Method and system for detecting leakage fault of low-voltage alternating-current power distribution network

Abstract

The invention discloses a low-voltage alternating-current power distribution network electric leakage fault detection method and system, relates to the technical field of power grid fault detection, and is used for solving the problem of detection lag caused by incapability of timely detection when electric leakage current is relatively small but continuously exists. For each pre-selected node, acquiring and synthesizing a safety distance coefficient and a sensor compatibility ratio of each pre-selected node to obtain a key point location, acquiring the key point location and accessing a sensor of the corresponding point location, performing power-on detection to obtain a drift error and a direct current component, substituting the drift error and the direct current component into a logistic regression algorithm to obtain a sensor screening coefficient, and performing sensor screening; a sensor screening threshold is obtained through binary recursion, reserved sensors and deleted sensors are obtained, the temperature difference between the environment of each reserved sensor and the surface of a line and the total harmonic distortion ratio are detected, a set of fuzzy rules are formulated for fuzzy reasoning, the electric leakage fault detection result is determined, and the accuracy of electric leakage fault detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid fault detection, and more specifically, to a method and system for detecting leakage faults in a low-voltage AC power distribution network. Background Art

[0002] The low-voltage AC distribution network is the terminal link of the power system and directly supplies power to users. The rated voltage of common low-voltage distribution networks is 220V (single-phase) and 380V (three-phase). The low voltage reduces the insulation requirements of electrical equipment and is used in residential buildings, commercial buildings, industrial enterprises, agricultural production, etc.

[0003] The prior art has the following deficiencies: At present, there are various distribution modes of low-voltage AC distribution networks, usually three-phase four-wire power supply, with the neutral point directly grounded to improve safety and reduce the risk of phase-to-phase short circuit. However, no matter which distribution mode is used, the line loss will increase, which limits the power supply distance. For the detection of leakage faults during short-distance transportation, relying solely on the existing residual current protection device will result in fewer leakage detection mechanisms in the line, and when the leakage current is small but persists, it cannot be detected in time and the protection mechanism is triggered, resulting in detection lag and insufficient detection accuracy. Therefore, a method and system for detecting leakage faults in low-voltage AC distribution networks are proposed.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for detecting leakage faults in a low-voltage AC distribution network. By using a screening mechanism to screen out sensors corresponding to pre-selected nodes and key nodes, the detection accuracy and timeliness are improved, and the problems raised in the above-mentioned background technology are solved.

[0006] To achieve the above object, the present invention provides the following technical solution, a method and system for detecting leakage faults in a low-voltage AC distribution network, comprising: S1: Based on historical data, multiple pre-selected nodes are selected in the initialization phase. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is then compared with the preset point classification coefficient threshold. The pre-selected points are screened for a second time to obtain the key points. S2: Obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, and substitute them into the logistic regression algorithm to obtain the sensor screening coefficient; S3: Obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, and select the retained sensors for retention; S4: Obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, and obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio; S5: Obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, substitute them into fuzzy logic for fuzzy reasoning, and obtain the leakage fault detection result.

[0007] In a preferred embodiment, in the initialization phase, historical data is scheduled, the number of pre-selected nodes is set to n, and the safety distance coefficient and sensor compatibility ratio of each pre-selected node are collected according to the pre-selected nodes; By obtaining the actual distance between the nearest object and the pre-selected node and calculating the ratio with the preset safety distance, the first Safety distance factor of pre-selected nodes ;in For the pre-selected node index, which is a positive integer; By presetting sensors of different categories, the number of compatible sensor categories of each pre-selected node is counted, and the ratio of the number of compatible sensor categories to the total number of sensor categories is calculated to obtain the first Sensor compatibility ratio of pre-selected nodes .

[0008] In a preferred embodiment, the safety distance coefficient and the sensor compatibility ratio of each pre-selected node are standardized and weighted to obtain the point classification coefficient of each pre-selected node, and the number of point classification coefficients is consistent with the number of pre-selected nodes; The point classification coefficient is compared with the preset point classification coefficient threshold. If the point classification coefficient is greater than or equal to the point classification coefficient threshold, the current pre-selected point is marked as a key point. If the point classification coefficient is less than the point classification coefficient threshold, the current pre-selected point is marked as a deleted point and the deleted point is deleted.

