network. Considering the significant characteristic of distribution networks extending deep into urban areas and directly connecting to low-voltage users, to shorten power outage waiting times for users and prevent equipment damage or even more serious accidents such as electric shock caused by continuous short-circuit currents, accurate fault type identification is needed for rapid fault disconnection. Currently, for mainstream radial distribution network protection, protection criteria are mainly constructed based on information such as the sequence and phase components of bus voltage and line current. However, frequent power fluctuations of low-voltage users, variable fault conditions, and the inherent asymmetry of the system pose significant challenges to the research and identification of distribution network protection characteristic quantities.

[0003] Currently, research on the intrinsic characteristics of distribution network fault signals containing fuzzy and random interference has sparked heated discussions. Relatively mature power grid fault identification algorithms mainly fall into two categories: digital signal microcomputer protection algorithms and supervised learning algorithms. While supervised learning algorithms, represented by artificial neural networks, fuzzy inference systems, and support vector machines, possess strong capabilities for capturing complex nonlinearities, they are difficult to promote and apply across different systems due to practical issues such as the selection of hyperparameters in the model structure and the quality of training data. The stability of these algorithms is also subject to considerable debate. In practice, well-applied single digital signal microcomputer protection algorithms, such as Fourier transform, Hilbert transform, Hilbert-Huang transform, and wavelet transform, extract the inherent characteristics of stable signals for fault identification, highlighting their advantages of fast and stable fault identification. However, single digital signal microcomputer protection algorithms have limitations in the accuracy and sensitivity of fault judgment, especially when the interference source signal is complex. For example, the voltage RMS detection method based on Hilbert transform has good noise resistance and stability, but when the system... When subjected to nonlinear load switching interference, fault type misidentification can occur, and the detection of high-impedance grounding faults is insensitive and difficult to identify. Harmonic information detection methods based on Discrete Fourier Transform perform well in identifying nonlinear load switching interference and short-circuit faults, but cannot effectively identify single-phase grounding short-circuit faults and two-phase-to-phase short-circuit faults. The abrupt change information detection method based on Hilbert-Huang Transform, known for its transient signal detection performance, can effectively detect high-impedance grounding faults, but its reliability in fault type identification is low under nonlinear load switching interference and noise interference. Therefore, improving the accuracy and sensitivity of fault type identification in distribution networks under various complex conditions is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, the present invention provides a method for determining the type of fault in a distribution network, comprising:

[0005] Obtain the fault voltage signal, standard integrated feature matrix, and standard voltage signal eigenvalues;

[0006] Based on the fault voltage signal and the characteristic values ​​of the standard voltage signal, a comprehensive feature matrix corresponding to the fault voltage signal is generated using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform.

[0007] The integrated feature matrix is ​​compared with the standard integrated feature matrix to obtain the power grid fault type;

[0008] There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault.

[0009] Preferably, the characteristic values ​​of the standard voltage signal include: the root mean square value of each phase voltage waveform and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform.

[0010] Preferably, based on the fault voltage signal and the characteristic values ​​of the standard voltage signal, a comprehensive feature matrix corresponding to the fault voltage signal is generated using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform, including:

[0011] Based on the root mean square (RMS) values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal, a sequence of eigenvalues ​​of the RMS value of the fault voltage signal is generated using Hilbert transform.

[0012] Based on the fault voltage signal, a sequence of harmonic characteristic values ​​of the fault voltage signal is generated using discrete Fourier transform;

[0013] Based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal, the Hilbert-Huang transform is used to generate the abrupt change characteristic value sequence of the fault voltage signal;

[0014] Based on the eigenvalue sequence, harmonic eigenvalue sequence, and abrupt change eigenvalue sequence of the root mean square value of the fault voltage signal, a comprehensive feature matrix corresponding to the fault voltage signal is generated.

[0015] Preferably, based on the root mean square (RMS) values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal, a sequence of eigenvalues ​​of the RMS value of the fault voltage signal is generated using Hilbert transform, including:

[0016] Based on the fault voltage signal, the envelope value of the fault voltage signal is calculated using Hilbert transform;

[0017] Based on the envelope value of the fault voltage signal, calculate the root mean square value of the voltage waveform of each phase of the fault voltage signal;

[0018] Based on the root mean square (RMS) values ​​of each phase voltage waveform of the fault voltage signal and the RMS values ​​of each phase voltage waveform of the standard voltage signal, a sequence of characteristic values ​​of the RMS values ​​of the fault voltage signal is generated.

