A distribution network fault detection and positioning measurement and control device and method

Abstract

The application discloses a kind of network fault detection and positioning measurement and control device and method, the device includes power module, acquisition module, wavelet energy spectrum transformation module, anti-series winding current transformer, short-circuit fault judgment and fault type judgment module, fault accurate positioning module, arc fault judgment module, summary module and communication module.Acquisition module acquires line current and sends into wavelet energy spectrum transformation module to extract wavelet energy spectrum value;Anti-series winding current transformer is single-winding positive and negative winding structure and parallel inductance, no output under power frequency, only respond to fault high-frequency harmonic;Short-circuit fault judgment combines wavelet energy spectrum threshold value and transformer output signal double confirmation;Fault accurate positioning module calculates fault accurate position by GRNN neural network;Arc fault judgment module judges according to transformer output signal.The application integrates short-circuit fault rapid detection, accurate positioning and arc fault identification, improves the reliability and positioning accuracy of microgrid fault detection.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network automation technology, specifically to a distribution network fault detection and location measurement and control device and method, applicable to short-circuit fault detection, precise location and arc fault identification in distributed microgrids. Background Technology

[0002] With the large-scale grid connection of new energy power generation and the widespread integration of distributed generation, microgrids have become an important means to improve the overall efficiency of energy utilization. However, the integration of distributed generation has brought many new technical challenges to the operation and protection of distribution networks. On the one hand, the integration of distributed generation changes the direction of short-circuit current from the traditional unidirectional to bidirectional. The connection and disconnection of distributed generation with different capacities may change the power flow direction, and traditional fault detection methods are difficult to adapt to this change. On the other hand, if a short-circuit fault occurs in a microgrid line and is not cleared quickly, the distributed generation will cause stability problems, seriously affecting the safe operation of the power grid.

[0003] In existing technologies, Distributed Terminal Units (DTUs) mainly implement communication and basic data acquisition functions, lacking advanced functions such as rapid detection and accurate location of line short-circuit faults, and arc fault identification. Regarding short-circuit fault location, existing research has applied wavelet energy spectrum algorithms to the early detection and location of short-circuit faults in microgrids, but this approach has limitations such as insufficient location accuracy, susceptibility to interference, and inability to adapt to changes in microgrid capacity. In arc fault detection, existing schemes often employ multivariate criteria, which are easily affected by various load interferences (especially high-frequency interference such as fluorescent lamp startup), leading to false alarms.

[0004] In addition, existing current transformers are mainly used for measuring power frequency current, and cannot effectively capture the high-frequency harmonic characteristic signals generated by short-circuit faults and arc faults, making it difficult to meet the needs of new microgrid measurement and control devices for high-frequency fault characteristic detection.

[0005] Therefore, there is an urgent need for a distribution network fault detection and location control device that integrates rapid short-circuit fault detection, precise location and arc fault identification to solve the problems existing in the above-mentioned prior art. Summary of the Invention

[0006] This invention addresses the limitations of existing distributed terminals, which can only perform communication and basic data acquisition, as well as the technical problems of insufficient short-circuit fault location accuracy and susceptibility to interference in arc fault detection. It provides a distribution network fault detection, location, and control device and method. This invention integrates rapid short-circuit fault detection, precise location, and arc fault identification, enabling accurate identification of arc faults and effectively distinguishing various types of load interference, thereby improving the reliability and location accuracy of microgrid fault detection.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a power distribution network fault detection and location monitoring and control device, comprising:

[0008] The power module is used to provide operating power to the entire device;

[0009] The data acquisition module is used to acquire the three-phase voltage, three-phase current, and zero-sequence current of the line.

[0010] The wavelet energy spectrum transformation module is connected to the acquisition module and is used to perform binary wavelet energy spectrum transformation on the acquired current signal to extract the wavelet energy spectrum value.