[0009] In a preferred embodiment, a sensor is set according to the position information of the key point, and after the key point is connected to the sensor, an input test of an electrical signal is performed on it; The sensor is set to run for 24 hours under a constant leakage current, and the output voltage of the sensor is collected. The absolute value of the difference between the maximum output voltage and the minimum output voltage measured by the sensor within 24 hours is calculated by ratio with the rated output value within the theoretical maximum measurement range of the sensor to obtain the value of the first The drift error of the sensor ,in, is the sensor index, which is a positive integer; By setting a complete cycle, the sensor is used to perform regular sampling of the sawtooth wave, the waveform value at each time point is recorded, the waveform values ​​of all sampling points in the cycle are summed up, and the ratio is calculated with the total number of sampling points to obtain the first The DC component of each sensor .

[0010] In a preferred embodiment, the DC component and the drift error are standardized and substituted into a logistic regression calculation to obtain the sensor screening coefficient.

[0011] In a preferred embodiment, by collecting historical data of sensors and sensor screening coefficients, a candidate sensor list is created, a search range is determined by setting a minimum value and a maximum value of a sensor screening threshold, and a recursive function is defined to obtain a sensor screening threshold; If the sensor screening coefficient is greater than or equal to the sensor screening threshold, the sensor corresponding to the current key point is marked as a deleted sensor, and an end signal is generated; If the sensor screening coefficient is less than the sensor screening threshold, the sensor corresponding to the current key point is marked as a reserved sensor, and a reservation signal is generated; The sensor corresponding to the current key point marked as the retained sensor is retained, and the sensor corresponding to the current key point marked as the deleted sensor is deleted.

[0012] In a preferred embodiment, a temperature sensor is used to collect the ambient temperature in the power distribution network, an infrared temperature sensor is used to measure the line surface temperature, and the ambient temperature is subtracted from the line surface temperature to obtain the temperature difference between the environment and the line surface; The frequency domain signal is obtained by sampling the current waveform signal and performing Fourier transform on the current waveform signal. The first frequency component and the remaining harmonics are extracted from the frequency domain signal, and the remaining harmonics are marked as high-order harmonics. The square sum of the current components of all high-order harmonics is calculated by the ratio of the first frequency component to obtain the total harmonic distortion ratio.

[0013] In a preferred embodiment, the temperature difference between the environment and the line surface and the total harmonic distortion ratio are defined as input variables, which are divided into different fuzzy sets respectively; The leakage fault detection result is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the temperature difference between the environment and the line surface and the total harmonic distortion ratio on the leakage fault detection results; Fuzzy reasoning is performed according to fuzzy rules to determine the leakage fault detection result.

[0014] A low-voltage AC power distribution network leakage fault detection system includes a point screening module, a sensor matching module, a sensor screening module, and a fault detection module; The point screening module is used to select multiple pre-selected nodes in the initialization stage based on historical data. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is compared with the preset point threshold. The pre-selected points are screened for a second time to obtain key points and sent to the sensor matching module. The sensor matching module is used to obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, substitute them into the logistic regression algorithm to obtain sensor screening coefficients, and send them to the sensor screening module; The sensor screening module is used to obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, select the retained sensors for retention, and send the retained sensors to the fault detection module; The fault detection module is used to obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, and use fuzzy logic to determine the leakage fault detection result.

