Single-phase fault interval positioning method and device, electronic equipment and storage medium

Abstract

The invention discloses a single-phase fault interval positioning method and device, electronic equipment and a storage medium, and belongs to the technical field of power distribution networks, and the method comprises the steps: obtaining three-phase current data in a fault sampling period; according to the three-phase current data, determining a fault phase line based on grey correlation analysis; extracting current data of a fault phase line in the three-phase current data, and further determining a fault feeder line according to the current data of the fault phase line based on transient zero-sequence current group amplitude comparison; and extracting current data of a fault feeder line in the three-phase current data, and further determining a fault interval according to the current data of the fault feeder line based on a K-means clustering algorithm. According to the method, fault phase selection is analyzed through the grey correlation degree, fault line selection is performed through amplitude comparison of the transient zero-sequence current group, and the fault interval is positioned through the K-means clustering algorithm, so that the accuracy of single-phase fault interval positioning can be effectively improved, different working environments can be adapted, and the safety and stability of the power distribution network are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of power distribution networks, and in particular to a single-phase fault interval locating method, device, electronic device and storage medium. Background Art

[0002] At present, the stability of the distribution network is closely related to the power supply security of users.

[0003] In related technologies, the steady-state component is usually used to locate single-phase grounding faults. However, in actual applications, it is found that due to factors such as neutral point grounding mode, transition resistance and intermittent grounding, the accuracy of fault line selection and interval positioning is poor, which can easily lead to insulation weak points in the power grid being broken down, thereby damaging electrical equipment and threatening the safe and stable operation of the power grid.

[0004] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0005] The embodiments of the present application provide a single-phase fault interval locating method, device, electronic device and storage medium, which can effectively improve the accuracy of single-phase fault interval locating, can adapt to different working environments, and thus effectively improve the safety and stability of the distribution network.

[0006] On the one hand, an embodiment of the present application provides a single-phase fault interval locating method, comprising the following steps:

[0007] Obtain three-phase current data within the fault sampling period;

[0008] According to the three-phase current data, determining the fault phase line based on grey relational analysis;

[0009] Extracting the current data of the fault phase line from the three-phase current data, and then determining the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio;

[0010] The current data of the faulty feeder is extracted from the three-phase current data, and then based on the K-means clustering algorithm, the fault interval is determined according to the current data of the faulty feeder.

[0011] Optionally, determining the fault phase line based on the three-phase current data and grey relational analysis includes:

[0012] Extracting three-phase current transient components from the three-phase current data, and then constructing an evaluation matrix according to the three-phase current transient components;

[0013] Normalizing the evaluation matrix to obtain a normalized matrix, and then standardizing the normalized matrix to obtain a standardized matrix;

[0014] Sequentially set any one of the three phases as a reference phase, calculate a correlation coefficient sequence according to the standardized matrix, and then construct a correlation coefficient matrix based on all the correlation coefficient sequences;

[0015] According to the correlation coefficient matrix, the inter-phase correlation is calculated; the inter-phase correlation is the correlation between any two different phase lines;

[0016] The faulty phase line is determined according to the phase-to-phase correlation.

[0017] Optionally, extracting the current data of the faulty phase line from the three-phase current data, and then determining the faulty feeder according to the current data of the faulty phase line based on the transient zero-sequence current group ratio, comprises:

[0018] Extracting zero-sequence current data of the fault phase line from the three-phase current data;

[0019] Determine the group zero-sequence current amplitude and the maximum zero-sequence current amplitude of each feeder in the fault phase line;

[0020] Determining the transient zero-sequence current group ratio amplitude of each feeder according to the ratio of the group zero-sequence current amplitude of each feeder to the maximum zero-sequence current amplitude;

[0021] The feeder with the largest transient zero-sequence current group amplitude is determined as the fault feeder.

[0022] Optionally, extracting current data of the faulty feeder from the three-phase current data, and then determining the fault interval according to the current data of the faulty feeder based on a K-means clustering algorithm, includes:

[0023] Extracting current data of the fault feeder from the three-phase current data;

[0024] Extracting features from the current data of the fault feeder to obtain a time domain feature component sequence array;

[0025] Determine a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array;

[0026] The fault interval is determined according to the clustering result and the distribution network topology.

[0027] Optionally, the time domain characteristic component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the time domain.

[0028] Optionally, the method further comprises:

[0029] Performing discrete Fourier transform on the current data of the fault feeder to obtain frequency spectrum data of the fault feeder;

[0030] Extracting features from the frequency spectrum data of the faulty feeder to obtain a frequency domain feature component sequence array;

[0031] Determine a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array and the frequency domain feature component sequence array;

[0032] The fault interval is determined according to the clustering result and the distribution network topology.