[0011] An anti-series winding current transformer, located at the same position as the acquisition module, is used to sense high-frequency harmonic components generated by faults in the line. The anti-series winding current transformer adopts a single-wire eccentric loop design, with a single winding that is wound in one direction first and then in the opposite direction for the other half. An inductor is connected in parallel at the output terminal. Under power frequency current, the outputs of the anti-series winding current transformer cancel each other out and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs.

[0012] The short-circuit fault detection and fault type detection module is connected to the wavelet energy spectrum transformation module and the anti-series winding current transformer, respectively. It is used to compare the wavelet energy spectrum value with the set short-circuit detection threshold and determine whether a short-circuit fault has occurred by combining the output signal of the anti-series winding current transformer. Compared with the existing technology, it has higher reliability. The specific fault type is determined according to the wavelet energy spectrum value and the zero-sequence current or residual current.

[0013] The precise positioning module is connected to the short-circuit fault judgment and fault type judgment module. It is used to calculate the precise location of the fault through the GRNN neural network based on the fault type and wavelet energy spectrum value output by the short-circuit fault judgment and fault type judgment module.

[0014] The arc fault detection module is connected to the anti-series winding current transformer and is used to determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer.

[0015] The aggregation module is connected to the precise positioning module and the arc fault judgment module respectively, and is used to aggregate fault information, including short circuit fault type and location information, and arc fault information.

[0016] The communication module, connected to the aggregation module, is used to transmit the fault information aggregated by the aggregation module to the host computer.

[0017] Furthermore, the inductor connected in parallel at the output terminal of the anti-series winding current transformer is a 33μH inductor, which is used to adjust the resonant frequency of the transformer to 100kHz and broaden the high-frequency response bandwidth.

[0018] Furthermore, in the short-circuit fault judgment and fault type judgment module, when the wavelet energy spectrum value E4 exceeds the short-circuit judgment threshold X... d Furthermore, when the anti-series winding current transformer outputs a signal, it is determined that a short-circuit fault has occurred; the short-circuit judgment threshold X d It is more than 5 times the E4 value during normal operation.

[0019] Furthermore, in the short-circuit fault judgment and fault type judgment module, for a three-phase three-wire system, the fault type is determined by detecting the zero-sequence current; for a three-phase four-wire system, the ground fault and non-ground fault are distinguished by detecting the residual current.

[0020] Furthermore, the precise positioning module uses the initial phase angle θ of the fault. c The maximum value of wavelet energy spectrum E4 is E4. max And the sum of the power generated by distributed power sources in the microgrid, P dg Using the distance d from the short-circuit point to the detection point as the input variable and the distance d from the short-circuit point to the detection point as the objective function, a precise localization formula is obtained through training a GRNN neural network; when a fault occurs, the P detected by the detection point... dg θ c E4 max Substituting the values ​​into the precise location formula, the exact location of the fault can be calculated.

[0021] Furthermore, the training samples of the GRNN neural network are obtained by testing different initial phase angles θ for different faults. c Different fault distances d and different distributed power sources P dg E4 below max The values ​​are obtained by sampling, and the smoothing factor of the GRNN neural network is 1.18.

[0022] Furthermore, in the arc fault judgment module, the number of data points greater than 75mV in each half-cycle of power frequency during an arc fault is recorded as Num1, and the number of half-cycles with Num1 greater than or equal to 1 per second is recorded as Num2. When Num2 is greater than a preset threshold, an arc fault is determined to have occurred.

[0023] The positive numbers in each half-cycle of the power frequency are accumulated to obtain the characteristic value S. When S is greater than the preset threshold, it is determined that the fluorescent lamp is started, and the judgment of arc fault is stopped within the next second.

[0024] Furthermore, the preset threshold of Num2 is 10; the preset threshold of the feature value S is 10.

[0025] This invention also provides a method for detecting, locating, and controlling distribution network faults, implemented based on the above-mentioned device, comprising the following steps:

[0026] Collect the three-phase voltage, three-phase current, and zero-sequence current of the line;

[0027] The acquired current signal is subjected to binary wavelet energy spectrum transformation to extract wavelet energy spectrum values;

[0028] The anti-series winding current transformer senses the high-frequency harmonic components generated by a fault in the line at the same location. The anti-series winding current transformer adopts a single-wire eccentric loop design. Its winding is a single winding, with half wound in one direction and the other half wound in the opposite direction. An inductor is connected in parallel at the output terminal. The outputs of the anti-series winding current transformer cancel each other out under power frequency current and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs.

