Power distribution network high-resistance fault rapid positioning method based on directional frequency scanning

Abstract

The invention relates to the technical field of power system fault detection, and discloses a power distribution network high-resistance fault rapid positioning method based on directional frequency scanning, and the method comprises the following steps: S1, intercepting a zero-sequence current signal I0j and a bus zero-sequence voltage signal U0 collected by each monitoring point of a power distribution network; s2, determining a fault occurrence moment tf; s3, extracting a first intrinsic mode function to be directly used for reconstructing an original fault signal; s4, projecting the first intrinsic mode function signals to a reference orthogonal vector group; and S5, distinguishing the upstream and the downstream of the fault point by using a clustering algorithm, and positioning a fault area by using a clustering result. According to the method for constructing the virtual reference direction through the orthogonal vector group, voltage transformers do not need to be installed at all measuring points, in addition, local rapid processing of fault data can be achieved through a distributed parallel computing mode, complete data do not need to be uploaded to a master station, therefore, hardware investment and maintenance cost are reduced, dependence on physical hardware is reduced, and the fault detection efficiency is improved. And meanwhile, the fault detection speed is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault detection, and in particular to a method for quickly locating high-resistance faults in a distribution network based on directional frequency scanning. Background Art

[0002] High-resistance faults in distribution networks are one of the most common and difficult to detect fault types. Such faults usually manifest as very weak current signals, often only in the order of a few amperes, which are easily masked by various noise signals in the distribution network. Therefore, accurately extracting the characteristics of high-resistance faults and quickly locating the fault location have become major challenges in the operation and maintenance of distribution networks.

[0003] Traditional methods usually rely on the measurement data of current and voltage transformers to identify and locate fault points through centralized calculations. However, these methods are not stable enough in extracting fault features due to their sensitivity to noise. In addition, a large amount of measurement data needs to be transmitted to the master station for centralized processing, which not only increases the data transmission burden, but also brings large delays, making it difficult to meet the requirements for rapid response to faults. In addition, installing expensive voltage transformers at all measurement points will result in a large amount of hardware investment and expensive maintenance costs. Summary of the invention

[0004] In order to make up for the above shortcomings, the present invention provides an image recognition method, which aims to improve the problem that traditional methods usually rely on measurement data of current and voltage transformers, and installing expensive voltage transformers at all measuring points will lead to large hardware investment and expensive maintenance costs.

[0005] In a first aspect, the present invention provides the following technical solution: a method for quickly locating a high-resistance fault in a distribution network based on directional frequency scanning, comprising the following steps:

[0006] S1. Signal acquisition: intercept the zero-sequence current signal I collected at each monitoring point of the distribution network 0j And bus zero sequence voltage signal U 0 , j is the corresponding serial number of the monitoring point, and the interception time of the zero-sequence current signal is [t f -T,t f +5T], t f is the time when the fault occurs, T is the power frequency cycle of the power grid;

[0007] S2. Determination of fault occurrence time: By analyzing the bus zero-sequence voltage signal U 0 , determine the fault occurrence time t f ;

[0008] S3. Discrete Fourier decomposition and reconstruction of fault characteristic signals: Discrete Fourier decomposition is used to reconstruct the zero-sequence current signal I collected at the monitoring point. 0jA directional frequency scanning strategy is constructed to extract the first intrinsic mode function and directly use it to reconstruct the original fault signal;

[0009] S4, eigenvector construction and projection: constructing a reference orthogonal vector group in a high-dimensional space, projecting the first intrinsic mode function signal onto the reference orthogonal vector group respectively, and generating a three-dimensional eigenvector;

[0010] S5. Feature fusion and clustering analysis: Based on the feature vectors of multiple time scales, a clustering algorithm is used to distinguish the upstream and downstream of the fault point, and the clustering results are used to locate the fault area.

[0011] Preferably, in step S2, determining the time when the fault occurs includes the following steps:

[0012] S201, to meet U for the first time 0 =0.15U n The time stamp t corresponding to the sampling sequence number set as a benchmark;

[0013] S202, at t set In the bus zero-sequence voltage sampling sequence of the first 100T, reverse search for the sampling point that meets the following conditions as t f :

[0014] |U(z)-U(z-4)<9&&|U(z-15)-U(z-30)|<4

[0015] Among them, U(z) is the discrete sampling value of the bus zero-sequence voltage, and z is the index number of the corresponding sampling moment; U(z-4) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-4; U(z-15) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-15; U(z-30) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-30.