[0019] Preferably, the eigenvalue sequence of the root mean square value of the fault voltage signal is generated by the following formula:

[0020]

[0021] In the formula, a V,j NOM represents the root mean square value of the j-th fault voltage signal waveform. V,jdenoted as the root mean square value of the j-th standard voltage signal waveform, 1 indicates that it is located in the m1-th row of the comprehensive feature matrix, j represents the j-th element of the feature value sequence of the root mean square value of the fault voltage signal, and the value range of j is 3, corresponding to the voltage signals of the three phases respectively.

[0022] Preferably, based on the fault voltage signal, a harmonic characteristic value sequence of the fault voltage signal is generated using discrete Fourier transform, including:

[0023] The discrete Fourier transform is used to calculate the maximum amplitude of the fundamental wave, the maximum amplitude of the odd harmonics, and the maximum amplitude of the even harmonics of each phase voltage waveform of the fault voltage signal.

[0024] Based on the fundamental maximum amplitude, odd harmonic maximum amplitude, and even harmonic maximum amplitude of each phase voltage waveform of the fault voltage signal, a harmonic characteristic value sequence of the fault voltage signal is generated.

[0025] Preferably, the harmonic characteristic value sequence of the fault voltage signal is generated according to the following formula:

[0026]

[0027] In the formula, P O / H,j P represents the maximum amplitude of the even harmonic of the j-th fault voltage signal. E / H,j denoted as the maximum amplitude of the odd harmonic of the j-th fault voltage signal, a is the threshold value, which is one-twentieth of the fundamental amplitude of the j-th fault voltage signal, 2 indicates that it is located in the m2-th row of the comprehensive feature matrix, j indicates the j-th element of the harmonic feature value sequence of the fault voltage signal, and the value range of j is 3, which corresponds to the voltage signals of the three phases respectively.

[0028] Preferably, based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal, a sequence of abrupt change characteristic values ​​of the fault voltage signal is generated using Hilbert-Huang transform, including:

[0029] Within one cycle of the voltage waveform of each phase of the fault voltage signal, the instantaneous amplitude summation of the IMF1 component of each phase voltage waveform of the fault voltage signal is generated by Hilbert-Huang transform;

[0030] Based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the fault voltage signal and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal, a sequence of abrupt change characteristic values ​​of the fault voltage signal is generated.

[0031] Preferably, the sequence of abrupt change characteristic values ​​of the fault voltage signal is generated according to the following formula:

[0032]

[0033] In the formula, S V,j S represents the sum of the instantaneous amplitudes of the IMF1 component of the j-th fault voltage waveform. NOM,j 3 represents the sum of instantaneous amplitudes of the IMF1 component of the j-th standard voltage waveform, 3 indicates that it is located in the m3-th row of the comprehensive feature matrix, j represents the j-th element of the abrupt change feature value sequence of the fault voltage signal, and the value range of j is 3, which corresponds to the voltage signals of the three phases respectively.

[0034] Preferably, the comprehensive feature matrix is ​​compared with the standard comprehensive feature matrix to obtain the power grid fault type, including:

[0035] Determine whether there exists a matrix in the standard comprehensive feature matrix that is consistent with the comprehensive feature matrix of the fault voltage signal:

[0036] If a consistent matrix exists, then the fault type of the power grid is the fault type corresponding to the consistent standard comprehensive feature matrix;

[0037] If a consistent matrix does not exist, the power grid is fault-free.

[0038] Based on the same concept, the present invention also provides a system for determining the type of fault in a distribution network, including:

[0039] The data acquisition module is used to acquire fault voltage signals, standard comprehensive feature matrices, and standard voltage signal feature values;

[0040] The comprehensive feature matrix generation module is used to generate a comprehensive feature matrix corresponding to the fault voltage signal based on the fault voltage signal and the feature values ​​of the standard voltage signal, using Hilbert transform, discrete Fourier transform and Hilbert-Huang transform;

[0041] The fault determination module is used to compare the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type;

[0042] There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault.