[0029] The wavelet energy spectrum value is compared with the set short-circuit judgment threshold, and the presence or absence of an output signal from the anti-series winding current transformer is used to determine whether a short-circuit fault has occurred. If a short-circuit fault is determined, the specific fault type is determined based on the wavelet energy spectrum value and the zero-sequence current or residual current.

[0030] The precise location of the fault is calculated using a GRNN neural network based on the fault type and wavelet energy spectrum value.

[0031] Determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer;

[0032] The system summarizes the types and locations of short-circuit faults and arc fault information, and transmits them to the host computer.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. Innovative Design of Anti-Series Winding Current Transformer: This invention employs a single-wire eccentric loop design, eliminating the need for a double-wire loop on the primary side, thus improving installation convenience. The single winding's forward-winding followed by reverse-winding structure causes the outputs to cancel each other out under power frequency current, responding only to high-frequency harmonic components generated by short circuits and arc faults. Combined with an optimized parallel inductor scheme, it significantly broadens the high-frequency signal response bandwidth, enhances the ability to capture high-frequency characteristics of arc faults, and is applicable to both AC and DC systems.

[0035] 2. Dual Confirmation and Precise Location of Short-Circuit Faults: This invention combines wavelet energy spectrum threshold judgment with the output signal of the anti-series winding current transformer. A short-circuit fault is only determined when both conditions are met simultaneously, significantly reducing the false alarm rate and improving the reliability of short-circuit fault detection. Furthermore, this invention uses the initial phase angle of the fault, the maximum value of the wavelet energy spectrum, and the sum of the distributed power sources as input variables. Through machine learning training, a precise location formula is obtained, enabling precise location of the short-circuit fault point (calculating the precise distance from the fault point to the detection point), rather than the area-based location methods used in existing technologies.

[0036] 3. Accurate identification of arc faults: This invention utilizes the characteristic of anti-series winding current transformers that only respond to high-frequency signals. Combining the dual criteria of half-cycle cumulative counting and characteristic value S, a reasonable judgment threshold has been determined through a large number of experiments. It can effectively distinguish between arc faults and various load interferences. In particular, it can accurately distinguish between fluorescent lamp starting and arc faults through characteristic value S, completely avoiding false alarms caused by such high-frequency interference.

[0037] 4. Integrated Design: This invention combines short-circuit fault precise location and arc fault accurate identification technologies to design a multi-functional DTU device, which realizes the integrated functions of data acquisition, fault diagnosis and data transmission for distributed active distribution networks based on the existing DTU architecture. Attached Figure Description

[0038] Figure 1 This is a structural block diagram of the power distribution network fault detection and location monitoring and control device provided in an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of a microgrid simulation model in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of the anti-series winding current transformer in an embodiment of the present invention;

[0041] Figure 4 This is a physical diagram of the anti-series winding current transformer (100 turns) in an embodiment of the present invention;

[0042] Figure 5 This is a test result diagram of the resonant platform of the anti-series winding current transformer in an embodiment of the present invention;

[0043] Figure 6 This is a flowchart of the arc fault judgment process in an embodiment of the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0046] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] like Figure 1 As shown, the present invention provides a power distribution network fault detection and location monitoring and control device, comprising:

[0048] The power module is used to provide operating power to the entire device;

[0049] The data acquisition module is used to acquire the three-phase voltage, three-phase current, and zero-sequence current of the line.

[0050] The wavelet energy spectrum transformation module is connected to the acquisition module and is used to perform binary wavelet energy spectrum transformation on the acquired current signal to extract the wavelet energy spectrum value.