[0015] Technical effects and advantages of the present invention: 1. The present invention preferentially selects multiple pre-selected nodes in the initialization stage based on historical data. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio of each pre-selected node are collected and integrated to obtain key points. The key points are obtained and sensors at corresponding points are connected to perform power-on detection to obtain drift errors and DC components. The drift errors and DC components are substituted into the logistic regression algorithm to obtain sensor screening coefficients. The sensor screening threshold is obtained through binary recursion to obtain retained sensors and deleted sensors. The retained sensors are selected for retention, thereby improving the accuracy of leakage detection, ensuring the long-term stability of sensors, improving detection efficiency, and reducing resource waste. 2. The present invention obtains the retained sensors, detects the environmental characteristic information of each retained sensor and the harmonic characteristics generated by the nonlinear load, obtains the temperature difference between the environment and the line surface and the total harmonic distortion ratio, formulates a set of fuzzy rules for fuzzy reasoning, determines the leakage fault detection result, improves the accuracy of leakage fault detection, early fault warning capability, and reduces the probability of system false alarms and missed alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a method flow chart of a method for detecting leakage faults in a low-voltage AC distribution network.

[0017] Figure 2The present invention is a module schematic diagram of a low-voltage AC distribution network leakage fault detection system. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1 See also Figure 1 , a low voltage AC distribution network leakage fault detection method, the specific operation process is as follows: S1: Based on historical data, multiple pre-selected nodes are selected in the initialization phase. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is then compared with the preset point classification coefficient threshold. The pre-selected points are screened for a second time to obtain the key points. In the initialization phase, historical data is dispatched according to the set data dispatch and analysis mechanism to ensure that the selected nodes can accurately reflect the leakage risk in the low-voltage distribution network. The following are the specific methods: Obtain and organize historical data, call the low-voltage distribution network operation and maintenance system, substation database, and fault log to count the number of leakage faults that occurred on the line, the duration of each fault, the fault type (instantaneous leakage, continuous leakage, intermittent leakage, etc.), and the proportion of the affected area; Determine the specific location and number of pre-selected nodes based on the number of leakage faults that occur on the line, the duration of each fault, the fault type, and the impact range; According to the pre-selected nodes, the safety distance coefficient and sensor compatibility ratio of each pre-selected node are collected; The logic of obtaining the safety distance coefficient is to obtain the actual distance between the nearest object and the pre-selected node and calculate the ratio with the preset safety distance to obtain the first Safety distance factor of pre-selected nodes ;in is the pre-selected node index, which is a positive integer; Specifically, the nearest object around the preselected node is determined. The nearest object refers to any object that is stationary relative to the preselected node, including but not limited to fixed facilities such as building structures, power distribution cabinets, equipment casings, metal conductors, trees, and pipelines. For the distance measurement between the preselected node and the nearest object, the straight-line distance between the installation point and the nearest object can be measured by a laser ranging sensor or the distance from the installation point to the nearest object can be obtained by an ultrasonic sensor. The specific measurement sensor is not limited and will not be described in detail here. It should be noted that the preset safety distance is obtained by the experimenter through comprehensive analysis based on historical data and the statistical proportion of the possibility of leakage faults in history, which will not be elaborated here; Among them, when the safety distance coefficient is greater than 1, it indicates that the installation point meets the safety requirements, that is, the distance between the installation point and the surrounding objects is greater than the preset safety distance and meets the safety specifications; when the safety distance coefficient is less than or equal to 1, it indicates that the installation point may have safety hazards and the installation position needs to be adjusted or additional safety protection measures need to be taken; The sensor compatibility ratio refers to the ratio of each pre-selected node to the sensor compatibility. The acquisition logic is to preset different categories of sensors, count the number of sensor categories that each pre-selected node is compatible with, and calculate the ratio of the number of compatible sensor categories to the total number of sensor categories to obtain the first Sensor compatibility ratio of pre-selected nodes ; Among them, the number of sensors of different categories is not limited, and each sensor can be used for leakage detection in a specific environment or a specific type, including but not limited to: residual current sensor, voltage sensor, current transformer, electromagnetic field