[0016] Preferably, the calculation of the discrete Fourier transform includes:

[0017] For a discrete time series I of length N 0j [n] Perform Fourier expansion:

[0018]

[0019] Among them, I 0j [n] refers to the discrete time series of the zero-sequence current signal at the jth monitoring point; DFT(I 0j [0]) refers to the discrete Fourier transform result, which represents the frequency component of the signal in the frequency domain; N refers to the intercepted signal I 0jThe actual number of sampling points; k refers to the frequency index, representing the number of the current frequency component, with a value range of 1 ≤ k < N / 2 - 1; n refers to the discrete point index in the time series, with a value range of 1 ≤ n ≤ N; Refers to the phase rotation factor in the Fourier transform, representing the phase information of the frequency component;

[0020] Take the real part of the above result as the signal component for subsequent feature extraction.

[0021] Preferably, the extraction of the first intrinsic mode function includes:

[0022] First, ensure that the zero-sequence current signal I 0j The length N is even. When its length is odd, discard the last sampling value so that the length of I 0j The length N is always even;

[0023] For the frequency component f i [n] after Fourier transform, calculate the characteristic function according to the following formula:

[0024]

[0025] Among them, f i [n] refers to the amplitude of the i-th frequency component; φ i [n] refers to the phase angle of the i-th frequency component; DFT(I 0k [n]) refers to the discrete Fourier transform result, representing the frequency component of the signal in the frequency domain; Refers to the phase rotation factor;

[0026] Ensure And N / 2 ≥ N i +1, then take the real part of the above formula to obtain N / 2 - 1 characteristic functions, denoted as IMF m , m = 1, 2,..., N / 2 - 1; Take the first intrinsic mode function IMF 1 , directly used to reconstruct the original signal for the extraction of high-resistance fault characteristics, and denoted as

[0027] Preferably, in the step S4, the construction of the feature vector includes the following steps:

[0028] S401. Design a set of mutually orthogonal vector groups And For signal projection, the expression is:

[0029]

[0030] Among them, ω 0 Is the grid angular frequency, t k∈[t f ,t f +0.5T×P], P and k are both positive integers, t k+1 -t k =1 / f c , f c is the actual sampling frequency of the current sensor;

[0031] Typical value: A 1 =A 2 =1, f c =10kHz, t k+1 -t k =10 -4 ;

[0032] S402, project the first intrinsic mode function signal onto the above vector group, taking P = 1, f c Take the actual sampling frequency of the current sensor and intercept all monitoring points corresponding to [t f ,t f The first intrinsic mode function obtained by calculating the jth monitoring point is recorded as Will Projection is performed on the orthogonal vector group proposed in the above step S401 to obtain the projection result:

[0033]

[0034] Preferably, in step S4, the result of generating the feature vector is expressed in polar coordinate form:

[0035] S403, finally a set of three-dimensional vectors is obtained:

[0036]

[0037] It can be represented in polar coordinates as a set of J pairs of vectors:

[0038]

[0039] S404. Take all j=1,2,3,...,J, if the following inequality holds:

[0040]

[0041] Then take the vector group As the characteristic vector group for judging high resistance fault, otherwise take the vector group As a feature vector group for judging high resistance fault;

[0042] S405. Denote the feature vector group for judging high-resistance faults obtained in the previous step as

[0043] S406. Repeat the above steps "S402 - S405" with P = 2 and P = 4 respectively to obtain two other feature vector groups for judging high-resistance faults and

[0044] Preferably, in the S5 step, the feature fusion and clustering analysis includes:

[0045] S501. Use the fuzzy clustering algorithm to cluster the generated direction vector groups and . Set the number of clustering categories to 2, representing the upstream and downstream of the fault point respectively;

[0046] S502. Calculate the silhouette coefficients corresponding to the above three clustering results, denoted as SC 0.5T 、SC 1T and SC 2T . The silhouette coefficient formula is:

[0047] where a(i) represents the cohesion of the sample point, and the calculation method is as follows:

[0048]

[0049] where j represents other sample points in the same class as sample i, and distance represents the distance between i and j. Therefore, the smaller a(i) is, the closer the class is; the calculation method of b(i) is similar to that of a(i), but it is necessary to traverse other clusters to obtain multiple values {b 1 (i), b 2 (i), b 3 (i), …, b m (i)} and select the minimum value as the final result;

[0050] So the original S(i):

[0051]

[0052] It can be found from the above formula that:

[0053] When a(i) < b(i), that is, the distance within the class is less than the distance between classes, the clustering result is more compact, and the value of S will approach 1. The closer it is to 1, the more obvious the silhouette is;

[0054] On the contrary, when a(i) > b(i), the distance within the class is greater than the distance between classes, indicating that the clustering result is very loose, and the value of S will approach -1. The closer it is to -1, the worse the clustering effect.