[0043] Preferably, the characteristic values ​​of the standard voltage signal include: the root mean square value of each phase voltage waveform and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform.

[0044] Preferably, the comprehensive feature matrix generation module includes:

[0045] The root mean square value feature value sequence generation module is used to generate the feature value sequence of the root mean square value of the fault voltage signal by using Hilbert transform based on the root mean square values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal.

[0046] The harmonic characteristic value sequence generation module is used to generate the harmonic characteristic value sequence of the fault voltage signal based on the fault voltage signal using discrete Fourier transform.

[0047] The mutation feature value sequence generation module is used to generate the mutation feature value sequence of the fault voltage signal by using the Hilbert-Huang transform based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal.

[0048] The matrix synthesis module is used to generate a comprehensive feature matrix corresponding to the fault voltage signal based on the eigenvalue sequence, harmonic eigenvalue sequence, and abrupt change eigenvalue sequence of the root mean square value of the fault voltage signal.

[0049] Preferably, the eigenvalue sequence generation module for the root mean square value includes:

[0050] The Hilbert transform module is used to calculate the envelope value of the fault voltage signal based on the fault voltage signal using the Hilbert transform.

[0051] The root mean square (RMS) value calculation module is used to calculate the root mean square (RMS) value of each phase voltage waveform of the fault voltage signal based on the envelope value of the fault voltage signal.

[0052] The first synthesis module is used to generate a sequence of feature values ​​of the root mean square value of the fault voltage signal based on the root mean square value of each phase voltage waveform of the fault voltage signal and the root mean square value of each phase voltage waveform of the standard voltage signal.

[0053] Preferably, the harmonic eigenvalue sequence generation module includes:

[0054] The Discrete Fourier Transform module is used to calculate the maximum amplitude of the fundamental wave, the maximum amplitude of the odd harmonics, and the maximum amplitude of the even harmonics of each phase voltage waveform of the fault voltage signal using the Discrete Fourier Transform.

[0055] The second synthesis module is used to generate a harmonic characteristic value sequence of the fault voltage signal based on the fundamental maximum amplitude, odd harmonic maximum amplitude, and even harmonic maximum amplitude of each phase voltage waveform of the fault voltage signal.

[0056] Preferably, the mutation feature value sequence generation module includes:

[0057] The Hilbert-Huang transform module is used to generate the instantaneous amplitude summation of the IMF1 components of the phase voltage waveform of the fault voltage signal within one cycle of each phase voltage waveform using the Hilbert-Huang transform.

[0058] The third synthesis module is used to generate a sequence of abrupt change characteristic values ​​of the fault voltage signal based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the fault voltage signal and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal.

[0059] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0060] This invention provides a method and system for determining the type of fault in a power distribution network, comprising: acquiring a fault voltage signal, a standard comprehensive feature matrix, and feature values ​​of the standard voltage signal; generating a comprehensive feature matrix corresponding to the fault voltage signal using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform based on the fault voltage signal and the feature values ​​of the standard voltage signal; comparing the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type; wherein there are multiple standard comprehensive feature matrices, each corresponding to a power grid fault type. This invention employs three digital microcomputer protection algorithms—Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform—to determine the fault type, eliminating the limitations of a single digital protection algorithm and achieving accurate identification of common single-phase short-circuit-to-ground faults, two-phase-to-phase short-circuit faults, two-phase short-circuit-to-ground faults, and three-phase short-circuit-to-ground faults in power distribution networks. It particularly improves the accuracy of identifying long-distance transmission faults, nonlinear load intrusion interference, noise interference faults, and high-impedance grounding faults that are difficult to identify in power distribution networks, thereby ensuring the safety and reliability of the power distribution network. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of a method for determining the type of fault in a power distribution network provided by the present invention;

[0062] Figure 2 This is a schematic diagram of a power distribution network fault type determination system provided by the present invention;

[0063] Figure 3 This is a schematic diagram of the distribution network fault type determination process provided in an embodiment of the present invention. Detailed Implementation

[0064] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0065] Example 1:

[0066] This invention discloses a method for determining the type of fault in a distribution network, such as... Figure 1 As shown, it includes:

[0067] S1 acquires the fault voltage signal, the standard comprehensive feature matrix, and the feature values ​​of the standard voltage signal;

[0068] S2 generates a comprehensive feature matrix corresponding to the fault voltage signal based on the fault voltage signal and the feature values ​​of the standard voltage signal using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform;

[0069] S3 compares the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type;

[0070] There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault.