induction sensor. Further, the experimenter can classify the sensors according to the size or corresponding function, or classify them according to different sensor versions. The number of sensors of different categories is not limited and will not be elaborated here. It should be noted that the higher the sensor compatibility ratio, the better the adaptability of the pre-selected node, which can install and support multiple types of sensors, so that the node has high availability under different detection schemes; The safety distance coefficient and sensor compatibility ratio of each pre-selected node are standardized according to the Max-Min standardization algorithm. The specific formula is: In the formula, is the safety distance factor, is the standardized safety distance factor, is the minimum value of the safety distance factor, is the maximum value of the safety distance coefficient; The sensor compatibility ratio is also standardized using the above formula, which will not be elaborated here. The safety distance coefficient and sensor compatibility ratio of each pre-selected node are weighted and calculated to obtain the point classification coefficient of each pre-selected node, and the number of point classification coefficients is consistent with the number of pre-selected points; It should be noted that the pre-selected points are specific points obtained by processing and calculating a series of parameters of the pre-selected nodes. The pre-selected points also include the location information of the pre-selected nodes. Specifically, one node corresponds to one point, that is, there are pre-selected points that are the same in number as the pre-selected nodes. Compare the point classification coefficient with the preset point classification coefficient threshold. If the point classification coefficient is greater than or equal to the point classification coefficient threshold, mark the current pre-selected point as a key point. If the point classification coefficient is less than the point classification coefficient threshold, mark the current pre-selected point as a deleted point and delete the deleted point. S2: Obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, and substitute them into the logistic regression algorithm to obtain the sensor screening coefficient; Among them, the key points include the location information of the key points, and the corresponding types of sensors are set according to the location information of the key points. Specifically, the types of sensors connected to each key point are not limited, but are implemented by the experimenters according to the actual application scenarios, which will not be elaborated here; Power-on detection refers to the input test of electrical signals after the key points are connected to the sensor to evaluate its performance under actual working conditions, including measuring DC components and drift errors, to ensure that the selected sensors have high accuracy, stability and adaptability. The specific operation process is as follows: Step A1: Install sensors of corresponding categories at key points and make electrical connections to the low-voltage distribution network; Step A2: Perform zero point calibration on the sensor to ensure that the output voltage / current is the reference value when there is no external interference; Step A3: Set the data sampling frequency (e.g. 10kHz) to ensure sufficient sampling accuracy; Through the above steps, the sawtooth wave and constant input corresponding to the sensor can be collected; Among them, the sawtooth wave refers to a periodic waveform whose shape is similar to the edge of a sawtooth. Its characteristic is that the signal rises (or falls) linearly within a cycle, then suddenly drops back to the starting value, and repeats the process. It is used to measure the dynamic response characteristics of the sensor, observe the sensor's ability to follow gradually changing signals, detect whether the sensor can accurately capture high-frequency components, and analyze whether there is abnormal jitter or distortion in the sawtooth wave signal; The Fourier series expansion of the sawtooth wave signal shows that it contains multiple high-order harmonics. The specific mathematical formula is: ; In the formula, is the signal amplitude, is the fundamental frequency, is a sawtooth wave signal in the time domain, that is, a voltage or current that changes with time. is the harmonic order (i.e. the index of the sinusoidal component in the Fourier expansion), The signs are alternating, indicating that the phases of odd harmonics change alternately. is the time variable, For frequency A sine wave; Furthermore, the sawtooth wave is a non-sinusoidal periodic signal, which can be decomposed into the superposition of multiple sine waves, containing all odd harmonics, and the amplitude of each harmonic decreases. Since the high-order harmonics decay slowly, the sawtooth wave has a strong high-frequency component, which places high requirements on the frequency response of the sensor. Constant input refers to a stable, unchanging DC or AC signal given to the sensor, which can further evaluate the sensitivity, linearity, frequency response, and error characteristics of the sensor; Drift error refers to the measurement stability of the sensor under long-term operation or environmental changes (temperature, electromagnetic interference). The acquisition logic is to set the sensor to run for 24 hours under a constant leakage current, collect the voltage output by the sensor, and calculate the ratio of the absolute value of the subtraction between the maximum output voltage value and the minimum output voltage value measured by the sensor within 24 hours and the rated output value within the theoretical maximum measurement range of the sensor to