Distribution Network Fault Detection Method, System, Device, Medium and Product

Abstract

The present invention relates to the technical field of distribution networks, and discloses a distribution network fault detection method, system, device, medium and product. This method obtains the instantaneous positive sequence components and instantaneous negative sequence components of the three-phase currents of multiple preset nodes in the distribution network, uses wavelet multi-scale decomposition for the instantaneous positive sequence components and instantaneous negative sequence components, and determines the energy entropy of the energy of the fault feature signals after wavelet decomposition, thereby effectively separating the fault signals from the noise, making the distinction between minor faults and normal fluctuations clearer, improving the detection sensitivity of minor fault detection in the distribution network, and reducing interference. Through the design of an adaptive threshold, it can adapt to the detection requirements under different operating conditions, and when the energy entropy is greater than the adaptive threshold, further identify the occurrence of minor faults according to the signal difference between the instantaneous positive sequence components and the instantaneous negative sequence components, thereby reducing the false alarm rate of minor faults and improving the accuracy of minor fault detection.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and particularly to a distribution network fault detection method, system, device, medium and product. Background Art

[0002] The distribution network is an important part of the power system, responsible for delivering electric energy from the substation to the user terminal. The distribution network has the characteristics of complex network structure, many line branches, a large number of devices and wide distribution. During actual operation, it is often affected by various internal and external factors and fails. These faults can be divided into two categories: obvious faults and minor faults. Obvious faults such as short circuits and groundings will immediately cause the protection device to act, while minor faults such as partial discharges, insulation aging, and poor contacts do not show obvious initial manifestations, but will gradually deteriorate over time and eventually evolve into serious faults, resulting in power supply interruptions.

[0003] The detection of minor faults is of great significance for preventing major accidents in the distribution network and improving power supply reliability. Minor faults usually show the following characteristics: (1) weak signal strength, with the amplitude usually only 1% - 5% of the normal signal; (2) short signal duration, often showing transient pulses; (3) easily masked by background noise; (4) randomness in the occurrence location and type. These characteristics make the detection of minor faults a major problem in the detection and operation and maintenance of distribution networks.

[0004] In the existing technical solutions for dealing with the detection of minor faults in the distribution network, the detection sensitivity is low, which is prone to missed detection. During the operation of the power system, various noises and interferences (such as switch operations, load fluctuations, etc.) will affect the detection of fault signals, and there is a lack of an effective anti-interference mechanism, resulting in a high false alarm rate in a strong noise environment. Summary of the Invention

[0005] In view of this, the present invention provides a distribution network fault detection method, system, device, medium and product, which solves the technical problems of low detection sensitivity in the detection of minor faults in the distribution network, being prone to missed detection, lacking an effective anti-interference mechanism, and having a high false alarm rate in a strong noise environment.

[0006] The first aspect of the present invention provides a distribution network fault detection method, including:

[0007] Collecting the three-phase currents of multiple preset nodes in the distribution network in real time, and calculating the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents;

[0008] Performing wavelet multi-scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively, and determining the fault feature signal energy corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively according to the decomposed wavelet coefficients;

[0009] Determine the energy entropy of the fault feature signal energy according to the fault feature signal energy;

[0010] Judge whether the energy entropy is greater than a preset adaptive threshold;

[0011] When it is judged that the energy entropy is greater than the preset adaptive threshold, identify the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component.

[0012] Preferably, calculating the instantaneous positive sequence component and the instantaneous negative sequence component of the three-phase current includes:

[0013] Determine the instantaneous values of the three-phase current according to the three-phase current;

[0014] Determine the instantaneous positive sequence component and the instantaneous negative sequence component of the three-phase current according to the instantaneous values of the three-phase current and a preset rotation operator.

[0015] Preferably, performing wavelet multi-scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component respectively, and determining the fault feature signal energy corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component respectively according to the decomposed wavelet coefficients, includes:

[0016] Perform wavelet five-layer scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component respectively through a db4 wavelet function to obtain the wavelet coefficients of the instantaneous positive sequence component and the instantaneous negative sequence component;

[0017] Extract the fault feature signal energy corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component respectively from the wavelet coefficients of the 3rd to 5th layers in the wavelet five-layer scale decomposition.