[0071] S2, based on the fault voltage signal and the characteristic values ​​of the standard voltage signal, uses Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform to generate a comprehensive feature matrix corresponding to the fault voltage signal, specifically including:

[0072] S2-1 Hilbert Transform Computer Theory

[0073] The Hilbert transform (HT) is an important transform widely used in digital and communication systems, and it is also a common method for analyzing nonlinear signals.

[0074] For any signal x(t), its Hilbert transform It can be represented as

[0075]

[0076] Therefore, complex signals can be further synthesized:

[0077]

[0078] Based on (2), the instantaneous amplitude, instantaneous angle and instantaneous frequency of the original signal can be directly obtained.

[0079] S2-2 Discrete Fourier Transform Computer Theory

[0080] The Discrete Fourier Transform (DFT) is one of the most mature methods for spectral analysis of signal systems. Because of its ability to convert time-domain signals to frequency-domain signals, the DFT is now widely used in embedded control devices and protective relays.

[0081] All nonlinear signals u(t) with period T = 2π / ω can be decomposed into

[0082]

[0083] Through the discrete Fourier transform, we can obtain

[0084]

[0085]

[0086] Based on (4) and (5), u na and u nb If we consider them as the real and imaginary parts of the nth harmonic respectively, we can obtain the relevant parameters of the nth harmonic.

[0087] S2-3 Hilbert-Huang Transform Computer Theory

[0088] Due to its high resolution in both the time and frequency domains, the Hilbert-Huang transform is highly effective for analyzing transient information in signals. A complete Hilbert-Huang transform consists of two processes: Empirical Mode Decomposition (EMD) and a Hilbert filter. Using EMD, the original fault signal can be decomposed into a series of intrinsic mode function (IMF) components and residuals characterizing time-domain parameters.

[0089]

[0090] In the formula, c i (t) and r n (t) represents the nth IMF component and the residual, respectively.

[0091] To obtain a finite number of IMF components, EMD adaptively decomposes the waveform of a given signal at different scales based on local feature scales. In general, the first decomposed IMF component (IMF1) has the smallest feature time scale and is therefore most similar to the original signal, while subsequent IMF components have a smaller similarity to the original signal due to their larger feature time scales.

[0092] Next, the instantaneous amplitude and frequency are calculated using a Hilbert filter.

[0093] S2-4 Generation of Synthetic Characteristic Moments

[0094] S2-4-1 initializes a 3×3 empty matrix, where (i,j) represents the j-th signal of the i-th fault detection method. Specifically, j1, j2, j3 represent the original three-phase voltage signals U. a U b U c i1, i2, and i3 represent the RMS detection method based on Hilbert transform, the harmonic detection method based on discrete Fourier transform, and the sudden voltage detection method based on Hilbert-Huang transform, respectively.

[0095] S2-4-2 processes the original signal according to a specific standard to obtain the binary representation of the definite eigenvalues ​​of each element in the comprehensive feature matrix.

[0096] The principle for calculating each element of a matrix is ​​as follows.

[0097] First, the Hilbert transform is applied to calculate the RMS value of each phase voltage waveform, and the first row of the matrix is ​​constructed from (7):

[0098]

[0099] In the formula, a V,j and NOM V,j These represent the fault waveform RMS value of the j-th voltage signal and the normal waveform RMS value of the j-th voltage signal in the power distribution system, respectively.

[0100] The phase voltage harmonic information of the input signal is extracted using Discrete Fourier Transform, and the second row of the matrix is ​​filled using (8).

[0101]

[0102] Among them, P O / H,j , and P E / H,j Let represent the maximum values ​​of the even and odd harmonic amplitudes of the j signals obtained by the discrete Fourier transform, respectively, and let a be the threshold, which is one-twentieth of the fundamental amplitude.