[0033] Optionally, the frequency domain characteristic component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the frequency domain.

[0034] On the other hand, an embodiment of the present application provides a single-phase fault interval locating device, the device comprising:

[0035] A data acquisition module, used to acquire three-phase current data within a fault sampling period;

[0036] A fault phase selection module is used to determine the fault phase line based on the three-phase current data and grey correlation analysis;

[0037] A fault line selection module is used to extract the current data of the fault phase line from the three-phase current data, and then determine the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio;

[0038] The fault location module is used to extract the current data of the fault feeder from the three-phase current data, and then determine the fault interval according to the current data of the fault feeder based on the K-means clustering algorithm.

[0039] On the other hand, an embodiment of the present application provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned single-phase fault interval locating method when executing the computer program.

[0040] On the other hand, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned single-phase fault interval locating method is implemented.

[0041] The embodiments of the present application select fault phases through grey correlation analysis, select fault lines through transient zero-sequence current group amplitude ratio, and locate fault intervals through K-means clustering algorithm, which can effectively improve the accuracy of single-phase fault interval positioning, adapt to different working environments, and effectively improve the safety and stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of an implementation environment of a single-phase fault interval locating method provided in an embodiment of the present application;

[0043] Figure 2 It is a flow chart of a single-phase fault interval locating method provided in an embodiment of the present application;

[0044] Figure 3 It is a schematic diagram of a process for determining a faulty phase line provided by an embodiment of the present application;

[0045] Figure 4 is a schematic diagram of a process for determining a faulty feeder provided in an embodiment of the present application;

[0046] Figure 5 It is a schematic diagram of a process for determining a fault interval provided in an embodiment of the present application;

[0047] Figure 6 It is a flow chart of another single-phase fault interval locating method provided in an embodiment of the present application;

[0048] Figure 7 It is a structural schematic diagram of a single-phase fault interval locating device provided in an embodiment of the present application;

[0049] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0051] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0052] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0054] At present, the stability of the distribution network is closely related to the power supply security of users. Although the line voltage of single-phase grounding faults that are more common in distribution networks remains symmetrical and the fault current is small after the fault, long-term operation with grounding points can easily lead to breakdown of weak insulation points in the power grid. In particular, intermittent arc grounding can also generate overvoltage, which in turn damages electrical equipment and threatens the safe and stable operation of the power grid.

[0055] In related technologies, the steady-state component is usually used to locate single-phase grounding faults. However, in actual applications, it is found that due to factors such as neutral point grounding mode, transition resistance and intermittent grounding, the accuracy of fault line selection and interval positioning is poor, which can easily lead to insulation weak points in the power grid being broken down, thereby damaging electrical equipment and threatening the safe and stable operation of the power grid.

[0056] In view of this, a single-phase fault interval locating method, device, electronic device and storage medium are provided in the embodiments of the present application. The fault phase is selected by grey correlation analysis, the fault line is selected by transient zero-sequence current group amplitude ratio, and the fault interval is located by K-means clustering algorithm. This can effectively improve the accuracy of single-phase fault interval locating, adapt to different working environments, and effectively improve the safety and stability of the distribution network.

[0057] The specific implementation of the embodiment of the present application is described in detail below in conjunction with the accompanying drawings. First, a single-phase fault interval locating method provided in the embodiment of the present application is described in conjunction with the accompanying drawings.

[0058] Please refer to Figure 1 , Figure 1 It is a schematic diagram of an implementation environment of a single-phase fault interval locating method provided in an embodiment of the present application. In this implementation environment, the main software and hardware entities involved include a terminal processor 110 and a server 120.

[0059] Specifically, the terminal processor 110 may be installed with a related single-phase fault interval location method control program, and the server 120 is a background server of the control program. The terminal processor 110 and the background server 120 are connected in communication. The single-phase fault interval location method provided in the embodiment of the present application may be executed on the terminal processor 110 side.

[0060] Server 120 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0061] In addition, the server 120 may also be a node server in the blockchain network.

[0062] The terminal processor 110 and the server 120 may establish a communication connection through a wireless network. The wireless network uses standard communication technology and / or protocols, and the network may be set to the Internet, or any other network, such as but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, or any combination of a wireless network, a private network, or a virtual private network. Moreover, the above-mentioned software and hardware entities may use the same communication connection mode or different communication connection modes, and this application does not impose any specific restrictions on this.