[0018] Preferably, the determining the energy entropy of the fault feature signal energy according to the fault feature signal energy includes:

[0019] Determine the energy proportion of the instantaneous positive sequence component and the instantaneous negative sequence component in the wavelet decomposition according to the fault feature signal energy and the wavelet coefficients;

[0020] Determine the energy entropy of the fault feature signal energy according to the energy proportion.

[0021] Preferably, when it is judged that the energy entropy is greater than the preset adaptive threshold, identifying the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component includes:

[0022] When it is determined that the energy entropy is greater than the preset adaptive threshold, the phase difference and amplitude ratio of the instantaneous positive sequence component and the instantaneous negative sequence component are determined according to the instantaneous positive sequence component and the instantaneous negative sequence component.

[0023] When the phase difference is greater than the preset phase difference threshold and the amplitude ratio is greater than the preset amplitude threshold, it is determined that a minor fault has occurred at the preset node corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component.

[0024] Preferably, the method further includes:

[0025] Determine the initial adaptive threshold according to the mean value, standard deviation of the energy entropy of the three-phase current when the distribution network is operating normally, and the preset sensitivity coefficient.

[0026] Determine the correction coefficient according to the load power and rated power of the distribution network.

[0027] The initial adaptive threshold is corrected by the correction coefficient to determine the adaptive threshold.

[0028] In a second aspect, the present invention provides a distribution network fault detection system, including:

[0029] A current acquisition module, configured to collect the three-phase currents of multiple preset nodes of the distribution network in real time, and calculate the instantaneous positive sequence component and the instantaneous negative sequence component of the three-phase currents.

[0030] A wavelet decomposition module, configured to perform wavelet multi-scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component respectively, and determine the energy of the fault feature signals corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component respectively according to the wavelet coefficients obtained by the decomposition.

[0031] An energy entropy determination module, configured to determine the energy entropy of the energy of the fault feature signals according to the energy of the fault feature signals.

[0032] An energy entropy judgment module, configured to judge whether the energy entropy is greater than a preset adaptive threshold.

[0033] A fault identification module, configured to, when it is determined that the energy entropy is greater than the preset adaptive threshold, identify the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component.

[0034] In a third aspect, the present invention provides an electronic device, the electronic device includes a memory and a processor, a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the distribution network fault detection method as described in the first aspect.

[0035] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps of the distribution network fault detection method described in the first aspect are realized.

[0036] Fifthly, the present invention provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is made to execute the steps of the distribution network fault detection method described in the first aspect.

[0037] As can be seen from the above technical solutions, the present invention obtains the instantaneous positive-sequence components and instantaneous negative-sequence components of the three-phase currents of multiple preset nodes in the distribution network, adopts wavelet multi-scale decomposition for the instantaneous positive-sequence components and instantaneous negative-sequence components, and determines the energy entropy of the energy of the fault feature signals after wavelet decomposition, thereby effectively separating the fault signals from the noise, making the distinction between minor faults and normal fluctuations clearer, improving the detection sensitivity of minor fault detection in the distribution network, and reducing interference. Through the design of an adaptive threshold, it can adapt to the detection requirements under different operating conditions, and when the energy entropy is greater than the adaptive threshold, then according to the signal difference between the instantaneous positive-sequence component and the instantaneous negative-sequence component, the occurrence of minor faults is identified, thereby reducing the false alarm rate of minor faults and improving the accuracy of minor fault detection. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is an application environment diagram of a distribution network fault detection method provided by an embodiment of the present invention;

[0040] Figure 2 It is a flowchart of a distribution network fault detection method provided by an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of the layout of distribution network monitoring points provided by an embodiment of the present invention;

[0042] Figure 4 It is a schematic diagram of instantaneous symmetrical component calculation provided by an embodiment of the present invention;

[0043] Figure 5 It is a schematic diagram of wavelet multi-scale decomposition provided by an embodiment of the present invention;

[0044] Figure 6 Schematic structural diagram of a distribution network fault detection system provided by an embodiment of the present invention;

[0045] Figure 7 Schematic structural diagram of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0046] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] The existing technical solutions mainly have the following disadvantages in dealing with the detection of tiny faults in the distribution network:

[0048] Low detection sensitivity: The existing methods based on the traditional instantaneous symmetrical component method are mainly designed for obvious faults. For tiny faults with signal amplitudes only 1%-5% of the normal signal, the detection sensitivity is insufficient, and it is easy to cause missed detections.