[0103] Finally, within one cycle of the corresponding phase voltage waveform, each element in the third row is filled using a sudden voltage detection method based on the Hilbert-Huang transform. Here, the characteristic value is the sum of the instantaneous amplitudes of IMF1 obtained by the Hilbert-Huang transform (abbreviated as ASIMF1).

[0104]

[0105] In the formula, S V,j S NOM,j These represent the fault waveform ASIMF1 value of the j-th voltage signal and the normal waveform ASIMF1 value of the j-th voltage signal in the power distribution system, respectively.

[0106] S3 compares the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type. The specific power grid fault types corresponding to the standard comprehensive feature matrix are shown in Table 1.

[0107] Table 1 Standard Comprehensive Feature Matrix

[0108]

[0109] A flowchart illustrating the process for determining the type of fault in a power distribution network is shown below. Figure 3 As shown:

[0110] Step 1: Input the normal operating three-phase voltage RMS value (NOM) of the distribution network. V,j and ASIMF1 value S NOM,j ;

[0111] Step 2: Input the three-phase voltage waveform of the signal to be judged;

[0112] Step 3: Calculate the RMS value of the three-phase voltage of the signal to be judged using Hilbert transform;

[0113] The three-phase voltage harmonic signal of the signal to be judged is calculated using the Discrete Fourier Transform.

[0114] The ASIMF1 value of the three-phase voltage of the signal to be judged is calculated using the Hilbert-Huang transform;

[0115] Calculate the comprehensive feature matrix of the signal to be judged according to formulas (7) to (9);

[0116] Step 4: Compare the synthesized feature matrix of the signal to be judged with the standard synthesized feature matrix:

[0117] If it matches the standard comprehensive feature matrix, output the corresponding fault type;

[0118] If it is inconsistent with the standard comprehensive feature matrix, the output is fault-free.

[0119] This invention employs a comprehensive feature matrix-based method for distribution network fault identification, which incorporates a voltage RMS detection method based on Hilbert transform, a harmonic information detection method based on discrete Fourier transform, and abrupt change information detection method based on Hilbert-Huang transform. This method achieves high-precision identification of single-phase short-circuit ground faults, two-phase-to-phase short-circuit faults, two-phase short-circuit ground faults, and three-phase short-circuit ground faults in distribution networks.

[0120] This invention fully utilizes a compromise combination of three traditional fault identification methods: the Hilbert transform, which can accurately detect the steady-state characteristics of long and short-distance transmission voltage signals in distribution networks and has good noise immunity; the Discrete Fourier transform, which can sensitively detect harmonic information of distribution network faults and harmonic information generated by nonlinear load switching; and the Hilbert-Huang transform, which has good discrimination performance for fault transient information of distribution network faults (including high-impedance faults). This combination can accurately identify common but difficult-to-identify long-distance transmission faults, nonlinear load switching interference, noise interference faults, and complex faults such as high-impedance grounding in distribution networks. This improves the selectivity and sensitivity of distribution network relay protection and ensures the safety and reliability of distribution network relay protection.

[0121] Example 2:

[0122] This invention discloses a system for determining the type of fault in a power distribution network, such as... Figure 2 As shown, it includes:

[0123] The data acquisition module is used to acquire fault voltage signals, standard comprehensive feature matrices, and standard voltage signal feature values;

[0124] The comprehensive feature matrix generation module is used to generate a comprehensive feature matrix corresponding to the fault voltage signal based on the fault voltage signal and the feature values ​​of the standard voltage signal, using Hilbert transform, discrete Fourier transform and Hilbert-Huang transform;

[0125] The fault determination module is used to compare the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type;

[0126] There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault.

[0127] Preferably, the characteristic values ​​of the standard voltage signal include: the root mean square value of each phase voltage waveform and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform.

[0128] The comprehensive feature matrix generation module includes:

[0129] The root mean square value feature value sequence generation module is used to generate the feature value sequence of the root mean square value of the fault voltage signal by using Hilbert transform based on the root mean square values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal.

[0130] The harmonic characteristic value sequence generation module is used to generate the harmonic characteristic value sequence of the fault voltage signal based on the fault voltage signal using discrete Fourier transform.