[0063] Of course, it is understandable that Figure 1 The implementation environment in the present application is only some optional application scenarios of the single-phase fault interval location method provided in the embodiment of the present application, and the actual application is not fixed. Figure 1 The software and hardware environment shown is not specifically limited in this application.

[0064] like Figure 2 As shown, Figure 2 It is a flow chart of a single-phase fault interval locating method provided in an embodiment of the present application, which specifically includes but is not limited to steps S100 to S400.

[0065] Step S100: Acquire three-phase current data within a fault sampling period.

[0066] In the embodiment of the present application, the three-phase current data can be obtained by analyzing the recording files collected by the distribution automation terminal at each monitoring point of the distribution line. In addition, by analyzing the recording files collected by several distribution automation terminals, the maximum amplitude moment of the zero-sequence current in the recording file is obtained, and the maximum amplitude moment with the highest frequency is determined as the fault occurrence time t f , and then the fault occurrence time t f The preset time Δt is used as the fault sampling period.

[0067] In practical applications, the fault occurrence time t f The recorded file data within the preset time Δt is analyzed and the transient component x of the three-phase current is extracted. h，m，t As three-phase current data, the three-phase current data may be in the format of a sampling array. For example, the total number of array elements n=Δt·f s , where f s is the sampling rate of the distribution automation terminal.

[0068] Step S200: Determine the faulty phase line based on the three-phase current data and grey relational analysis.

[0069] In the embodiment of the present application, since a large difference will occur between the transient phase currents of the healthy phase and the faulty phase when a single-phase grounding fault occurs, the gray correlation analysis is used to judge the correlation degree between the three-phase transient phase current waveforms, so as to determine the faulty phase line and complete the fault phase selection.

[0070] Specifically, in an optional implementation, please refer to Figure 3 , Figure 3 : is a flow chart of determining a faulty phase line provided by an embodiment of the present application, wherein determining the faulty phase line based on the three-phase current data and grey correlation analysis comprises:

[0071] Step S210, extracting the three-phase current transient components from the three-phase current data, and then constructing an evaluation matrix according to the three-phase current transient components;

[0072] Step S220, normalizing the evaluation matrix to obtain a normalized matrix, and then standardizing the normalized matrix to obtain a standardized matrix;

[0073] Step S230, sequentially setting any phase of the three phases as a reference phase, calculating a correlation coefficient sequence according to the standardized matrix, and then constructing a correlation coefficient matrix based on all the correlation coefficient sequences;

[0074] Step S240, calculating the inter-phase correlation degree according to the correlation coefficient matrix; the inter-phase correlation degree is the correlation degree between any two different phase lines;

[0075] Step S250: Determine the faulty phase line according to the phase-to-phase correlation.

[0076] In the embodiment of the present application, a distribution automation terminal can be randomly selected from a number of distribution automation terminals at each monitoring point of the distribution line, and the three-phase current transient component x collected by the distribution automation terminal can be extracted. h，m，t , construct the evaluation matrix L, for example, L = [X o , X a , X b , X c ] T , where X a represents the transient component sequence of phase current of phase A; X b represents the transient component sequence of phase current of phase B; X c represents the transient component sequence of phase current of phase C; X o Represents the sequence of transient components of the phase current of the selected reference phase.

[0077] Furthermore, the evaluation matrix L is normalized to obtain a normalized matrix S, and the normalized matrix S is standardized to obtain a standardized matrix Z.

[0078] For example, the normalization matrix Z = [Z o , Z a , Z b , Z c ] T Among them, normalization processing can eliminate the dimensional influence between different feature quantities and make the data at the same order of magnitude. For example, the data can be linearly transformed into the interval [0,1]; standardization processing can transform the data into a form with a mean of 0 and a standard deviation of 1.

[0079] Furthermore, any phase of the three phases is set as a reference phase in turn. For example, phase A can be used as a reference phase first, and the correlation coefficient sequence is calculated according to the obtained standardized matrix Z.

[0080] For example, the reference phase is set as phase A, and the correlation coefficient sequence Kaa, Kab, Kac between the transient components of the phase currents of phases A, B, and C and the reference phase A is calculated by the following formula (1):

[0081]

[0082] Where m = 2, 3, 4, corresponding to phase A, phase B, and phase C respectively; o represents the reference phase; i represents the number of measurements within Δt after the fault, and the maximum value is n = Δt·f s ;k ami represents the correlation coefficient corresponding to the normalized value of the transient component of the phase current between phase m and the reference phase A at the i-th sampling point; Z mi represents the value of the transient component of the phase current of phase m at the i-th sampling point in the standardized matrix Z; Z oi It represents the value of the transient component of the phase current of the reference phase at the i-th sampling point in the standardized matrix Z; μ is a resolution coefficient ranging from 0 to 1, which can usually be μ=0.5, or it can be defined by the user according to the specific usage scenario.