[0049] Weak anti-interference ability: During the operation of the power system, various noises and interferences (such as switch operations, load fluctuations, etc.) will affect the detection of fault signals. The existing methods lack effective anti-interference mechanisms, and the false alarm rate is high in a strong noise environment.

[0050] Inflexible threshold setting: Traditional methods usually use fixed thresholds for fault judgment, and cannot adapt to the changes under different operating conditions of the distribution network, resulting in a high false alarm rate in scenarios with large load changes.

[0051] Insufficient signal feature extraction: Existing methods mostly use simple time-domain or frequency-domain analysis, and fail to fully extract the feature information of tiny faults, making it difficult to distinguish tiny faults from normal fluctuations.

[0052] Poor system stability: Existing methods are often sensitive to system parameters, and improper parameter settings are likely to lead to unstable detection results, making it difficult to operate reliably in practical engineering for a long time.

[0053] Therefore, the distribution network fault detection method provided by the embodiments of the present application can be applied to such as Figure 1In the application environment shown. Among them, the terminal 101 communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or can be placed on the cloud or other network servers. The terminal 101 or the server 102 collects the three-phase currents of multiple preset nodes of the distribution network in real time, and calculates the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents; performs wavelet multi-scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively, and determines the fault characteristic signal energy corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively according to the wavelet coefficients obtained by the decomposition; determines the energy entropy of the fault characteristic signal energy according to the fault characteristic signal energy; determines whether the energy entropy is greater than a preset adaptive threshold; when it is determined that the energy entropy is greater than the preset adaptive threshold, the occurrence of small faults at multiple preset nodes is identified according to the signal difference between the instantaneous positive-sequence component and the instantaneous negative-sequence component.

[0054] The terminal 101 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, etc.

[0055] The server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0056] As Figure 2 shown, an embodiment of the present application provides a distribution network fault detection method. Taking the method applied to Figure 1 the terminal 101 or the server 102 in it as an example, the method includes the following steps S1 to S5. Among them:

[0057] Step S1: Collect the three-phase currents of multiple preset nodes of the distribution network in real time, and calculate the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents.

[0058] Among them, the preset nodes can select key nodes of the distribution network, such as substation outgoing lines, line branch points, and key load connection points, etc., and high-precision current transformers (CTs) are installed at the preset nodes to perform real-time monitoring of the three-phase currents at a high sampling rate. As Figure 3 shown in the layout of the distribution network monitoring points.

[0059] Among them, the sampling rate is required to be at least 10 kHz to ensure that high-frequency transient signals can be captured. The collected data includes the instantaneous values ia(t), ib(t), ic(t) of the three-phase currents. The data collection device transmits the collected data to the data processing center through the communication network for analysis.

[0060] The instantaneous positive sequence component and the instantaneous negative sequence component respectively reflect the changes in the positive sequence and negative sequence currents of the system. In the distribution network fault detection system of the present invention, the instantaneous positive sequence component and the instantaneous negative sequence component of the three-phase current are calculated by adopting an improved instantaneous symmetrical component method.

[0061] Step S2: Perform wavelet multi-scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component respectively, and determine the fault characteristic signal energy corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component according to the wavelet coefficients obtained by the decomposition.