[0055] S503, comparing the sizes of the three, taking the group of clustering results corresponding to the maximum value as the basis for determining the fault location, and determining the section location where the fault is located in combination with the actual network topology.

[0056] In a second aspect, the present invention provides the following technical solution, a distribution network high-resistance fault rapid location system based on directional frequency scanning, comprising:

[0057] Signal acquisition module, used to collect zero-sequence current signal I of each monitoring point in the distribution network 0j And bus zero sequence voltage signal U 0 , providing basic data for fault location;

[0058] Fault time determination module, used to analyze the bus zero-sequence voltage signal U 0 , by setting the threshold and reverse search algorithm to determine the exact time t when the fault occurs f ;

[0059] Feature extraction module, used to extract zero-sequence current signal I 0j Perform discrete Fourier transform to extract the first intrinsic mode function for reconstructing fault signal characteristics;

[0060] A feature vector construction module is used to generate a reference vector in a high-dimensional space, project the first intrinsic mode function to an orthogonal vector group, and construct a three-dimensional feature vector to intuitively represent the fault characteristics;

[0061] The feature fusion and analysis module is used to analyze the direction vector based on the fuzzy clustering algorithm, calculate the silhouette coefficient, and locate the fault section in combination with the network topology.

[0062] In the third aspect, the invention provides the following technical solution: a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for quickly locating high-resistance faults in a distribution network based on directional frequency scanning when executing the computer program.

[0063] In a fourth aspect, the present invention provides the following technical solution: a readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the above-mentioned method for quickly locating high-resistance faults in a distribution network based on directional frequency scanning.

[0064] The present invention has the following beneficial effects:

[0065] 1. In the present invention, the method of constructing a virtual reference direction through an orthogonal vector group does not need to install voltage transformers at all measuring points. In addition, its distributed parallel computing mode can realize local rapid processing of fault data without uploading complete data to the main station, thereby reducing hardware investment and maintenance costs, reducing dependence on physical hardware, and greatly improving the fault detection speed.

[0066] 2. In the present invention, by returning the processed three-dimensional feature vector instead of the complete sampled data, the data transmission volume and communication delay are significantly reduced compared with the traditional method, and the response speed of the system is improved. This is crucial for achieving real-time and efficient fault location, especially for situations where fast feedback is required in large-scale distribution networks.

[0067] 3. In the present invention, the first intrinsic mode function (IMF1) is extracted by adaptive Fourier decomposition (FDM), and the original fault signal can be effectively reconstructed in the noise background. This ensures that the characteristics of the high-resistance fault are accurately extracted, improves the recognition rate of the fault, and maintains high accuracy even in a complex noise environment.

[0068] 4. In the present invention, edge computing nodes are deployed at each sampling point, and each node independently completes data feature extraction and computing tasks, using distributed parallel computing to accelerate processing and effectively reduce the computing burden of the master station. This design improves the real-time, reliability and efficiency of the overall system, and is particularly suitable for application scenarios with strict requirements on fault response time. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A method flow chart of a method for quickly locating a high-resistance fault in a distribution network based on directional frequency scanning according to the present invention;

[0070] Figure 2 It is a schematic diagram of 7 zero-sequence current sampling points distributed in a 10kV medium voltage power distribution system of the present invention;

[0071] Figure 3 It is a schematic diagram of an ideal zero-sequence current sampling signal without considering the influence of noise interference at each measuring point of the present invention;

[0072] Figure 4 It is a schematic diagram of the zero-sequence current sampling signal of each measuring point of the present invention under -2.8dB strong noise interference;

[0073] Figure 5 It is a schematic diagram of a characteristic vector group in polar coordinate form under different fault angles of the present invention;

[0074] Figure 6 It is a schematic diagram of the polar coordinate form characteristic vector group under -2.8dB strong noise interference of the present invention. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Embodiment 1

[0077] Reference Figure 1-Figure 6 In a first embodiment of the present invention, the present invention provides a method for quickly locating a high-resistance fault in a distribution network based on directional frequency scanning, comprising the following steps:

[0078] S1. Signal acquisition: intercept the zero-sequence current signal I collected at each monitoring point of the distribution network 0j And bus zero sequence voltage signal U 0 , j is the corresponding serial number of the monitoring point, and the interception time of the zero-sequence current signal is [t f -T,t f +5T], t f is the time when the fault occurs, T is the power frequency cycle of the power grid;

[0079] S2. Determination of fault occurrence time: By analyzing the bus zero-sequence voltage signal U 0 , determine the fault occurrence time t f ;

[0080] S3. Discrete Fourier decomposition and reconstruction of fault characteristic signals: Discrete Fourier decomposition is used to reconstruct the zero-sequence current signal I collected at the monitoring point. 0j A directional frequency scanning strategy is constructed to extract the first intrinsic mode function and directly use it to reconstruct the original fault signal;

[0081] S4, eigenvector construction and projection: constructing a reference orthogonal vector group in a high-dimensional space, projecting the first intrinsic mode function signal onto the reference orthogonal vector group respectively, and generating a three-dimensional eigenvector;

[0082] S5. Feature fusion and clustering analysis: Based on the feature vectors of multiple time scales, a clustering algorithm is used to distinguish the upstream and downstream of the fault point, and the clustering results are used to locate the fault area.

[0083] Specifically, the process of the overall method is described, including the core steps of signal acquisition, determination of fault time, discrete Fourier transform and feature extraction, feature vector construction and projection, feature fusion and cluster analysis. Through directional frequency scanning and extraction of the first intrinsic mode function (IMF1), this method has a high fault feature extraction capability in a noisy environment and can reliably capture weak high-resistance fault signal characteristics; the edge computing method disperses the data processing tasks to each sampling point, greatly reducing the data transmission volume and the computational burden of the central station, and significantly improving the response speed; there is no need to install expensive voltage transformers at all monitoring points, and relying on the calculation of virtual reference directions, low-cost and high-precision fault location is achieved; the use of multi-time scale analysis and fuzzy clustering technology can effectively improve the robustness of fault detection and location, and avoid misjudgment caused by the randomness of data at a single time point.

[0084] In step S2, determining the fault occurrence time includes the following steps:

[0085] S201, to meet U for the first time 0 =0.15U n The time stamp t corresponding to the sampling sequence number set as a benchmark;

[0086] S202, at t set In the bus zero-sequence voltage sampling sequence of the first 100T, reverse search for the sampling point that meets the following conditions as t f :

[0087] |U(z)-U(z-4)|<9&&|U(z-15)-U(z-30)|<4

[0088] Among them, U(z) is the discrete sampling value of the bus zero-sequence voltage, and z is the index number of the corresponding sampling moment; U(z-4) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-4; U(z-15) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-15; U(z-30) is the discrete sampling value of the bus zero-sequence voltage corresponding to the index number z-30.

[0089] Specifically, it adopts a specific zero-sequence voltage threshold condition U 0 =0.15U n As a reference, the exact time t when the fault occurred is finally determined by reversely searching for sampling points that meet specific conditions. f， Its functions and effects include:

[0090] Simplified calculation: Through simple voltage threshold comparison, the potential fault occurrence time range can be quickly locked, reducing the calculation complexity;

[0091] Improve accuracy: The reverse search combined with multi - condition constraints (such as |U(z)-U(z - 4)|<9) effectively avoids the misjudgment of moments caused by random noise or mismeasurement;

[0092] Enhance adaptability: This method is not only applicable to high - resistance faults but also can adapt to the judgment of other types of fault moments, with strong generality.

[0093] The calculation of the discrete Fourier transform includes:

[0094] Perform Fourier expansion on the discrete - time sequence I 0j [n] of length N:

[0095]

[0096] where I 0j [n] refers to the discrete - time sequence of the zero - sequence current signal at the j - th monitoring point; DFT(I 0j [0]) refers to the result of the discrete Fourier transform, representing the frequency components of the signal in the frequency domain; N refers to the actual number of sampling points of the intercepted signal I 0j ; k refers to the frequency index, representing the number of the current frequency component, with the value range of 1≤k<N / 2 - 1; n refers to the discrete - point index in the time sequence, with the value range of 1≤n≤N; refers to the phase rotation factor in the Fourier transform, representing the phase information of the frequency component;

[0097] Take the real part of the above result as the signal component for subsequent feature extraction.