[0051] An anti-series winding current transformer, located at the same position as the acquisition module, is used to sense high-frequency harmonic components generated by faults in the line. The anti-series winding current transformer adopts a single-wire eccentric loop design, with a single winding that is wound in one direction first and then in the opposite direction for the other half. An inductor is connected in parallel at the output terminal. Under power frequency current, the outputs of the anti-series winding current transformer cancel each other out and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs.

[0052] The short-circuit fault detection and fault type detection module is connected to the wavelet energy spectrum transformation module and the anti-series winding current transformer, respectively. It is used to compare the wavelet energy spectrum value with the set short-circuit detection threshold, and combine the presence or absence of an output signal from the anti-series winding current transformer to determine whether a short-circuit fault has occurred. It also determines the specific fault type based on the wavelet energy spectrum value and the zero-sequence current or residual current. Compared with the existing technology, it has higher reliability.

[0053] The precise positioning module is connected to the short-circuit fault judgment and fault type judgment module. It is used to calculate the precise location of the fault through the GRNN neural network based on the fault type and wavelet energy spectrum value output by the short-circuit fault judgment and fault type judgment module.

[0054] The arc fault detection module is connected to the anti-series winding current transformer and is used to determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer.

[0055] The aggregation module is connected to the precise positioning module and the arc fault judgment module respectively, and is used to aggregate fault information, including short circuit fault type and location information, and arc fault information.

[0056] The communication module, connected to the aggregation module, is used to transmit the fault information aggregated by the aggregation module to the host computer.

[0057] The proposed distribution network fault detection and location control device scheme, based on the existing DTU architecture, adds short-circuit fault judgment and precise location functions, as well as arc fault identification functions. The system employs the existing binary wavelet energy spectrum transform algorithm to extract features from the line current collected by traditional current transformers, obtaining its wavelet energy spectrum value E4. A GRNN neural network is then used to train and derive the precise location formula for short-circuit faults, thus achieving rapid detection and precise location of short-circuit faults. Through the design of the anti-series winding current transformer, installation convenience is improved by eliminating the need for a double-conductor loop on the primary side and using only a single conductor with an eccentric loop. Simultaneously, low-frequency interference is reduced through secondary-side self-compensation, enabling accurate capture of high-frequency characteristics of arc faults, thereby achieving arc fault identification. Furthermore, it can effectively verify various arcing tests and false alarm tests under GB14287.4.

[0058] The structure of the device is as follows Figure 1 As shown, the power module provides a stable power supply for the entire device, supports the operation of two parallel functional branches, and finally transmits the signal through the communication module.

[0059] The first branch line is for short-circuit fault detection and location: First, the acquisition module uses traditional acquisition methods to collect the three-phase voltage, three-phase current, and zero-sequence current of the line. The acquired current signal is then transmitted to the wavelet energy spectrum transformation module, which analyzes and extracts the wavelet energy spectrum value. This value is then transmitted to the short-circuit fault and fault type judgment module. Based on the comparison between the wavelet energy spectrum value of the E4 current transformer and the set short-circuit judgment threshold Xd, short-circuit fault detection is determined. Early warning of faults is provided, and the presence of an output signal from the anti-series winding current transformer in the second branch line is used to confirm whether a short-circuit fault has occurred. If a fault is detected, the specific fault type is determined based on the value of the E4 anti-series winding current transformer and the zero-sequence current or residual current. After determining the fault type, the fault type and the E4 anti-series winding current transformer value information are transmitted to the precise positioning module. The precise location of the fault is calculated by the GRNN anti-series winding current transformer neural network, and the short-circuit fault type and location signal are transmitted to the aggregation module.

[0060] The second branch line is for arc fault identification: the anti-series winding current transformer collects the current at the same location as the first branch line. Its single conductor eccentric loop design and parallel anti-series winding current transformer 33μH inductance can optimize high-frequency response and amplify the high-frequency characteristics of short circuit or arc faults. The effective value of the transformer output is simultaneously transmitted to the arc fault judgment module and the short circuit fault judgment and fault type judgment module of the first branch line. If an arc fault is detected, the arc fault information is transmitted to the summary module.