[0083] Furthermore, by summing up the correlation coefficients of any phase and the reference phase at all sampling points, a correlation coefficient sequence can be obtained. aai Then you can get Kaa.

[0084] Furthermore, the reference phase is set as phase B, and the correlation coefficient sequences Kba, Kbb, and Kbc between the transient components of the phase currents of phases A, B, and C and the reference phase B are calculated by the above formula (1), and the correlation coefficient matrix K is constructed according to the correlation coefficient sequences Kab, Kac, and Kbc, where the correlation coefficient matrix K = [Kab, Kac, Kbc]

[0085] Furthermore, the inter-phase correlation degree is calculated according to the correlation coefficient matrix K, which can be specifically calculated by the following formula (2), formula (3) and formula (4):

[0086]

[0087] Among them, P ab Indicates the interphase correlation between phase A and phase B; P ac Indicates the interphase correlation between phase A and phase C; P bc Indicates the interphase correlation between phase B and phase C; k abi , k aci and k bci It can be extracted from the correlation coefficient matrix.

[0088] Furthermore, the average correlation degree of each phase line can be calculated by the average value of the sum of the interphase correlation degrees between each phase and the other two phases, which can be calculated by the following formula (5), formula (6) and formula (7):

[0089]

[0090] Among them, P arepresents the average correlation degree of phase A; P b represents the average correlation degree of phase B; P c Represents the average correlation of the C phase.

[0091] Furthermore, the suspected fault evaluation index of each phase can be calculated according to the average correlation of each phase line, which can be calculated by the sum of the absolute values ​​of the difference between the average correlation of any phase and the average correlation of the other two phases. Specifically, it can be calculated by the following formulas (8), (9) and (10):

[0092] λ a =|P a -P b |+|P a -P c | (8)

[0093] λ b =|P b -P a |+|P b -P c | (9)

[0094] λ c =|P c -P a |+|P c -P b | (10)

[0095] Among them, λ a Indicates the suspected fault evaluation index of phase A; b Indicates the suspected fault evaluation index of phase B; c Indicates the suspected fault evaluation index of phase C.

[0096] Finally, a fault phase selection threshold can be set, and its value can be calculated by the following formula (11):

[0097]

[0098] Among them, λ set Indicates the fault phase selection threshold; K re represents the reliability coefficient, which can be 1.1 for example.

[0099] In practical applications, by obtaining the suspected fault evaluation index of each phase line and calculating the fault phase selection threshold, if the suspected fault evaluation index of any phase line is greater than or equal to the fault phase selection threshold, then the phase line can be determined to be a fault phase line. For example, λ a ≥λ set , it can be determined that phase A is the fault phase line.

[0100] Step S300: extracting the current data of the faulty phase line from the three-phase current data, and then determining the faulty feeder based on the transient zero-sequence current group ratio and the current data of the faulty phase line.

[0101] In an embodiment of the present application, when a single-phase grounding fault occurs, the capacitive current of the fault line is equal to the sum of the capacitive currents of all non-fault components (lines and arc suppression coils), so the transient zero-sequence current amplitude of the fault line is usually greater than the transient zero-sequence current amplitude of the healthy line.

[0102] Specifically, as an optional implementation, please refer to Figure 4 , Figure 4 : is a flow chart of determining a faulty feeder provided by an embodiment of the present application, wherein the current data of the faulty phase line in the three-phase current data is extracted, and then the faulty feeder is determined according to the current data of the faulty phase line based on the transient zero-sequence current group ratio, including:

[0103] Step S310, extracting zero-sequence current data of the fault phase line from the three-phase current data;

[0104] Step S320, determining the group zero-sequence current amplitude and the maximum zero-sequence current amplitude of each feeder in the faulty phase line;

[0105] Step S330, determining the transient zero-sequence current group ratio of each feeder according to the ratio of the group zero-sequence current amplitude of each feeder to the maximum zero-sequence current amplitude;

[0106] Step S340: determine that the feeder with the largest transient zero-sequence current group amplitude is the fault feeder.