[0062] Among them, wavelet multi-scale decomposition can decompose the signal into different frequency scales, so as to extract the fault characteristics hidden in the complex signal. In the distribution network fault detection system of the present invention, the db4 wavelet function is selected to perform five-layer wavelet scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component. The db4 wavelet function has compact support and orthogonality, which can effectively remove the redundant information in the signal and improve the accuracy of fault characteristic extraction. Through wavelet decomposition, the wavelet coefficients of the instantaneous positive sequence component and the instantaneous negative sequence component at each scale can be obtained, and these coefficients reflect the energy distribution of the signal at different frequencies. Further, the fault characteristic signal energy is extracted from the wavelet coefficients of the 3rd to 5th layers in the five-layer wavelet scale decomposition, and these energy values can reflect the changes in the high-frequency band of the fault signal.

[0063] Step S3: Determine the energy entropy of the fault characteristic signal energy according to the fault characteristic signal energy.

[0064] Among them, energy entropy is an index to measure the complexity of signal energy distribution, which can reflect the disorder degree of fault characteristic signal energy. In the distribution network fault detection system of the present invention, first, according to the fault characteristic signal energy and wavelet coefficients, the energy proportion of the instantaneous positive sequence component and the instantaneous negative sequence component in the wavelet decomposition is determined. The energy proportion reflects the relative importance of different frequency components in the signal. Then, the energy entropy of the fault characteristic signal energy is calculated according to the energy proportion. The larger the energy entropy, the more complex the distribution of the fault characteristic signal energy, which may mean that there are minor faults. By calculating the energy entropy, the uncertainty and complexity of the fault characteristic signal can be quantified.

[0065] Step S4: Judge whether the energy entropy is greater than a preset adaptive threshold.

[0066] Among them, traditional fixed thresholds are prone to false alarms or missed alarms in the case of large load fluctuations. The present invention designs a non-fixed adaptive threshold, which can be dynamically adjusted according to the system operation state.

[0067] Step S5: When it is judged that the energy entropy is greater than the preset adaptive threshold, the occurrence of minor faults at multiple preset nodes is identified according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component.

[0068] When it is determined that the energy entropy is not greater than a preset adaptive threshold, no further operation is required.

[0069] It should be noted that in this application, by obtaining the instantaneous positive sequence components and instantaneous negative sequence components of the three-phase currents of multiple preset nodes in the distribution network, using wavelet multi-scale decomposition for the instantaneous positive sequence components and instantaneous negative sequence components, and determining the energy entropy of the energy of the fault feature signals after wavelet decomposition, the fault signals and noises can be effectively separated, making the distinction between minor faults and normal fluctuations clearer, improving the detection sensitivity of minor fault detection in the distribution network, and reducing interference. Through the design of the adaptive threshold, the detection requirements under different operating conditions can be met. When the energy entropy is greater than the adaptive threshold, the occurrence of minor faults can be identified based on the signal difference between the instantaneous positive sequence components and the instantaneous negative sequence components, thereby reducing the false alarm rate of minor faults and improving the accuracy of minor fault detection.

[0070] In some embodiments, the present invention uses an improved instantaneous symmetrical component method to calculate the instantaneous positive sequence components and instantaneous negative sequence components of the three-phase currents. As Figure 4 shown, the traditional symmetrical component transformation requires phasor information, while the real-time monitoring obtains instantaneous values. Therefore, an improved method is used to construct a rotating vector to achieve the calculation of the instantaneous symmetrical components.

[0071] Specifically, calculating the instantaneous positive sequence components and instantaneous negative sequence components of the three-phase currents includes:

[0072] Step S101: Determine the instantaneous values of the three-phase currents based on the three-phase currents.

[0073] Among them, the instantaneous values of the three-phase currents at time t are:

[0074]

[0075] In the formula, i a (t), i b (t), i c (t) are the instantaneous values of the three-phase currents at time t respectively; I am , I bm , I cm are the amplitudes of the three-phase currents respectively; φ a , φ b , φ c are the initial phases of the three-phase currents respectively, is the angular frequency.

[0076] Through operations on the instantaneous values of the three-phase currents at time t, the instantaneous values of the three-phase currents are obtained as:

[0077]

[0078] wherein, i a , i b , i c are respectively the instantaneous values of three-phase currents, and j is the unit of the imaginary part.

[0079] Step S102: Determine the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase current according to the instantaneous values of the three-phase current and a preset rotation operator.