[0098] Specifically, the implementation method of the discrete Fourier transform is defined. By expanding and decomposing the signal into frequency components, it is used to extract high - frequency and low - frequency features. Its functions and effects are reflected in:

[0099] Multi - band analysis: By Fourier transform, the zero - sequence current signal is decomposed into frequency components, laying a foundation for subsequent feature extraction and analysis;

[0100] Retain signal features: By using the real - part feature extraction method, it can reduce the computational complexity while maintaining the original characteristics of the signal;

[0101] Wide applicability: This step is not only applicable to high - resistance faults in the distribution network but also can be applied to other fault - detection scenarios involving frequency - domain analysis.

[0102] The extraction of the first intrinsic mode function includes:

[0103] First, ensure that the length N of the zero - sequence current signal I 0j is an even number. When its length is odd, discard the last sampling value so that the length N of I 0j is always an even number;

[0104] The frequency component f after Fourier transformation i [n] The characteristic function is calculated according to the following formula:

[0105]

[0106] Among them, f i [n] refers to the amplitude of the i-th frequency component; φ i [n] refers to the phase angle of the i-th frequency component; DFT(I 0k [n]) refers to the discrete Fourier transform result, which represents the frequency components of the signal in the frequency domain; refers to the phase rotation factor;

[0107] make sure and N / 2≥N i +1, and then take the real part of the above formula, we can get N / 2-1 characteristic functions, denoted as IMF m , m=1,2,...,N / 2-1; take the first intrinsic mode function IMF 1 , which is directly used to reconstruct the original signal to extract the high-resistance fault characteristics and is recorded as

[0108] Specifically, the extraction process of the first intrinsic mode function (IMF1) is described, the core of which is to obtain the main characteristic components of the signal through frequency scanning. Its functions and effects include:

[0109] Signal feature enhancement: IMF1, as the first mode of the signal, contains the most critical high-frequency components in the signal and can effectively enhance the fault features;

[0110] Noise reduction capability: By selecting the frequency monotonicity condition, the frequency components irrelevant to the fault are eliminated, effectively suppressing noise interference;

[0111] Optimized feature extraction: Adaptive Fourier decomposition technology is used to accurately extract the core features of high-resistance faults in complex environments.

[0112] In step S4, the feature vector construction includes the following steps:

[0113] S401. Design a set of mutually orthogonal vector groups and For signal projection, the expression is:

[0114]

[0115] Among them, ω 0 is the grid angular frequency, t k ∈[t f ,tf +0.5T×P], P and k are both positive integers, t k+1 -t k =1 / f c , f c is the actual sampling frequency of the current sensor;

[0116] Typical value: A 1 =A 2 =1, f c =10kHz, t k+1 -t k =10 -4 ;

[0117] S402, project the first intrinsic mode function signal onto the above vector group, taking P = 1, f c Take the actual sampling frequency of the current sensor and intercept all monitoring points corresponding to [t f ,t f The first intrinsic mode function obtained by calculating the jth monitoring point is recorded as Will Projection is performed on the orthogonal vector group proposed in the above step S401 to obtain the projection result:

[0118]

[0119] Specifically, a method for constructing feature vectors is proposed by designing an orthogonal vector group and Project the real signal of the first intrinsic mode function (IMF1) to generate a physically meaningful eigenvector. Its functions and effects include:

[0120] Intuitive representation: Through the projection of orthogonal vector groups, complex signals are decomposed into geometric features that are easy to analyze, improving the intuitiveness of fault analysis;

[0121] Spatial characteristic enhancement: The feature vector shows the distribution characteristics of the fault signal in a high-dimensional space, providing high-dimensional information support for subsequent fault location;

[0122] Reduced complexity: Orthogonal projection reduces the amount of raw signal data, optimizing computational efficiency while maintaining signal integrity.

[0123] In step S4, the result of feature vector generation is expressed in polar coordinates:

[0124] S403, finally a set of three-dimensional vectors is obtained:

[0125]

[0126] It can be represented in polar coordinates as a set of J pairs of vectors:

[0127]

[0128] S404. Take all j=1,2,3,...,J, if the following inequality holds:

[0129]

[0130] Then take the vector group As the characteristic vector group for judging high resistance fault, otherwise take the vector group As a feature vector group for judging high resistance fault;

[0131] S405: record the characteristic vector group for judging high resistance fault obtained in the previous step as

[0132] S406, respectively taking P=2 and P=4 to repeat the above steps "S402-S405", to obtain another two sets of characteristic vector groups for judging high resistance faults and

[0133] Specifically, the calculation method of direction vector and three-dimensional feature vector is further refined, and the polar coordinate representation of feature vector is proposed. Its functions and effects include:

[0134] High-precision modeling: Accurately capture the directional characteristics of fault signals through the calculation of directional vectors;

[0135] Geometric representation optimization: The polar coordinate representation of three-dimensional feature vectors enhances the spatial distribution information of signal features and lays the foundation for cluster analysis;

[0136] Improved robustness: Polar coordinate representation avoids the deviation caused by the selection of coordinate system and improves the ability to express fault signal characteristics.