[0107] In the embodiment of the present application, after the faulty phase line is determined, the three-phase current data collected by the monitoring points at the headends of all feeders under the faulty phase line can be extracted, and the zero-sequence current data of each feeder can be extracted therefrom.

[0108] Furthermore, taking any feeder as an example, the zero-sequence current amplitude of the feeder at each sampling moment within the preset time Δt is calculated, the maximum zero-sequence current amplitude of the feeder is determined, and the group zero-sequence current amplitude of the feeder is obtained by adding and summing the zero-sequence current amplitude at each sampling moment.

[0109] Further, the transient zero-sequence current group amplitude ratio of the feeder is determined by the ratio of the group zero-sequence current amplitude of the feeder to the maximum zero-sequence current amplitude.

[0110] In practical applications, the transient zero-sequence current group ratio of each feeder under the fault phase line is obtained by traversal calculation, and the feeder with the largest transient zero-sequence current group ratio is selected as the fault feeder.

[0111] Step S400: extracting the current data of the faulty feeder from the three-phase current data, and then determining the fault interval according to the current data of the faulty feeder based on a K-means clustering algorithm.

[0112] In the embodiment of the present application, when a single-phase grounding fault occurs in the feeder, there is a significant difference between the fault phase current upstream and downstream of the fault point, and when the current data of the feeder is processed by the K-means clustering algorithm, the abnormal situation will be clearly reflected in the different cluster centers in the clustering results. Among them, the K-means clustering algorithm can divide the sample data into several sample subsets according to the similarity or distance between the sample feature vectors, and each subset is defined as a class; similar samples are clustered in the same class, and dissimilar samples are scattered in different classes.

[0113] In the prior art, when steady-state quantities are used to locate faults, the accuracy of fault line selection and section positioning is usually poor due to the influence of factors such as neutral point grounding method, transition resistance and intermittent grounding. When transient quantities are used to locate faults, fault positioning is often performed based on only a single feature, which also has the defect of poor accuracy in fault line selection and section positioning.

[0114] Therefore, the present application can effectively improve the accuracy of single-phase fault interval positioning by selecting fault phases through grey correlation analysis, selecting fault lines through transient zero-sequence current group amplitude ratio, and locating fault intervals through K-means clustering algorithm.

[0115] Specifically, in an optional implementation, please refer to Figure 5 , Figure 5 : is a flow chart of determining a fault interval provided by an embodiment of the present application, wherein the current data of the fault feeder in the three-phase current data is extracted, and then the fault interval is determined according to the current data of the fault feeder based on the K-means clustering algorithm, including:

[0116] Step S410, extracting the current data of the fault feeder from the three-phase current data;

[0117] Step S420, extracting features from the current data of the fault feeder to obtain a time domain feature component sequence array;

[0118] Step S430, determining a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array;

[0119] Step S440: determine the fault interval according to the clustering result and the distribution network topology.

[0120] In the embodiment of the present application, after the faulty feeder is determined, the current data of the faulty feeder can be extracted, and then feature extraction is performed on the current data of the faulty feeder to obtain a time domain feature component sequence array.

[0121] Exemplarily, in an optional embodiment, the time domain feature component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the time domain.

[0122] It can be understood that the current data x collected by any distribution automation terminal on the fault feeder h，m，i Taking the current data as an example, feature extraction is performed on the current data, and the obtained feature components may include time domain features in 13 dimensions, including mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy.

[0123] The average value in the time domain describes the average value of the current signal over the entire period of time, which can be calculated using the following formula (12):

[0124]

[0125] Among them, X h，m，1 represents the average value in the time domain; x h，m，i is the current data collected by the distribution automation terminal; n is the number of data.

[0126] The standard deviation in the time domain indicates the discreteness of the current signal, that is, the fluctuation of the current signal data points relative to the mean value, which can be calculated by the following formula (13):

[0127]

[0128] Among them, X h，m，2 Represents the standard deviation in the time domain.

[0129] The root mean square in the time domain can be calculated by the following formula (14):

[0130]

[0131] Among them, X h，m，3 Represents the root mean square in the time domain.

[0132] The maximum value in the time domain can be obtained by the maximum amplitude of the current signal, which can be calculated by the following formula (15):

[0133] X h，m，4 =maxx h,m，j , 1≤i≤n (15)

[0134] Among them, X h，m，4 Indicates the maximum value in the time domain.

[0135] The minimum value in the time domain can be obtained by the minimum amplitude of the current signal, which can be calculated by the following formula (16):

[0136] X h,m,5 =min X h,m，j , 1≤i≤n (16)

[0137] Among them, X h，m，5 Indicates the minimum value in the time domain.