[0080] Among them, the calculation of the instantaneous positive-sequence component and the instantaneous negative-sequence component is as follows:

[0081]

[0082]

[0083] wherein, i a(1) , i b(1) , i c(1) are respectively the instantaneous positive-sequence components of the three-phase current; i a(2) , i b(2) , i c(2) are respectively the instantaneous negative-sequence components of the three-phase current; a is the rotation operator, a = e (j*2π / 3) .

[0084] In some embodiments, wavelet multi-scale decomposition is respectively performed on the instantaneous positive-sequence component and the instantaneous negative-sequence component, and the fault feature signal energies corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component are determined according to the decomposed wavelet coefficients, including:

[0085] Step S201: Perform wavelet five-layer scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively through a db4 wavelet function to obtain the wavelet coefficients of the instantaneous positive-sequence component and the instantaneous negative-sequence component.

[0086] Among them, wavelet multi-scale decomposition is performed on the calculated instantaneous positive-sequence current and instantaneous negative-sequence current to extract minute fault features. In the present invention, the db4 wavelet function is selected for 5-layer decomposition because the db4 wavelet has good time-frequency localization characteristics and is suitable for analyzing non-stationary signals in a power system.

[0087] As Figure 5 shown, the wavelet decomposition process is as follows:

[0088] Perform wavelet transform on the instantaneous positive-sequence current and the instantaneous negative-sequence current respectively:

[0089]

[0090] wherein, W j,k (i a (1)) and W j,k (i a(2) are the wavelet coefficients of the instantaneous positive-sequence current and the instantaneous negative-sequence current respectively; j is the decomposition scale; k is the time translation parameter; ψ() is the wavelet mother function.

[0091] Step S202: Extract the fault feature signal energies corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively from the wavelet coefficients of the 3rd to 5th layers in the five-layer wavelet decomposition.

[0092] Among them, extracting the micro fault feature signal energy from the wavelet coefficients of the 3rd to 5th layers, we get:

[0093]

[0094] In the formula, F(1) and F(2) are the fault feature signal energies of the instantaneous positive-sequence current and the instantaneous negative-sequence current respectively.

[0095] The reason for selecting the 3rd to 5th layer coefficients is that through a large number of experimental verifications, the main characteristic frequencies of the micro fault signals are generally distributed between 500 Hz and 4 kHz, corresponding to the 3rd to 5th layers of the five-layer wavelet decomposition.

[0096] In other embodiments, other wavelet functions suitable for power signal analysis can also be used, such as sym8, coif5, etc. Different wavelet functions have different time-frequency characteristics, and the most suitable wavelet function can be selected according to the specific application scenario. Before wavelet decomposition, signal preprocessing steps can be added, such as using empirical mode decomposition, variational mode decomposition and other methods to preprocess the signal to further improve the effect of feature extraction.

[0097] In some embodiments, according to the fault feature signal energy, determining the energy entropy of the fault feature signal energy includes:

[0098] Step S301: Determine the energy proportion of the instantaneous positive-sequence component and the instantaneous negative-sequence component in the wavelet decomposition according to the fault feature signal energy and the wavelet coefficients.

[0099] Step S302: Determine the energy entropy of the fault feature signal energy according to the energy proportion.

[0100] Among them, the energy entropy is an index describing the uniformity of the signal energy distribution. When a fault occurs in the system, the energy distribution will change, and the energy entropy value will also change accordingly. Under normal operating conditions, the signal energy distribution is uniform and the energy entropy value is relatively large; while in the fault state, the energy is concentrated in certain frequency bands and the energy entropy value decreases.

[0101] The energy entropy of the fault feature signal calculated by the present invention is:

[0102]

[0103] Among them,

[0104] Wherein, pj and qj are respectively the energy proportion of the instantaneous positive-sequence current and the instantaneous negative-sequence current in the j-th layer of wavelet decomposition.

[0105] In some embodiments, when it is determined that the energy entropy is greater than a preset adaptive threshold, the occurrence of minor faults at multiple preset nodes is identified according to the signal difference between the instantaneous positive-sequence component and the instantaneous negative-sequence component, including:

[0106] Step S501: When it is determined that the energy entropy is greater than a preset adaptive threshold, the phase difference and amplitude ratio between the instantaneous positive-sequence component and the instantaneous negative-sequence component are determined according to the instantaneous positive-sequence component and the instantaneous negative-sequence component.