obtain the first The drift error of the sensor ,in, is the sensor index, which is a positive integer; Among them, the rated output value within the theoretical maximum measurement range of the sensor refers to the standard output voltage or current value of the sensor at full scale within its designed working range, which is determined by the design specification and calibration of the sensor and is not affected by the actual measurement conditions. Even if the current measurement value of the sensor is small, it still has a fixed theoretical maximum output value. The rated output value within the theoretical maximum measurement range of the sensor is not limited and will not be elaborated here; The DC component refers to the average value of all instantaneous values ​​of the signal waveform (sawtooth wave) in a complete cycle in a periodic signal. The acquisition logic is to set a complete cycle, use the sensor to perform regular sampling of the sawtooth wave, record the waveform value at each time point, sum the waveform values ​​of all sampling points in the cycle, and calculate the ratio with the total number of sampling points to obtain the first The DC component of each sensor ; It should be noted that the calculation of the DC component involves periodic sampling of the sawtooth wave signal, while the calculation of the drift error involves the output fluctuation of the sensor under long-term operation. The setting of the complete cycle needs to be consistent with the 24-hour setting of the drift error to ensure that the accuracy, stability and comparability of the measurement data are improved within the same cycle time period, thereby more scientifically evaluating the performance of the sensor; The DC component and the drift error are normalized. The specific processing method can be based on the Max-Min normalization algorithm mentioned above, which will not be described in detail here. Substituting the DC component and drift error into the logistic regression calculation, the specific formula is expressed as follows: ; In the formula, is the result of logistic regression calculation, that is, the sensor screening coefficient, e is the natural base, and y is the linear combination term of the logistic regression model. Specifically, y can be set as: ; In the formula, is the bias term, and are the regression coefficients of the DC component and the drift error respectively; S3: Obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, and select the retained sensors for retention; The logic of obtaining the sensor screening threshold is to collect the historical data of the sensor, including the sensor readings and the corresponding key point data, determine the sensor screening coefficient, and evaluate the sensor screening results. According to the sensor screening coefficient, select sensors that are strongly related to the key points, create a candidate sensor list, and then determine the search range by setting the minimum value low and the maximum value high of the sensor screening threshold. Define a recursive function find_threshold(low,high), and its implementation steps are as follows: Step B1: Calculate the middle value of the current range mid = (low + high) / 2; Step B2: Use the current intermediate value mid to evaluate the performance of the sensor and determine whether the evaluation result meets expectations; Step B3: If it meets the requirements, it is used as a candidate threshold. If it does not meet the requirements and the performance is too low, the search range is adjusted to low = mid + 1 (i.e., greater than the middle value of the current range). If it does not meet the requirements and the performance meets the requirements, the search continues in the left half and is adjusted to high = mid - 1. Step B4: When low is greater than high, the recursion ends and the best threshold found is returned; It should be noted that in step B3, the right half can be preferentially selected for searching. The specific rules for selecting the left and right halves are not limited, but are determined by the experimenter according to the specific actual candidate sensor list, and will not be elaborated here. After obtaining the sensor screening coefficient, the sensor screening coefficient is compared and analyzed with the sensor screening threshold obtained by binary recursion; If the sensor screening coefficient is greater than or equal to the sensor screening threshold, the sensor corresponding to the current key point is marked as a deleted sensor, and an end signal is generated; If the sensor screening coefficient is less than the sensor screening threshold, the sensor corresponding to the current key point is marked as a reserved sensor, and a reservation signal is generated; The sensor corresponding to the current key point marked as a retained sensor is retained, and the sensor corresponding to the current key point marked as a deleted sensor is deleted; The present invention preferentially selects a plurality of pre-selected nodes in the initialization stage according to historical data, collects and integrates the safety distance coefficient and the sensor compatibility ratio of each pre-selected node for each pre-selected node, obtains the key points, obtains the key points and connects the sensors at the corresponding points, performs power-on detection, obtains the drift error and the DC component, substitutes them into the logistic regression algorithm to obtain the sensor screening coefficient, obtains the sensor screening threshold through binary recursion, obtains the retained sensors and the deleted sensors, selects the retained sensors for retention, improves the accuracy of leakage detection, ensures the long-term stability of the sensor, improves the detection efficiency, and reduces the waste of resources.