[0138] The peak-to-peak value in the time domain is the difference between the maximum value and the minimum value, which can be calculated using the following formula (17):

[0139] X h，m，6 =X h,m,4 -X h，m，5 (17)

[0140] Among them, X h，m，6 Represents the peak-to-peak value in the time domain.

[0141] The skewness in the time domain reflects the symmetry of the distribution and can be calculated using the following formula (18):

[0142]

[0143] Among them, X h，m，7 Represents the skewness in the time domain.

[0144] The kurtosis in the time domain measures the peak degree of the current signal distribution, which can be calculated by the following formula (19):

[0145]

[0146] Among them, X h，m，8 Represents the kurtosis in the time domain.

[0147] The crest factor in the time domain represents the ratio of the maximum value to the root mean square, which can be calculated using the following formula (20):

[0148] X h,m，9 =X h,m，4 / X h,m，3 (20)

[0149] Among them, X h，m，9 Represents the crest factor in the time domain.

[0150] The waveform factor in the time domain represents the ratio of the root mean square value to the absolute average value of the signal, which can be calculated using the following formula (21):

[0151]

[0152] Among them, X h，m，10 Represents the waveform factor in the time domain.

[0153] The pulse factor in the time domain represents the ratio of the maximum value to the absolute average value of the signal, which can be calculated using the following formula (22):

[0154]

[0155] Among them, X h，m,11 Represents the impulse factor in the time domain.

[0156] The margin factor in the time domain represents the ratio of the maximum value to the square root of the square mean value, which can be calculated using the following formula (23):

[0157]

[0158] Among them, X h，m，12 Represents the margin factor in the time domain.

[0159] The energy in the time domain can be calculated using the following formula (24):

[0160]

[0161] Among them, X h,m，13 Represents energy in the time domain.

[0162] Furthermore, after completing the time domain feature extraction calculation of the above 13 dimensions, the current data of each distribution automation terminal on the fault feeder can be traversed to extract features, and all the extracted time domain features can be constructed into a time domain feature component sequence array.

[0163] Optionally, the time domain feature component sequence array may be normalized, and the normalized time domain feature component sequence array may be clustered using a K-means clustering algorithm to obtain a final clustering result.

[0164] In practical applications, when a single-phase grounding fault occurs in the feeder, there is a significant difference between the fault phase currents upstream and downstream of the fault point. Therefore, the k-means clustering results combined with the known distribution network topology can complete the fault line selection and section location.

[0165] Specifically, as an optional implementation, the single-phase fault interval locating method provided by the present application further includes:

[0166] Performing discrete Fourier transform on the current data of the fault feeder to obtain frequency spectrum data of the fault feeder;

[0167] Extracting features from the frequency spectrum data of the faulty feeder to obtain a frequency domain feature component sequence array;

[0168] Determine a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array and the frequency domain feature component sequence array;

[0169] The fault interval is determined according to the clustering result and the distribution network topology.

[0170] In the embodiment of the present application, the current data of the faulty feeder may be processed by a discrete Fourier transform method to obtain the frequency spectrum data of the faulty feeder, and then obtain a frequency domain characteristic component sequence array.

[0171] Exemplarily, as an optional implementation, the frequency domain characteristic component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the frequency domain.

[0172] For example, the current data x collected by any distribution automation terminal on the fault feeder is h,m,i Taking as an example, discrete Fourier transform is performed on the current data, and then feature extraction is performed, and the characteristic components obtained may include frequency domain characteristics in 13 dimensions, including mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy.

[0173] It can be understood that the extraction and calculation method of the above-mentioned frequency domain features of a total of 13 dimensions can refer to the above-mentioned formulas (12) to (24), and this application will not go into details.

[0174] Furthermore, after completing the calculation of the frequency domain feature extraction of the above 13 dimensions, the current data of each distribution automation terminal on the fault feeder can be traversed to extract features, and all the extracted frequency domain features can be constructed into a frequency domain feature component sequence array.

[0175] Optionally, the frequency domain feature component sequence array can also be normalized, and the normalized time domain feature component sequence array and the frequency domain feature component sequence array can be clustered using the K-means clustering algorithm to obtain the final clustering result, and the fault interval can be determined in combination with the distribution network topology.