[0107] Among them, by analyzing the phase relationship and amplitude change characteristics of the instantaneous positive and negative sequence currents, the phase difference and amplitude ratio between the instantaneous positive-sequence component and the instantaneous negative-sequence component are:

[0108]

[0109]

[0110] Wherein, φ(1) and φ(2) are respectively the phases of the instantaneous positive-sequence current and the instantaneous negative-sequence current; A(1) and A(2) are respectively the amplitudes of the instantaneous positive-sequence current and the instantaneous negative-sequence current, is the phase difference, is the amplitude ratio.

[0111] Step S502: When the phase difference is greater than a preset phase difference threshold and the amplitude ratio is greater than a preset amplitude threshold, it is determined that a minor fault has occurred at the preset node corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component.

[0112] Among them, the phase difference threshold and the amplitude threshold are usually fixed values. For example, the amplitude threshold is set according to the actual system, generally taking 0.01 - 0.05.

[0113] In some embodiments, this method further includes:

[0114] Step S11: Determine the initial adaptive threshold according to the mean value, standard deviation of the energy entropy of the three-phase current during the normal operation of the distribution network and a preset sensitivity coefficient;

[0115] Step S12: Determine the correction coefficient according to the load power and rated power of the distribution network;

[0116] Step S13: Correct the initial adaptive threshold through the correction coefficient to determine the adaptive threshold.

[0117] It is understandable that traditional fixed thresholds are prone to false alarms or missed alarms in the case of large load fluctuations. The present invention designs an adaptive threshold that can be dynamically adjusted according to the system operating state.

[0118] The calculation formula for the adaptive threshold is:

[0119]

[0120] In the formula, is the adaptive threshold, E avg is the mean value of the energy entropy of the distribution network during normal operation; σ is the standard deviation of the energy entropy; K is the sensitivity coefficient, and its value range is [2.0, 3.5], which is adjusted according to the importance of the system and the requirement for the false alarm rate; μ is the correction coefficient, which is dynamically adjusted according to the system load rate:

[0121]

[0122] In the formula, 、 are the load power and rated power of the distribution network respectively.

[0123] In other embodiments, the adaptive threshold based on the load rate can be replaced by an adaptive threshold based on statistical learning, such as calculating the statistical characteristics of historical data using a sliding window, or automatically adjusting the threshold parameters using machine learning methods.

[0124] In summary, by introducing wavelet transform and energy entropy analysis, the present invention can detect tiny faults with signal amplitudes as low as 2% of the normal value, while the traditional instantaneous symmetrical component method usually requires the signal amplitude to reach more than 5% of the normal value for reliable detection. In actual tests, for tiny discharges caused by aging of line insulation, the detection rate of the present invention reaches 95%, while the traditional method is only 65%. By using wavelet multi-scale decomposition and energy entropy analysis, the fault signal and noise are effectively separated, and the detection accuracy rate can still be maintained above 95% in a harsh environment with a signal-to-noise ratio of 3dB, while the accuracy rate of the traditional method is only about 60% under the same conditions. Through the design of the adaptive threshold, the present invention can dynamically adjust the detection parameters according to the system load rate to meet the detection requirements under different operating conditions. In the scenario where the load changes by 50%, the false alarm rate is controlled within 3%, while the false alarm rate of the traditional fixed threshold method is as high as 12%. By using wavelet multi-scale decomposition and energy entropy analysis, the present invention can more fully extract the characteristic information of tiny faults, and the characteristic discrimination degree is increased by about 40%, making the distinction between tiny faults and normal fluctuations clearer.

[0125] Based on the same inventive concept, the embodiments of the present application also provide a distribution network fault detection system for implementing the above-mentioned distribution network fault detection method.

[0126] The solution provided by this system for problem-solving is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the distribution network fault detection system provided below can refer to the limitations on the distribution network fault detection method in the above text, and will not be elaborated here.