[0131] The mutation feature value sequence generation module is used to generate the mutation feature value sequence of the fault voltage signal by using the Hilbert-Huang transform based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal.

[0132] The matrix synthesis module is used to generate a comprehensive feature matrix corresponding to the fault voltage signal based on the eigenvalue sequence, harmonic eigenvalue sequence, and abrupt change eigenvalue sequence of the root mean square value of the fault voltage signal.

[0133] The eigenvalue sequence generation module for root mean square values ​​includes:

[0134] The Hilbert transform module is used to calculate the envelope value of the fault voltage signal based on the fault voltage signal using the Hilbert transform.

[0135] The root mean square (RMS) value calculation module is used to calculate the root mean square (RMS) value of each phase voltage waveform of the fault voltage signal based on the envelope value of the fault voltage signal.

[0136] The first synthesis module is used to generate a sequence of feature values ​​of the root mean square value of the fault voltage signal based on the root mean square value of each phase voltage waveform of the fault voltage signal and the root mean square value of each phase voltage waveform of the standard voltage signal.

[0137] The harmonic eigenvalue sequence generation module includes:

[0138] The Discrete Fourier Transform module is used to calculate the maximum amplitude of the fundamental wave, the maximum amplitude of the odd harmonics, and the maximum amplitude of the even harmonics of each phase voltage waveform of the fault voltage signal using the Discrete Fourier Transform.

[0139] The second synthesis module is used to generate a harmonic characteristic value sequence of the fault voltage signal based on the fundamental maximum amplitude, odd harmonic maximum amplitude, and even harmonic maximum amplitude of each phase voltage waveform of the fault voltage signal.

[0140] The mutation feature value sequence generation module includes:

[0141] The Hilbert-Huang transform module is used to generate the instantaneous amplitude summation of the IMF1 components of the phase voltage waveform of the fault voltage signal within one cycle of each phase voltage waveform using the Hilbert-Huang transform.

[0142] The third synthesis module is used to generate a sequence of abrupt change characteristic values ​​of the fault voltage signal based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the fault voltage signal and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal.

[0143] The comprehensive feature matrix is ​​compared with the standard comprehensive feature matrix to obtain the power grid fault types, including:

[0144] Determine whether there exists a matrix in the standard comprehensive feature matrix that is consistent with the comprehensive feature matrix of the fault voltage signal:

[0145] If a consistent matrix exists, then the fault type of the power grid is the fault type corresponding to the consistent standard comprehensive feature matrix;