[0176] Next, in conjunction with a specific application implementation process, the single-phase fault interval locating method provided in the present invention is introduced and described in detail:

[0177] In an embodiment of the present application, a single-phase fault interval locating method is provided, which can be applied to the single-phase fault locating scenario of the distribution network. The method can effectively improve the accuracy of single-phase fault interval locating by grey correlation analysis, select the fault phase by transient zero-sequence current group amplitude ratio, and locate the fault interval by K-means clustering algorithm, and can adapt to different working environments, thereby effectively improving the safety and stability of the distribution network.

[0178] For details, please refer to Figure 6 , Figure 6 It is a flow chart of another single-phase fault interval locating method provided in an embodiment of the present application. When starting to locate the single-phase grounding fault interval of the distribution network, the three-phase current data within the fault sampling period can be obtained first. The three-phase current data can be obtained by analyzing the recording files collected by the distribution automation terminal at each monitoring point of the distribution line.

[0179] Furthermore, the three-phase current transient component in the three-phase current data is extracted, an evaluation matrix is ​​constructed, and the constructed evaluation matrix is ​​normalized to obtain a normalized matrix, and then the normalized matrix S is standardized to obtain a standardized matrix.

[0180] Furthermore, any one of the three phases is set as the reference phase in turn, and the correlation coefficient sequence is calculated according to the obtained standardized matrix. By summarizing the correlation coefficients between any phase and the reference phase at all sampling points, the correlation coefficient sequence can be obtained.

[0181] Furthermore, a correlation coefficient matrix is ​​constructed according to the correlation coefficient sequence, and the inter-phase correlation, average correlation, suspected fault evaluation index of each phase and fault phase selection threshold are calculated in turn, and finally the fault phase line is determined.

[0182] Furthermore, taking any feeder as an example, the zero-sequence current amplitude of the feeder at each sampling moment within a preset time is calculated, the maximum zero-sequence current amplitude of the feeder is determined, and the group zero-sequence current amplitude of the feeder is obtained by adding and summing the zero-sequence current amplitude at each sampling moment.

[0183] It can be understood that the transient zero-sequence current group amplitude ratio of the feeder can be determined by the ratio of the group zero-sequence current amplitude to the maximum zero-sequence current amplitude, and the feeder with the largest transient zero-sequence current group amplitude ratio is selected as the fault feeder.

[0184] Finally, the current data of the fault feeder is extracted, and the time domain features are extracted from it to construct the time domain feature component sequence array, which includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the time domain.

[0185] Furthermore, the current data of the faulty feeder can be subjected to discrete Fourier transform to obtain the spectrum data of the faulty feeder, and the spectrum data can be subjected to feature extraction to construct a frequency domain feature component sequence array. The frequency domain feature component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the frequency domain.

[0186] According to the feature vectors of 26 dimensions in total of the time domain feature component sequence array and the frequency domain feature component sequence array, normalization processing is performed, and the K-means clustering algorithm is used to cluster the normalized time domain feature component sequence array and the frequency domain feature component sequence array to obtain the final clustering result. Then, according to the clustering result and the topological structure of the distribution network, the fault interval is determined.

[0187] Therefore, through step-by-step fault phase selection, fault line selection and fault section positioning, the fault section when a single-phase grounding fault occurs can be accurately located, which helps maintenance personnel to quickly go to the fault point for maintenance work, thereby ensuring the stability of users' power consumption.

[0188] See also Figure 7 , Figure 7 is a structural schematic diagram of a single-phase fault interval locating device provided in an embodiment of the present application. The embodiment of the present application also provides a single-phase fault interval locating device 700, which can implement the above-mentioned single-phase fault interval locating method, and the device includes:

[0189] The data acquisition module 710 is used to acquire the three-phase current data within the fault sampling period;

[0190] A fault phase selection module 720 is used to determine the fault phase line based on the three-phase current data and grey correlation analysis;

[0191] A fault line selection module 730 is used to extract the current data of the fault phase line from the three-phase current data, and then determine the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio;

[0192] The fault location module 740 is used to extract the current data of the fault feeder from the three-phase current data, and then determine the fault interval according to the current data of the fault feeder based on the K-means clustering algorithm.

[0193] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0194] Please refer to Figure 8 , Figure 8 The hardware structure of the electronic device provided in the embodiment of the present application is illustrated, and the electronic device includes:

[0195] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0196] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 calls and executes the single-phase fault interval location method of the embodiment of the present application;

[0197] Input / output interface 803, used to implement information input and output;

[0198] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0199] A bus 805 that transmits information between the various components of the device (e.g., the processor 801, the memory 802, the input / output interface 803, and the communication interface 804);

[0200] The processor 801 , the memory 802 , the input / output interface 803 and the communication interface 804 are connected to each other in communication within the device via a bus 805 .