[0020] Example 2 In Example 1 of the present invention, an example is given to illustrate that based on historical data, multiple pre-selected nodes are preferentially selected in the initialization stage. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio of each pre-selected node are collected and integrated to obtain key points, the key points are obtained and the sensors at the corresponding points are connected, and power-on detection is performed to obtain drift errors and DC components, which are substituted into the logistic regression algorithm to obtain sensor screening coefficients, and the sensor screening threshold is obtained by binary recursion to obtain retained sensors and deleted sensors, and the retained sensors are selected for retention; however, in Example 1, only the accuracy of leakage fault detection of the low-voltage AC distribution network is calibrated and screened, and no analysis is made on how to detect leakage faults, and there is a lack of a specific leakage fault determination mechanism. In view of the above problems, Example 2 of the present invention is further refined; S4: Obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, and obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio; Environmental characteristic information refers to finding electrical fire risks by detecting temperature characteristics to avoid causing electrical equipment temperature rise and fire. Usually, due to line aging, the insulation layer is damaged, resulting in leakage current, loose equipment wiring, local high temperature, and overload operation causing wire heating, accelerating insulation aging, etc. Among them, the harmonic characteristics generated by nonlinear loads refer to the distortion of current waveforms when nonlinear devices such as rectifiers, inverters, switching power supplies, and arc furnaces are in operation, resulting in the injection of high-order harmonic currents other than the fundamental wave into the power grid, thereby affecting the quality of power. Since the current waveform of nonlinear loads is different from the standard sine wave, its Fourier series expansion will contain integer multiple harmonic components other than the fundamental frequency (power frequency, such as 50Hz or 60Hz), so the harmonic currents cause additional losses, increase the temperature of equipment such as motors, transformers, and cables, and increase the risk of electrical fires; The logic of obtaining the temperature difference between the environment and the line surface is to use a temperature sensor to collect the ambient temperature in the distribution network, use an infrared temperature sensor to measure the line surface temperature, subtract the ambient temperature from the line surface temperature, and obtain the temperature difference between the environment and the line surface; The logic of obtaining the total harmonic distortion ratio is to sample the current waveform signal, perform Fourier transform on the current waveform signal, obtain the frequency domain signal, extract the first frequency component and the remaining harmonics from the frequency domain signal, mark the remaining harmonics as high-order harmonics, calculate the ratio of the square sum of the current components of all high-order harmonics to the first frequency component, and obtain the total harmonic distortion ratio; It should be noted that the total harmonic distortion ratio indicates the intensity of the harmonic components in the current, reflecting the degree of distortion of the current waveform. The higher the total harmonic distortion ratio value, the greater the degree of distortion of the current waveform and the greater the risk of electrical fire. S5: Obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, substitute them into fuzzy logic for fuzzy reasoning, and obtain the leakage fault detection result; For example, "High", "Low", "Medium" for the temperature difference between the environment and the line surface, "Elevated", "Slow", "Moderate" for the total harmonic distortion ratio; Formulate a set of fuzzy rules to describe the impact of different input variables on output variables. The definition of rules can be based on professional knowledge or obtained through data analysis and experiments. For example: The temperature difference between the environment and the line surface is marked as X, the total harmonic distortion ratio is marked as U, and the leakage fault detection result is marked as C_results; Then we can define: Rule 1: IF (X is High) AND (U is Elevated) THEN (C_results is High) Rule 2: IF (X is Low) AND (U is Slow) THEN (C_results is Low) ... Perform fuzzy reasoning based on fuzzy rules to determine the leakage fault detection result; It should be noted that the division of fuzzy sets can be adjusted according to actual conditions. For example, although this embodiment uses three fuzzy sets as examples, the temperature difference between the environment and the line surface and the total harmonic distortion ratio can actually be divided into more than three sets to facilitate better precise adjustment according to different occupancy rates and change rates. Furthermore, for the judgment of the temperature difference between the environment and the line surface and the total harmonic distortion ratio, the threshold value can be set according to the actual situation for judgment. For example, when the temperature difference between the environment and the line surface exceeds 67%, it is marked as "High", and when the total harmonic distortion ratio is higher than 72%, it is marked as "Elevated", etc., which will not be elaborated here; Optionally, the leakage fault detection result is returned to step S1 as historical data, and when the pre-selected points are called in the next round, it is used as a selection rule in the initialization stage to screen out multiple pre-selected nodes to complete the closed loop of the method; The present invention obtains the reserved sensors, detects the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, obtains the temperature difference between the environment and the line surface and the total harmonic distortion ratio, formulates a set of fuzzy rules for fuzzy reasoning, determines the leakage fault detection result, improves the accuracy of leakage fault detection, early fault warning capability, and reduces the probability of system false alarm and missed alarm; Example 3 See also Figure 2 , a low voltage AC distribution network leakage fault detection system, including a point screening module, a sensor matching module, a sensor screening module, and a fault detection module; The point screening module is used to select multiple pre-selected nodes in the initialization stage based on historical data. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is compared with the preset point threshold. The pre-selected points are screened for a second time to obtain key points and sent to the sensor matching module. The sensor matching module is used to obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, substitute them into the logistic regression algorithm to obtain sensor screening coefficients, and send them to the sensor screening module; The sensor screening module is used to obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, select the retained sensors for retention, and send the retained sensors to the fault detection module; The fault detection module is used to obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, and use fuzzy logic to determine the leakage fault detection result.