[0061] The aggregation module summarizes the fault information and transmits it to the communication module. Finally, the communication module transmits the fault information to the host computer via RS485 or Ethernet through the anti-series winding current transformer, realizing the integrated function of data acquisition, fault diagnosis and data transmission of the device.

[0062] The following section provides a further explanation of the specific implementation of the relevant modules involved in this device.

[0063] I. Data Acquisition Module

[0064] Three-phase voltage and current, as well as zero-sequence voltage and current, are collected using traditional sampling methods. After processing, they are converted into signals that can be recognized by a microcontroller and transmitted to the wavelet energy spectrum transformation module.

[0065] II. Wavelet Energy Spectrum Transform Module

[0066] The wavelet energy spectrum value E4 is extracted using the existing binary wavelet energy spectrum transform algorithm, and the E4 value is transmitted to the short-circuit fault judgment and fault type judgment module.

[0067] III. Short Circuit Fault Diagnosis and Fault Type Diagnosis Module

[0068] If the protection device detects that the E4 value exceeds the set fault threshold Xd (generally it should be more than 5 times the E4 value during normal operation), it indicates that a short circuit fault may have occurred. If the anti-series sensor module also detects a signal output (regardless of the magnitude), it can be determined that a short circuit fault has occurred. For a three-phase three-wire system, the protection device determines the fault type by detecting the zero-sequence current, and the zero-sequence current formula is shown in equation (1). For a three-phase four-wire system, the protection device distinguishes between ground faults and non-ground faults by detecting the residual current, and the residual current formula is shown in equation (2). After determining the type of short circuit fault, the fault type and the E4 value are transmitted to the precise positioning module.

[0069] IV. Precise Positioning Module

[0070] Figure 2 The image shows a simulation model of a microgrid, with fault points located at distances of 100 meters, 200 meters, 300 meters, 400 meters, 500 meters, and 600 meters from the detection points.

[0071] Due to different initial phase angles θ during faults c The magnitude of the first peak of the fault current wavelet energy spectrum waveform is E4. max The value decreases as the distance *d* from the short-circuit point to the detection point increases. Research has shown that this conclusion holds true for different short-circuit fault types. Therefore, E4 can be measured by the protection device. max and the fault phase angle θ c To achieve precise location of short-circuit faults.

[0072] The fault location method proposed in this invention uses θ in microgrids c E4 max The sum of power generated by distributed power sources in a microgrid, P dg Let be the variable, and let d be the distance from the short-circuit point to the detection point. Let be the objective function. Figure 2 For example, to ensure the accuracy of the model, we take θ. c ∈{0,30,60,90,120,150}, d∈{100,200,300,400,500,600}, P dg ∈{80,85,90,95,100,105,110,115,120}, the E4 value at the detection point when a single-phase ground fault occurs is measured. max This yields a total of 6 × 6 × 9 = 324 E4 values ​​for single-phase ground faults occurring on the lines. max The total number of sample points is 324. 300 sample points are randomly selected as the training set for the GRNN neural network algorithm, and the remaining 24 sample points are used as the test set. The precise positioning formula shown in equation (3) is obtained through training the GRNN neural network. In equation (3), D is the predicted distance from the fault point to the detection point; y k Let d be the value of the k-th sample in the training set; a k The expression is shown in equation (4), where IW is a 3×300 matrix. k σ represents the element of the k-th column of the IW matrix, which is the input of the k-th training set sample; σ is the smoothing factor, and the essence of training is to determine the optimal smoothing factor. In this invention, this parameter is set to 1.18. Equation (5) is the expression for the input quantity X. When a fault occurs, after determining the fault type, the P detected by the detection point is... dg θ c E4 max Substitute the values ​​into the precise location formula to calculate the accurate fault location. Then, transmit the accurate fault location value to the aggregation module.