[0146] If a consistent matrix does not exist, the power grid is fault-free.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0149] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0150] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method for determining the type of fault in a distribution network, characterized in that, include: Obtain the fault voltage signal, standard integrated feature matrix, and standard voltage signal eigenvalues; Based on the fault voltage signal and the characteristic values ​​of the standard voltage signal, a comprehensive feature matrix corresponding to the fault voltage signal is generated using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform. The integrated feature matrix is ​​compared with the standard integrated feature matrix to obtain the power grid fault type; There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault. The standard voltage signal characteristic values ​​include: the root mean square value of each phase voltage waveform and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform; The process of generating a comprehensive feature matrix corresponding to the fault voltage signal based on the feature values ​​of the fault voltage signal and the standard voltage signal, using Hilbert transform, discrete Fourier transform, and Hilbert-Huang transform, includes: Based on the root mean square (RMS) values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal, a sequence of eigenvalues ​​of the RMS value of the fault voltage signal is generated using Hilbert transform. Based on the fault voltage signal, a sequence of harmonic characteristic values ​​of the fault voltage signal is generated using discrete Fourier transform; Based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal, the Hilbert-Huang transform is used to generate the abrupt change characteristic value sequence of the fault voltage signal; Based on the eigenvalue sequence, harmonic eigenvalue sequence, and abrupt change eigenvalue sequence of the root mean square value of the fault voltage signal, a comprehensive feature matrix corresponding to the fault voltage signal is generated. The process of generating a sequence of eigenvalues ​​of the fault voltage signal's root mean square (RMS) value based on the RMS values ​​of each phase voltage waveform of the standard voltage signal and the fault voltage signal using Hilbert transform includes: Based on the fault voltage signal, the envelope value of the fault voltage signal is calculated using Hilbert transform; Based on the envelope value of the fault voltage signal, calculate the root mean square value of the voltage waveform of each phase of the fault voltage signal; Based on the root mean square (RMS) values ​​of each phase voltage waveform of the fault voltage signal and the RMS values ​​of each phase voltage waveform of the standard voltage signal, a sequence of characteristic values ​​of the RMS value of the fault voltage signal is generated. The eigenvalue sequence of the root mean square value of the fault voltage signal is generated by the following formula: In the formula, Indicates the first j The root mean square value of each fault voltage signal waveform. Indicates the first j The root mean square value of a standard voltage signal waveform, where 1 in (1,j) indicates that it is located in the m1th row of the comprehensive feature matrix, and j represents the jth element of the feature value sequence of the root mean square value of the fault voltage signal. The value range of j is [1,3], which correspond to the voltage signals of the three phases respectively. The step of generating a harmonic characteristic value sequence of the fault voltage signal using discrete Fourier transform based on the fault voltage signal includes: The discrete Fourier transform is used to calculate the maximum amplitude of the fundamental wave, the maximum amplitude of the odd harmonics, and the maximum amplitude of the even harmonics of each phase voltage waveform of the fault voltage signal. Based on the fundamental maximum amplitude, odd harmonic maximum amplitude, and even harmonic maximum amplitude of each phase voltage waveform of the fault voltage signal, a harmonic characteristic value sequence of the fault voltage signal is generated. The harmonic characteristic value sequence of the fault voltage signal is generated by the following formula: In the formula, Indicates the first j The maximum amplitude of the even harmonic of the fault voltage signal. Indicates the first j The maximum amplitude of the odd harmonics of the fault voltage signal. a The threshold is the value of the first threshold. j One-twentieth of the fundamental amplitude of the fault voltage signal, where 2 in (2,j) represents the m2th row of the comprehensive feature matrix, j represents the jth element of the harmonic feature value sequence of the fault voltage signal, and the value range of j is [1,3], which correspond to the voltage signals of the three phases respectively. The process of generating a sequence of abrupt change characteristics of the fault voltage signal by summing the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal and the fault voltage signal using Hilbert-Huang transform includes: Within one cycle of the voltage waveform of each phase of the fault voltage signal, the instantaneous amplitude summation of the IMF1 component of each phase voltage waveform of the fault voltage signal is generated by Hilbert-Huang transform; Based on the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the fault voltage signal and the sum of the instantaneous amplitudes of the IMF1 components of each phase voltage waveform of the standard voltage signal, a sequence of abrupt change characteristic values ​​of the fault voltage signal is generated. The abrupt change sequence of the fault voltage signal is generated by the following formula: In the formula, Indicates the first j The sum of the instantaneous amplitudes of the IMF1 components of each fault voltage waveform. Indicates the first j The sum of the instantaneous amplitudes of the IMF1 components of the standard voltage waveform, (3,j), where 3 represents the m3th row of the comprehensive feature matrix, j represents the jth element of the abrupt change feature value sequence of the fault voltage signal, and the value range of j is [1,3], corresponding to the three-phase voltage signals respectively.

2. The method as described in claim 1, characterized in that, The comparison of the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type includes: Determine whether there exists a matrix in the standard comprehensive feature matrix that is consistent with the comprehensive feature matrix of the fault voltage signal: If a consistent matrix exists, then the fault type of the power grid is the fault type corresponding to the consistent standard comprehensive feature matrix; If a consistent matrix does not exist, the power grid is fault-free.

3. A system for determining the type of fault in a distribution network, used to implement the method as described in claim 1, characterized in that, include: The data acquisition module is used to acquire fault voltage signals, standard comprehensive feature matrices, and standard voltage signal feature values; The comprehensive feature matrix generation module is used to generate a comprehensive feature matrix corresponding to the fault voltage signal based on the fault voltage signal and the feature values ​​of the standard voltage signal, using Hilbert transform, discrete Fourier transform and Hilbert-Huang transform; The fault determination module is used to compare the comprehensive feature matrix with the standard comprehensive feature matrix to obtain the power grid fault type; There are multiple standard comprehensive feature matrices, and each standard comprehensive feature matrix corresponds to a type of power grid fault.

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