[0137] In step S5, feature fusion and clustering analysis include:

[0138] S501, generating a direction vector group and The fuzzy clustering algorithm is used for clustering, and the number of cluster categories is set to 2, representing the upstream and downstream of the fault point respectively;

[0139] S502: Calculate the silhouette coefficients corresponding to the three clustering results, recorded as SC 0.5T , SC 1T and SC 2T , the silhouette coefficient formula is:

[0140] Among them, a(i) represents the cohesion of the sample points, and the calculation method is as follows:

[0141]

[0142] Among them, j represents other sample points within the same class as sample i, and distance represents the distance between i and j. Therefore, the smaller a(i) is, the closer the class is; the calculation method of b(i) is similar to that of a(i), but it is necessary to traverse other clusters to obtain multiple values {b 1 (i), b 2 (i), b 3 (i), …, b m (i)} and select the minimum value as the final result;

[0143] So the original S(i):

[0144]

[0145] It can be found from the above formula that:

[0146] When a(i) < b(i), that is, the distance within the class is less than the distance between classes, the clustering result is more compact, and the value of S will approach 1. The closer it is to 1, the more obvious the silhouette is;

[0147] On the contrary, when a(i) > b(i), the distance within the class is greater than the distance between classes, indicating that the clustering result is very loose, and the value of S will approach -1. The closer it is to -1, the worse the clustering effect.

[0148] S503. Compare the sizes of the three, and take the clustering result corresponding to the maximum value as the basis for judging the fault location, and combine the actual network topology to determine the section location where the fault is located.

[0149] Specifically, a feature fusion and analysis method based on the fuzzy clustering algorithm (FCM) and the silhouette coefficient calculation is defined in detail, and its functions and effects include:

[0150] Fault section judgment: By clustering the direction vectors, quickly identify the upstream and downstream distribution of the fault point;

[0151] Optimization of classification effect: The introduction of the silhouette coefficient can quantify the clustering effect and ensure the accuracy of the classification result;

[0152] Combined with network topology: Through the clustering result combined with the network topology structure, further accurately locate the section where the fault is located;

[0153] Calculation flexibility: The fuzzy clustering algorithm has strong adaptability and can handle the distribution of feature vectors in different scenarios.

[0154] Figure 5The polar coordinate form of the characteristic vector group under different fault angles is represented in the figure (a) a = 90°, (b) a = 60°, (c) a = 0°, and (d) a = -30°. The vector group in the (light green) shaded part is the characteristic vector group that is finally selected as the characteristic vector group for judging high-resistance faults, and the other group must be discarded.

[0155] Embodiment 2:

[0156] Reference Figure 1 In a second embodiment of the present invention, the present invention provides a distribution network high-resistance fault rapid location system based on directional frequency scanning, comprising:

[0157] Signal acquisition module, used to collect zero-sequence current signal I of each monitoring point in the distribution network 0j And bus zero sequence voltage signal U 0 , providing basic data for fault location;

[0158] Fault time determination module, used to analyze the bus zero-sequence voltage signal U 0 , by setting the threshold and reverse search algorithm to determine the exact time t when the fault occurs f ;

[0159] Feature extraction module, used to extract zero-sequence current signal I 0j Perform discrete Fourier transform to extract the first intrinsic mode function for reconstructing fault signal characteristics;

[0160] A feature vector construction module is used to generate a reference vector in a high-dimensional space, project the first intrinsic mode function to an orthogonal vector group, and construct a three-dimensional feature vector to intuitively represent the fault characteristics;

[0161] The feature fusion and analysis module is used to analyze the direction vector based on the fuzzy clustering algorithm, calculate the silhouette coefficient, and locate the fault section in combination with the network topology.

[0162] Embodiment 3

[0163] The third embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method for quickly locating high-resistance faults in distribution networks based on directional frequency scanning in the above-mentioned embodiment.