[0073] (1)

[0074] (2)

[0075] (3)

[0076] (4)

[0077] (5)

[0078] V. Anti-series winding current transformer

[0079] Electromagnetic induction current transformers are non-contact devices that can efficiently transmit alternating current signals and have high output accuracy in low-frequency scenarios. However, when faced with high-frequency signals, their performance is easily affected by various factors such as winding structure, material properties, and spatial location, resulting in significant differences in the output of different windings corresponding to the same signal line. This poses a challenge to the accurate detection of high-frequency fault signals (such as high-frequency signals associated with arc faults).

[0080] To solve this problem, the present invention makes targeted modifications to the magnetic winding assembly, the structure of which is as follows: Figure 3 As shown, by winding two sets of windings with the same number of turns but in opposite directions, induced electromotive forces (EMFs) in opposite directions are generated. The specific amplitude of the induced EMF is affected by the distribution of the magnetic field strength and the rate of change of magnetic flux inside the magnetic ring. Connecting the corresponding terminals on one side of the two sets of windings together, and using the corresponding terminal on the other side as the signal output terminal, constitutes an anti-series winding current transformer. The actual device is shown in the image. Figure 4 As shown. The core advantage of this structure lies in the fact that by adjusting the bias position of the conductor within the magnetic ring (making the distance between the conductor and the two sets of windings different), the asymmetry of the magnetic field distribution can be used to amplify the high-frequency signal response, and then the arc fault detection can be achieved by analyzing the characteristics of the high-frequency signal.

[0081] To optimize the high-frequency response characteristics of the instrument transformer, its resonance characteristics need further study. Since the strength of the winding output signal depends on the impedance and the magnitude of the induced electromotive force at high-frequency resonance, if the parallel resistance is too small, it will weaken the instrument transformer's response to high-frequency current signals. On the other hand, the impedance of the inductor increases with frequency. Therefore, connecting an inductor in parallel at the output of the instrument transformer can suppress the output signal at low frequencies (reducing interference) and adjust the resonant frequency to adapt to the high-frequency characteristics of arc faults.

[0082] Based on this, this invention compares the output characteristics of a current transformer with a 10kΩ resistor and a 33μH inductor connected in parallel through resonant frequency testing. The test results are as follows: Figure 5As shown, when a 10kΩ resistor is connected in parallel, the transformer resonant frequency is 800kHz, corresponding to an effective output value of 222mV; while when a 33μH inductor is connected in parallel, the resonant frequency drops to 100kHz, and the effective output value is 170mV. Furthermore, the transformer output bandwidth with the parallel inductor is significantly wider than that with the parallel resistor. This is crucial for arc fault detection, as arc faults generate multi-band high-frequency signals, and the wide bandwidth ensures that more fault characteristics are captured. Therefore, the parallel inductor solution offers greater application advantages.

[0083] After the current on the line is collected by the anti-series winding current transformer, the transformer has no output under normal conditions. When a short circuit or arc fault occurs in the line, the transformer will couple and induce the high-frequency characteristic signal and transmit the effective value of the transformer output to the arc fault judgment module and the short circuit fault judgment module.

[0084] VI. Arc Fault Diagnosis Module

[0085] During an arc fault, the number of data points with a voltage greater than 75mV per half-cycle of power frequency is denoted as Num1, and the number of half-cycles with Num1 greater than or equal to 1 per second is denoted as Num2. Experiments show that under normal operation of conventional loads (such as refrigerators and air compressors) other than fluorescent lamps, the maximum Num2 is less than 10 (only a small number of pulses are generated by contactor operation); while during an arc fault, the maximum Num2 is 24-86, which can effectively distinguish interference from conventional loads.

[0086] The characteristic value S is obtained by accumulating the positive numbers in each half-cycle of the power frequency. The test found that the highest S value in the arc fault was 4.95, while the maximum S value when the fluorescent lamp was started was 13.67, which was significantly higher than the former. Therefore, S can accurately distinguish between the starting of the fluorescent lamp and the arc fault, and completely avoid false alarms caused by such high-frequency interference.