[0127] As Figure 6 shown, an embodiment of the present application provides a distribution network fault detection system, including:

[0128] A current acquisition module 100, configured to collect the three-phase currents of multiple preset nodes in the distribution network in real time, and calculate the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents;

[0129] A wavelet decomposition module 200, configured to perform wavelet multi-scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively, and determine the energy of the fault characteristic signals corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively according to the wavelet coefficients obtained by the decomposition;

[0130] An energy entropy determination module 300, configured to determine the energy entropy of the energy of the fault characteristic signals according to the energy of the fault characteristic signals;

[0131] An energy entropy judgment module 400, configured to judge whether the energy entropy is greater than a preset adaptive threshold;

[0132] A fault identification module 500, configured to, when it is judged that the energy entropy is greater than the preset adaptive threshold, identify the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive-sequence component and the instantaneous negative-sequence component.

[0133] In some embodiments, the current acquisition module 100 is configured to:

[0134] Determine the instantaneous values of the three-phase currents according to the three-phase currents;

[0135] Determine the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents according to the instantaneous values of the three-phase currents and a preset rotation operator.

[0136] In some embodiments, the wavelet decomposition module 200 is configured to:

[0137] Perform wavelet five-layer scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively through a db4 wavelet function to obtain the wavelet coefficients of the instantaneous positive-sequence component and the instantaneous negative-sequence component;

[0138] Extract the energy of the fault characteristic signals corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively from the wavelet coefficients of the 3rd to 5th layers in the wavelet five-layer scale decomposition.

[0139] In some embodiments, the energy entropy determination module 300 is configured to:

[0140] Determine the energy proportion of the instantaneous positive sequence component and the instantaneous negative sequence component in the wavelet decomposition according to the energy of the fault feature signal and the wavelet coefficients;

[0141] Determine the energy entropy of the energy of the fault feature signal according to the energy proportion.

[0142] In some embodiments, the fault identification module 500 is configured to:

[0143] When it is determined that the energy entropy is greater than a preset adaptive threshold, determine the phase difference and amplitude ratio of the instantaneous positive sequence component and the instantaneous negative sequence component according to the instantaneous positive sequence component and the instantaneous negative sequence component;

[0144] In the case where the phase difference is greater than a preset phase difference threshold and the amplitude ratio is greater than a preset amplitude threshold, it is determined that a minor fault has occurred at the preset nodes corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component.

[0145] In some embodiments, the system further includes: an adaptive threshold determination module, configured to:

[0146] Determine an initial adaptive threshold according to the mean value, standard deviation of the energy entropy of the three-phase current during normal operation of the distribution network and a preset sensitivity coefficient;

[0147] Determine a correction coefficient according to the load power and rated power of the distribution network;

[0148] Correct the initial adaptive threshold through the correction coefficient to determine the adaptive threshold.

[0149] As Figure 7 shown, an embodiment of the present application provides an electronic device. The electronic device 10 includes a memory 20 and a processor 30. A computer program is stored in the memory 20. When the computer program is executed by the processor 30, the processor 30 is caused to execute the steps of the distribution network fault detection method in the above embodiment.

[0150] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the steps of the distribution network fault detection method in the above embodiment are implemented.

[0151] An embodiment of the present application provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the steps of the distribution network fault detection method described in the above embodiment.

[0152] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, electronic devices, computer storage media, and computer program products described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0153] It should be noted that the terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0154] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the indications of the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0155] In several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, 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 displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces, and the indirect coupling or communication connection of devices or units can be in electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] In addition, in each embodiment of the present invention, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0158] If the above-mentioned 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 such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks, or optical discs.

[0159] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present invention.