[0164] Embodiment 4

[0165] The fourth embodiment of the present invention is based on the same inventive concept. The present invention proposes a computer device, the terminal includes: a processor, a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory, and execute the method for quickly locating high-resistance faults in the distribution network based on directional frequency scanning of the above embodiment.

[0166] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0167] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quickly locating high-resistance faults in a distribution network based on directional frequency scanning, characterized in that: It includes the following steps: S1. Signal acquisition: intercept the zero-sequence current signal I collected at each monitoring point of the distribution network 0j and bus zero-sequence voltage signal U0, j is the corresponding serial number of the monitoring point, and the zero-sequence current signal interception time is [t f -T,t f +5T], t f is the time when the fault occurs, T is the power frequency cycle of the power grid; S2. Determination of the fault occurrence time: By analyzing the bus zero-sequence voltage signal U0, the fault occurrence time t f ; S3. Discrete Fourier decomposition and reconstruction of fault characteristic signals: Discrete Fourier decomposition is used to reconstruct the zero-sequence current signal I collected at the monitoring point. 0j A directional frequency scanning strategy is constructed to extract the first intrinsic mode function and directly use it to reconstruct the original fault signal; S4. Feature vector construction and projection: Construct a reference orthogonal vector group in a high-dimensional space, project the first intrinsic mode function signal onto the reference orthogonal vector group respectively to generate three-dimensional feature vectors; S5. Feature fusion and clustering analysis: Based on the feature vectors of multiple time scales, use a clustering algorithm to distinguish the upstream and downstream of the fault point, and use the clustering results to locate the fault area.

2. The method for quickly locating high-resistance faults in distribution network based on directional frequency scanning according to claim 1 is characterized in that: In the step S2, the determination of the fault occurrence time includes the following steps: S201, to first meet U0=0.15U n The time stamp t corresponding to the sampling sequence number set as a benchmark; S202, at t set In the bus zero-sequence voltage sampling sequence of the first 100T, reverse search for the sampling point that meets the following conditions as t f : |U(z)-U(z - 4)|<9 && |U(z - 15)-U(z - 30)|<4 Where, U(z) is the discrete sampling value of the zero-sequence voltage of the bus, z is the index number of the corresponding sampling time; U(z - 4) is the discrete sampling value of the zero-sequence voltage of the bus corresponding to the index number z - 4; U(z - 15) is the discrete sampling value of the zero-sequence voltage of the bus corresponding to the index number z - 15; U(z - 30) is the discrete sampling value of the zero-sequence voltage of the bus corresponding to the index number z - 30.

3. The method for quickly locating high-resistance faults in distribution network based on directional frequency scanning according to claim 1 is characterized in that: The calculation of the discrete Fourier transform includes: For a discrete time series I of length N 0j [n] Perform Fourier expansion: Among them, I 0j [n] refers to the discrete-time sequence of zero-sequence current signals at the jth monitoring point; DFT(I 0j [0]) refers to the result of discrete Fourier transform, representing the frequency components of the signal in the frequency domain; N refers to the actual number of sampling points of the intercepted signal I 0j ; k refers to the frequency index, representing the number of the current frequency component, with a value range of 1 ≤ k < N / 2 - 1; n refers to the discrete point index in the time series, with a value range of 1 ≤ n ≤ N; refers to the phase rotation factor in the Fourier transform, representing the phase information of the frequency component; Take the real part of the above result as the signal component for subsequent feature extraction.

4. The method for quickly locating high-resistance faults in distribution network based on directional frequency scanning according to claim 1 is characterized in that: The extraction of the first intrinsic mode function includes: First, ensure that the zero-sequence current signal I 0j When the length N is an even number, the last sample value is discarded so that I 0j The length N is always an even number; The frequency component f after Fourier transformation i [n] The characteristic function is calculated according to the following formula: Among them, f i [n] refers to the amplitude of the i-th frequency component; φ i [n] refers to the phase angle of the i-th frequency component; DFT(I 0k [n]) refers to the discrete Fourier transform result, which represents the frequency components of the signal in the frequency domain; refers to the phase rotation factor; make sure and N / 2≥N i +1, and then take the real part of the above formula, we can get N / 2-1 characteristic functions, denoted as IMF m , m=1,2,...,N / 2-1; take the first intrinsic mode function IMF1, directly use it to reconstruct the original signal to extract the high resistance fault characteristics, and record it as 5. The method for quickly locating high-resistance faults in distribution network based on directional frequency scanning according to claim 1 is characterized in that: In the step S4, the feature vector construction includes the following steps: S401. Design a set of mutually orthogonal vector groups and For signal projection, the expression is: Where ω0 is the grid angular frequency, t k ∈[t f ,t f +0.5T×P], P and k are both positive integers, t k+1 -t k =1 / f c , f c is the actual sampling frequency of the current sensor; Typical values ​​are: A1=A2=1, f c =10kHz, t k+1 -t k =10 -4 ; S402, project the first intrinsic mode function signal onto the above vector group, taking P = 1, f c Take the actual sampling frequency of the current sensor and intercept all monitoring points corresponding to [t f ,t f The first intrinsic mode function obtained by calculating the jth monitoring point is recorded as Will Projection is performed on the orthogonal vector group proposed in the above step S401 to obtain the projection result:

6. The method for quickly locating high-resistance faults in a distribution network based on directional frequency scanning according to claim 1, characterized in that: In the step S4, the result of the feature vector generation is represented in polar coordinate form: S403. Finally, a group of three-dimensional vectors obtained are: It can be correspondingly represented as J pairs of vector groups in polar coordinates: S404. Take all j = 1, 2, 3,..., J. If the following inequality can hold: Then take the vector group As the characteristic vector group for judging high resistance fault, otherwise take the vector group As a feature vector group for judging high resistance fault; S405: record the characteristic vector group for judging high resistance fault obtained in the previous step as S406, respectively taking P=2 and P=4 to repeat the above steps "S402-S405" to obtain another two sets of characteristic vectors for judging high resistance faults and 7. The method for quickly locating high-resistance faults in distribution network based on directional frequency scanning according to claim 1 is characterized in that: In the step S5, the feature fusion and clustering analysis include: S501, generate a direction vector group and The fuzzy clustering algorithm is used for clustering, and the number of cluster categories is set to 2, representing the upstream and downstream of the fault point respectively; S502: Calculate the silhouette coefficients corresponding to the three clustering results, recorded as SC 0.5T , SC 1T and SC 2T , the silhouette coefficient formula is: Where, a(i) represents the cohesion of the sample point, and the calculation method is as follows: Where j represents other sample points in the same class as sample i, and distance represents the distance between i and j. Therefore, the smaller a(i) is, the closer the class is. The calculation method of b(i) is similar to a(i), but it is necessary to traverse other clusters to obtain multiple values ​​{b1(i), b2(i), b3(i), …, b m (i)} Select the smallest value as the final result; So the original S(i): It can be found from the above formula: When a(i)<b(i), that is, the distance within the class is less than the distance between classes, the clustering result is more compact, and the value of S will approach 1. The closer it is to 1, the more obvious the contour is; On the contrary, when a(i)>b(i), the distance within the class is greater than the distance between classes, indicating that the clustering result is very loose, and the value of S will approach -1. The closer it is to -1, the worse the clustering effect is. S503. Compare the magnitudes of the three, and take the clustering result corresponding to the maximum value as the basis for judging the fault location, and determine the section location where the fault is located in combination with the actual network topology.

8. A distribution network high-resistance fault rapid location system based on directional frequency scanning, characterized in that: For the method for rapid location of high-resistance faults in a distribution network based on directional frequency scanning according to any one of claims 1 - 7, it includes: Signal acquisition module, used to collect zero-sequence current signal I of each monitoring point in the distribution network 0j And bus zero-sequence voltage signal U0, providing basic data for fault location; The fault time determination module is used to analyze the bus zero-sequence voltage signal U0 and determine the exact time t when the fault occurs by setting the threshold and the reverse search algorithm. f ; Feature extraction module, used to extract zero-sequence current signal I 0j Perform discrete Fourier transform to extract the first intrinsic mode function for reconstructing fault signal characteristics; A feature vector construction module, configured to generate a reference vector in a high-dimensional space, project the first intrinsic mode function onto the orthogonal vector group, construct three-dimensional feature vectors, and visually represent the fault features; A feature fusion and analysis module, configured to analyze the direction vectors based on a fuzzy clustering algorithm, calculate the silhouette coefficient, and locate the fault section in combination with the network topology structure.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the method for rapid location of high-resistance faults in a distribution network based on directional frequency scanning according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for quickly locating a high-resistance fault in a distribution network based on directional frequency scanning as described in any one of claims 1 to 7 is implemented.

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