[0087] Based on the above analysis of the signals, an arc fault detection program was designed, and its flowchart is as follows: Figure 6 As shown.

[0088] When the microcontroller acquires enough data for half a power frequency cycle, it calculates the characteristic value S and Num2. Based on the analysis of the characteristic value S for each arc fault, a threshold of twice the maximum value of each fault experiment is used. When S is greater than the threshold of 10, it is determined that the fluorescent lamp has started, and the microcontroller stops judging the arc fault for the next second. The characteristic value Num2 is then judged, with a threshold of twice the maximum Num2 of each load (excluding the fluorescent lamp). When Num2 is greater than 10, an arc fault is determined to have occurred, and fault detection ends; otherwise, it waits for the next half-cycle of power frequency data acquisition to complete before proceeding with the next judgment. After an arc fault is determined, the arc fault information is transmitted to the aggregation module.

[0089] This embodiment also provides a method for detecting, locating, and controlling distribution network faults, implemented based on the above-mentioned device, including the following steps:

[0090] Collect the three-phase voltage, three-phase current, and zero-sequence current of the line;

[0091] The acquired current signal is subjected to binary wavelet energy spectrum transformation to extract wavelet energy spectrum values;

[0092] The anti-series winding current transformer senses the high-frequency harmonic components generated by a fault in the line at the same location. The anti-series winding current transformer adopts a single-wire eccentric loop design. Its winding is a single winding, with half wound in one direction and the other half wound in the opposite direction. An inductor is connected in parallel at the output terminal. The outputs of the anti-series winding current transformer cancel each other out under power frequency current and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs.

[0093] The wavelet energy spectrum value is compared with the set short-circuit judgment threshold, and the presence or absence of an output signal from the anti-series winding current transformer is used to determine whether a short-circuit fault has occurred. If a short-circuit fault is determined, the specific fault type is determined based on the wavelet energy spectrum value and the zero-sequence current or residual current.

[0094] The precise location of the fault is calculated using a GRNN neural network based on the fault type and wavelet energy spectrum value.

[0095] Determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer;

[0096] The system summarizes the types and locations of short-circuit faults and arc fault information, and transmits them to the host computer.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A power distribution network fault detection and location monitoring and control device, characterized in that, include: The power module is used to provide operating power to the entire device; The data acquisition module is used to acquire the three-phase voltage, three-phase current, and zero-sequence current of the line. The wavelet energy spectrum transformation module is connected to the acquisition module and is used to perform binary wavelet energy spectrum transformation on the acquired current signal to extract the wavelet energy spectrum value. An anti-series winding current transformer, located at the same position as the acquisition module, is used to sense high-frequency harmonic components generated by faults in the line. The anti-series winding current transformer adopts a single-wire eccentric loop design, with a single winding that is wound in one direction first and then in the opposite direction for the other half. An inductor is connected in parallel at the output terminal. Under power frequency current, the outputs of the anti-series winding current transformer cancel each other out and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs. The short-circuit fault judgment and fault type judgment module is connected to the wavelet energy spectrum transformation module and the anti-series winding current transformer, respectively. It is used to compare the wavelet energy spectrum value with the set short-circuit judgment threshold, and to determine whether a short-circuit fault has occurred by combining whether the anti-series winding current transformer has an output signal, and to determine the specific fault type based on the wavelet energy spectrum value and the zero-sequence current or residual current. The precise positioning module is connected to the short-circuit fault judgment and fault type judgment module. It is used to calculate the precise location of the fault through the GRNN neural network based on the fault type and wavelet energy spectrum value output by the short-circuit fault judgment and fault type judgment module. The arc fault detection module is connected to the anti-series winding current transformer and is used to determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer. The aggregation module is connected to the precise positioning module and the arc fault judgment module respectively, and is used to aggregate fault information, including short circuit fault type and location information, and arc fault information. The communication module, connected to the aggregation module, is used to transmit the fault information aggregated by the aggregation module to the host computer.