[0021] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0022] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0023] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0024] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0025] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0026] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0027] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0028] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0029] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0030] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0031] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. 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 perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0032] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for detecting leakage faults in a low voltage AC distribution network, characterized in that: include: S1: Based on historical data, multiple pre-selected nodes are selected in the initialization stage. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is compared with the preset point classification coefficient threshold. The pre-selected points are screened for a second time to obtain the key points. S2: Obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, and substitute them into the logistic regression algorithm to obtain the sensor screening coefficient; S3: Obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, and select the retained sensors for retention; S4: Obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, and obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio; S5: Obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, substitute them into fuzzy logic for fuzzy reasoning, and obtain the leakage fault detection result.

2. A method for detecting leakage faults in a low voltage AC distribution network according to claim 1, characterized in that: In the initialization phase, historical data is dispatched to collect the safety distance coefficient and sensor compatibility ratio of each pre-selected node according to the pre-selected nodes; By obtaining the actual distance between the nearest object and the pre-selected node and calculating the ratio with the preset safety distance, the first Safety distance factor of pre-selected nodes ;in is the pre-selected node index, which is a positive integer; By presetting sensors of different categories, the number of compatible sensor categories of each pre-selected node is counted, and the ratio of the number of compatible sensor categories to the total number of sensor categories is calculated to obtain the first Sensor compatibility ratio of pre-selected nodes .

3. A method for detecting leakage faults in a low voltage AC distribution network according to claim 2, characterized in that: The safety distance coefficient and sensor compatibility ratio of each pre-selected node are standardized and weighted to obtain the point classification coefficient of each pre-selected node, and the number of point classification coefficients is consistent with the number of pre-selected nodes; The point classification coefficient is compared with the preset point classification coefficient threshold. If the point classification coefficient is greater than or equal to the point classification coefficient threshold, the current pre-selected point is marked as a key point. If the point classification coefficient is less than the point classification coefficient threshold, the current pre-selected point is marked as a deleted point and the deleted point is deleted.