[0201] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned single-phase fault interval locating method is implemented.

[0202] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0203] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0204] The embodiments of the present application provide a single-phase fault interval locating method, device, electronic device and storage medium, which can effectively improve the accuracy of single-phase fault interval locating by fault phase selection through grey correlation analysis, fault line selection through transient zero-sequence current group amplitude ratio, and locate the fault interval through K-means clustering algorithm, can adapt to different working environments, and thus effectively improve the safety and stability of the distribution network.

[0205] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0206] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0207] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0208] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0209] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0210] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0211] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0212] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0213] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0214] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0215] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A single-phase fault interval locating method, characterized in that: The method comprises the following steps: Obtain three-phase current data within the fault sampling period; According to the three-phase current data, determining the fault phase line based on grey relational analysis; Extracting the current data of the fault phase line from the three-phase current data, and then determining the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio; The current data of the faulty feeder is extracted from the three-phase current data, and then based on the K-means clustering algorithm, the fault interval is determined according to the current data of the faulty feeder.

2. The single-phase fault interval locating method according to claim 1, characterized in that: The determining of the fault phase line based on the three-phase current data and grey relational analysis includes: Extracting three-phase current transient components from the three-phase current data, and then constructing an evaluation matrix according to the three-phase current transient components; Normalizing the evaluation matrix to obtain a normalized matrix, and then standardizing the normalized matrix to obtain a standardized matrix; Sequentially set any one of the three phases as a reference phase, calculate a correlation coefficient sequence according to the standardized matrix, and then construct a correlation coefficient matrix based on all the correlation coefficient sequences; According to the correlation coefficient matrix, the inter-phase correlation is calculated; the inter-phase correlation is the correlation between any two different phase lines; The faulty phase line is determined according to the phase-to-phase correlation.

3. The single-phase fault interval locating method according to claim 1, characterized in that: The extracting the current data of the fault phase line from the three-phase current data, and then determining the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio, comprises: Extracting zero-sequence current data of the fault phase line from the three-phase current data; Determine the group zero-sequence current amplitude and the maximum zero-sequence current amplitude of each feeder in the fault phase line; Determining the transient zero-sequence current group ratio amplitude of each feeder according to the ratio of the group zero-sequence current amplitude of each feeder to the maximum zero-sequence current amplitude; The feeder with the largest transient zero-sequence current group amplitude is determined as the fault feeder.

4. The single-phase fault interval locating method according to claim 1, characterized in that: The extracting the current data of the fault feeder from the three-phase current data, and then determining the fault interval according to the current data of the fault feeder based on a K-means clustering algorithm, includes: Extracting current data of the fault feeder from the three-phase current data; Extracting features from the current data of the fault feeder to obtain a time domain feature component sequence array; Determine a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array; The fault interval is determined according to the clustering result and the distribution network topology.

5. The single-phase fault interval locating method according to claim 4, characterized in that: The time domain characteristic component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the time domain.

6. The single-phase fault interval locating method according to claim 4, characterized in that: The method further comprises: Performing discrete Fourier transform on the current data of the fault feeder to obtain frequency spectrum data of the fault feeder; Extracting features from the frequency spectrum data of the faulty feeder to obtain a frequency domain feature component sequence array; Determine a clustering result based on the K-means clustering algorithm according to the time domain feature component sequence array and the frequency domain feature component sequence array; The fault interval is determined according to the clustering result and the distribution network topology.

7. The single-phase fault interval locating method according to claim 6, characterized in that: The frequency domain characteristic component sequence array includes the mean value, standard deviation, root mean square, maximum value, minimum value, peak-to-peak value, skewness, kurtosis, peak factor, waveform factor, pulse factor, margin factor and energy in the frequency domain.

8. A single-phase fault interval locating device, characterized in that: The device comprises: A data acquisition module, used to acquire three-phase current data within a fault sampling period; A fault phase selection module is used to determine the fault phase line based on the three-phase current data and grey correlation analysis; A fault line selection module is used to extract the current data of the fault phase line from the three-phase current data, and then determine the fault feeder according to the current data of the fault phase line based on the transient zero-sequence current group ratio; The fault location module is used to extract the current data of the fault feeder from the three-phase current data, and then determine the fault interval according to the current data of the fault feeder based on the K-means clustering algorithm.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the single-phase fault interval locating method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the single-phase fault interval locating method according to any one of claims 1 to 7 is implemented.

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