2. The distribution network fault detection and location monitoring device according to claim 1, characterized in that, The inductor connected in parallel at the output terminal of the anti-series winding current transformer is a 33μH inductor, which is used to adjust the resonant frequency of the transformer to 100kHz and broaden the high-frequency response bandwidth.

3. The distribution network fault detection and location monitoring device according to claim 1, characterized in that, In the short-circuit fault detection and fault type detection module, when the wavelet energy spectrum value E4 exceeds the short-circuit detection threshold X... d Furthermore, when the anti-series winding current transformer outputs a signal, it is determined that a short-circuit fault has occurred; the short-circuit judgment threshold X d It is more than 5 times the E4 value during normal operation.

4. The distribution network fault detection and location monitoring device according to claim 1, characterized in that, In the short-circuit fault judgment and fault type judgment module, for a three-phase three-wire system, the fault type is determined by detecting the zero-sequence current; for a three-phase four-wire system, the ground fault and non-ground fault are distinguished by detecting the residual current.

5. The distribution network fault detection and location monitoring device according to claim 1, characterized in that, The precise positioning module uses the initial phase angle θ of the fault. c The maximum value of wavelet energy spectrum E4 is E4. max And the sum of the power generated by distributed power sources in the microgrid, P dg Using the distance d from the short-circuit point to the detection point as the input variable and the distance d from the short-circuit point to the detection point as the objective function, a precise localization formula is obtained through training a GRNN neural network; when a fault occurs, the P detected by the detection point... dg θ c E4 max Substituting the values ​​into the precise location formula, the exact value of the fault location can be calculated.

6. The distribution network fault detection and location monitoring and control device according to claim 5, characterized in that, The training samples of the GRNN neural network are obtained by testing different initial phase angles θ for different faults. c Different fault distances d and different distributed power sources P dg E4 below max The values ​​are obtained by sampling, and the smoothing factor of the GRNN neural network is 1.

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7. The distribution network fault detection and location monitoring device according to claim 1, characterized in that, In the arc fault judgment module, the number of data points greater than 75mV in each half-cycle of power frequency during an arc fault is recorded as Num1, and the number of half-cycles with Num1 greater than or equal to 1 per second is recorded as Num2. When Num2 is greater than a preset threshold, an arc fault is determined to have occurred. The positive numbers in each half-cycle of the power frequency are accumulated to obtain the characteristic value S. When S is greater than the preset threshold, it is determined that the fluorescent lamp is started, and the judgment of arc fault is stopped within the next second.

8. The distribution network fault detection and location monitoring and control device according to claim 7, characterized in that, The preset threshold for Num2 is 10; the preset threshold for the feature value S is 10.

9. A method for detecting and locating distribution network faults, implemented based on the device described in any one of claims 1-8, characterized in that, Includes the following steps: Collect the three-phase voltage, three-phase current, and zero-sequence current of the line; The acquired current signal is subjected to binary wavelet energy spectrum transformation to extract wavelet energy spectrum values; The anti-series winding current transformer senses the high-frequency harmonic components generated by a fault in the line at the same location. The anti-series winding current transformer adopts a single-wire eccentric loop design. Its winding is a single winding, with half wound in one direction and the other half wound in the opposite direction. An inductor is connected in parallel at the output terminal. The outputs of the anti-series winding current transformer cancel each other out under power frequency current and have no effective output. It only responds to the high-frequency harmonic components generated by the fault and outputs a corresponding signal when a short circuit or arc fault occurs. The wavelet energy spectrum value is compared with the set short-circuit judgment threshold, and the presence or absence of an output signal from the anti-series winding current transformer is used to determine whether a short-circuit fault has occurred. If a short-circuit fault is determined, the specific fault type is determined based on the wavelet energy spectrum value and the zero-sequence current or residual current. The precise location of the fault is calculated using a GRNN neural network based on the fault type and wavelet energy spectrum value. Determine whether an arc fault has occurred based on the output signal of the anti-series winding current transformer; The system summarizes the types and locations of short-circuit faults and arc fault information, and transmits them to the host computer.

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