4. A method for detecting leakage faults in a low voltage AC distribution network according to claim 3, characterized in that: Sensors are set up according to the location information of key points. After the key points are connected to the sensors, electrical signal input tests are performed on them. The sensor is set to run for 24 hours under a constant leakage current, and the output voltage of the sensor is collected. The absolute value of the difference between the maximum output voltage and the minimum output voltage measured by the sensor within 24 hours is calculated by ratio with the rated output value within the theoretical maximum measurement range of the sensor to obtain the value of the first The drift error of the sensor ,in, is the sensor index, which is a positive integer; By setting a complete cycle, the sensor is used to perform regular sampling of the sawtooth wave, the waveform value at each time point is recorded, the waveform values ​​of all sampling points in the cycle are summed up, and the ratio is calculated with the total number of sampling points to obtain the first The DC component of each sensor .

5. A method for detecting leakage faults in a low voltage AC distribution network according to claim 4, characterized in that: The DC component and drift error are standardized and substituted into the logistic regression calculation to obtain the sensor screening coefficient.

6. A method for detecting leakage faults in a low voltage AC distribution network according to claim 5, characterized in that: By collecting historical data of sensors and sensor screening coefficients, a candidate sensor list is created, the search range is determined by setting the minimum and maximum values ​​of the sensor screening threshold, and a recursive function is defined to obtain the sensor screening threshold; If the sensor screening coefficient is greater than or equal to the sensor screening threshold, the sensor corresponding to the current key point is marked as a deleted sensor, and an end signal is generated; If the sensor screening coefficient is less than the sensor screening threshold, the sensor corresponding to the current key point is marked as a reserved sensor, and a reservation signal is generated; The sensor corresponding to the current key point marked as the retained sensor is retained, and the sensor corresponding to the current key point marked as the deleted sensor is deleted.

7. A method for detecting leakage faults in a low voltage AC distribution network according to claim 6, characterized in that: Use a temperature sensor to collect the ambient temperature in the distribution network, use an infrared temperature sensor to measure the line surface temperature, subtract the ambient temperature from the line surface temperature to get the temperature difference between the environment and the line surface; The frequency domain signal is obtained by sampling the current waveform signal and performing Fourier transform on the current waveform signal. The first frequency component and the remaining harmonics are extracted from the frequency domain signal, and the remaining harmonics are marked as high-order harmonics. The square sum of the current components of all high-order harmonics is calculated by the ratio of the first frequency component to obtain the total harmonic distortion ratio.

8. A method for detecting leakage faults in a low voltage AC distribution network according to claim 7, characterized in that: The temperature difference between the environment and the line surface and the total harmonic distortion ratio are defined as input variables, which are divided into different fuzzy sets respectively; The leakage fault detection result is defined as the output variable and divided into fuzzy sets; Formulate fuzzy rules to describe the impact of the temperature difference between the environment and the line surface and the total harmonic distortion ratio on the leakage fault detection results; Fuzzy reasoning is performed according to fuzzy rules to determine the leakage fault detection result.

9. A low-voltage AC distribution network leakage fault detection system, applied to a low-voltage AC distribution network leakage fault detection method according to any one of claims 1 to 8, characterized in that: Including point screening module, sensor matching module, sensor screening module, fault detection module; The point screening module is used to select multiple pre-selected nodes in the initialization stage based on historical data. For each pre-selected node, the safety distance coefficient and sensor compatibility ratio corresponding to the pre-selected points of each pre-selected node are collected and integrated to obtain the point classification coefficient, which is compared with the preset point threshold. The pre-selected points are screened for a second time to obtain key points and sent to the sensor matching module. The sensor matching module is used to obtain key points and connect sensors at corresponding points, perform power-on detection, obtain drift error and DC component, substitute them into the logistic regression algorithm to obtain sensor screening coefficients, and send them to the sensor screening module; The sensor screening module is used to obtain the sensor screening coefficient, and obtain the sensor screening threshold through binary recursion, obtain the retained sensors and deleted sensors, select the retained sensors for retention, and send the retained sensors to the fault detection module; The fault detection module is used to obtain the reserved sensors, detect the environmental characteristic information of each reserved sensor and the harmonic characteristics generated by the nonlinear load, obtain the temperature difference between the environment and the line surface and the total harmonic distortion ratio, and use fuzzy logic to determine the leakage fault detection result.

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