Claims

1. A distribution network fault detection method, characterized in that, Including: Real-time collect the three-phase currents of multiple preset nodes in the distribution network, and calculate the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents; Perform wavelet multi-scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively, and determine the fault characteristic signal energy corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively according to the decomposed wavelet coefficients; Determine the energy entropy of the fault characteristic signal energy according to the fault characteristic signal energy; Judge whether the energy entropy is greater than a preset adaptive threshold; When it is judged that the energy entropy is greater than the preset adaptive threshold, identify the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive-sequence component and the instantaneous negative-sequence component, including: When it is judged that the energy entropy is greater than the preset adaptive threshold, determine the phase difference and amplitude ratio between the instantaneous positive-sequence component and the instantaneous negative-sequence component according to the instantaneous positive-sequence component and the instantaneous negative-sequence component; In the case where the phase difference is greater than a preset phase difference threshold and the amplitude ratio is greater than a preset amplitude threshold, it is determined that a minor fault has occurred at the preset node corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component.

2. The power distribution network fault detection method according to claim 1, wherein Calculating the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase current includes: Determine the instantaneous values of the three-phase current according to the three-phase current; Determine the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase current according to the instantaneous values of the three-phase current and a preset rotation operator.

3. The distribution network fault detection method according to claim 1, wherein, The performing wavelet multi-scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively, and determining the fault characteristic signal energy corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively according to the decomposed wavelet coefficients includes: Perform wavelet five-layer scale decomposition on the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively through the db4 wavelet function to obtain the wavelet coefficients of the instantaneous positive-sequence component and the instantaneous negative-sequence component; Extract the fault characteristic signal energy corresponding to the instantaneous positive-sequence component and the instantaneous negative-sequence component respectively from the wavelet coefficients of the 3rd to 5th layers in the wavelet five-layer scale decomposition.

4. The method for detecting faults in a distribution network according to claim 1, wherein, The determining the energy entropy of the fault characteristic signal energy according to the fault characteristic signal energy includes: Determine the energy proportion of the instantaneous positive-sequence component and the instantaneous negative-sequence component in the wavelet decomposition according to the fault characteristic signal energy and the wavelet coefficients; Determine the energy entropy of the fault characteristic signal energy according to the energy proportion.

5. The distribution network fault detection method according to any one of claims 1 to 4, characterized in that Also including: Determine the initial adaptive threshold according to the mean value, standard deviation of the energy entropy of the three-phase current when the distribution network is operating normally and a preset sensitivity coefficient; Determine the correction coefficient according to the load power and rated power of the distribution network; Correct the initial adaptive threshold through the correction coefficient to determine the adaptive threshold.

6. A distribution network fault detection system, characterized in that, Including: A current acquisition module for real-time collecting the three-phase currents of multiple preset nodes in the distribution network and calculating the instantaneous positive-sequence component and the instantaneous negative-sequence component of the three-phase currents; A wavelet decomposition module, which is used to perform wavelet multi-scale decomposition on the instantaneous positive sequence component and the instantaneous negative sequence component respectively, and determine the energy of the fault feature signals corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component respectively according to the wavelet coefficients obtained by the decomposition; An energy entropy determination module, which is used to determine the energy entropy of the energy of the fault feature signals according to the energy of the fault feature signals; An energy entropy judgment module, which is used to judge whether the energy entropy is greater than a preset adaptive threshold; A fault identification module, which is used to, when it is judged that the energy entropy is greater than the preset adaptive threshold, identify the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component; When it is judged that the energy entropy is greater than the preset adaptive threshold, identifying the occurrence of minor faults at multiple preset nodes according to the signal difference between the instantaneous positive sequence component and the instantaneous negative sequence component includes: When it is judged that the energy entropy is greater than the preset adaptive threshold, determining the phase difference and amplitude ratio between the instantaneous positive sequence component and the instantaneous negative sequence component according to the instantaneous positive sequence component and the instantaneous negative sequence component; In the case where the phase difference is greater than a preset phase difference threshold and the amplitude ratio is greater than a preset amplitude threshold, it is determined that a minor fault has occurred at the preset node corresponding to the instantaneous positive sequence component and the instantaneous negative sequence component.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor. When a computer program stored in the memory is executed by the processor, the processor executes the steps of the distribution network fault detection method according to any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, the steps of the distribution network fault detection method according to any one of claims 1-5 are implemented.

9. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the distribution network fault detection method according to any one of claims 1-5.

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