Method and system for quickly positioning ground fault of coal mine power supply system

Abstract

The invention discloses a coal mine power supply system ground fault rapid positioning method and system, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining the real-time zero-sequence current value of each junction box in a coal mine power supply system, and when the zero-sequence current value of any junction box exceeds a preset current threshold value, determining the current value of any junction box; acquiring voltage waveform data and current waveform data in a preset time period before and after the fault point, and calculating to obtain a distortion rate and a phase offset of each phase voltage according to the voltage waveform data and the current waveform data; determining a fault type based on the variation trend of the distortion rate and the phase offset; performing time sequence comparative analysis on the zero sequence current values collected at the junction boxes at the fault moment to obtain propagation time sequence characteristics of the zero sequence current among the detection points; and positioning the fault cable section where the fault point is located according to the propagation time sequence characteristics and the fault type. By implementing the method, the accuracy of ground fault positioning of the coal mine power supply system can be improved.

Description

Technical Field

[0001] This application relates to the field of electrical digital data processing, and particularly to a method and system for quickly locating grounding faults in a coal mine power supply system. Background Art

[0002] With the continuous expansion of the coal mine mining scale and the continuous upgrading of the mining technology, the complexity of the coal mine power supply system is also increasing. As the energy guarantee for coal mine production, the safe and stable operation of the coal mine power supply system is crucial for the normal production of the coal mine. Among them, the grounding fault in the cable network is one of the most common fault types in the coal mine power supply system. How to detect and accurately locate the grounding fault point in a timely manner has become an important issue for ensuring the safe operation of the coal mine power supply system.

[0003] Currently, the zero-sequence current protection method is often used in the coal mine power supply system to detect grounding faults. This scheme detects the zero-sequence current by installing zero-sequence current transformers at important nodes of the power supply system. When the detected zero-sequence current exceeds the set threshold, it is determined that a grounding fault has occurred in the system. In terms of fault location, it mainly relies on segmental testing of suspicious sections, and judges the approximate area of the fault point by measuring the change of the zero-sequence current.

[0004] However, in practical applications, due to the intricate cable lines and numerous branches in the coal mine power supply system, and some cables are laid in the underground environment. In a complex power supply network, the zero-sequence currents at each node often show complex distribution characteristics during a fault, and it is difficult to accurately judge the specific location of the fault point only based on the magnitude of the zero-sequence current value. Summary of the Invention

[0005] This application provides a method and system for quickly locating grounding faults in a coal mine power supply system, which is used to improve the accuracy of locating grounding faults in the coal mine power supply system.

[0006] In the first aspect, this application provides a method for quickly locating grounding faults in a coal mine power supply system, which is applied to the coal mine power supply system. The method includes: obtaining the real-time zero-sequence current values at each junction box in the coal mine power supply system. When the real-time zero-sequence current value at any junction box exceeds the preset current threshold, taking the sampling point at which the real-time zero-sequence current value first exceeds the preset current threshold as the fault point, and collecting the voltage waveform data and current waveform data within a preset time period before and after the fault point. According to the voltage waveform data and current waveform data, calculate the distortion rate and phase shift amount of each phase voltage; determine the fault type based on the change trend of the distortion rate and phase shift amount; perform a timing comparison analysis on the zero-sequence current values collected at each junction box at the fault moment to obtain the propagation timing characteristics of the zero-sequence current between each detection point; and locate the fault cable section where the fault point is located according to the propagation timing characteristics and the fault type.

[0007] In the above embodiments, the zero-sequence current value is collected in real time at each junction box, and the voltage and current waveform data before and after the fault point are obtained when a fault occurs. By calculating the distortion rate and phase shift of each phase voltage to determine the fault type, and combining the propagation time sequence characteristics of the zero-sequence current for location analysis, an active fault section location method is established, which realizes the accurate location of the cable section where the fault point is located in a complex power supply network, and improves the efficiency and accuracy of fault location.

[0008] Combined with some embodiments of the first aspect, in some embodiments, the step of locating the faulty cable section where the fault point is located according to the propagation time sequence characteristics and the fault type specifically includes: obtaining the zero-sequence current propagation characteristic template corresponding to the fault type from the preset fault characteristic library, and the zero-sequence current propagation characteristic template includes the propagation delay reference value and the amplitude attenuation reference value of the zero-sequence current between adjacent detection points under different fault types; calculating the actual propagation delay value and the actual amplitude attenuation value between each adjacent junction box according to the propagation time sequence characteristics, and the actual propagation delay value is obtained through the time difference between the zero-crossing points of the zero-sequence current waveforms at adjacent junction boxes, and the actual amplitude attenuation value is obtained through the ratio of the maximum amplitudes of the zero-sequence current at adjacent junction boxes; calculating the matching degree between the actual propagation delay value and the actual amplitude attenuation value and the propagation delay reference value and the amplitude attenuation reference value in the zero-sequence current propagation characteristic template to obtain the matching degree value of each cable section; determining the faulty cable section where the fault point is located according to the matching degree value.

[0009] In the above embodiments, the zero-sequence current propagation characteristic template in the preset fault characteristic library is established, and the matching degree analysis is carried out by calculating the actual propagation delay value and the amplitude attenuation value and the template. Since faults at different positions will result in different propagation characteristics, through the calculation and comparison of the matching degree value, the fault location result is more persuasive, and the reliability of fault location is further improved.

[0010] Combined with some embodiments of the first aspect, in some embodiments, after the step of locating the faulty cable section where the fault point is located according to the propagation time sequence characteristics and the fault type, the method further includes: respectively obtaining the waveform information of each cycle in the voltage waveform data and the current waveform data, and calculating the key characteristic parameters of each cycle according to the waveform information, and the key characteristic parameters include the amplitude mutation point parameter, the phase mutation point parameter and the waveform distortion parameter; converting the key characteristic parameters into key characteristic codes, and the key characteristic codes are used to characterize the waveform information of each cycle; using the preset standard characteristic code as the reference characteristic code, comparing the key characteristic code with the reference characteristic code to obtain the difference data; according to the difference data, screening out the cycle bands with the difference greater than the preset difference threshold, and marking the cycle bands as the fault characteristic cycle bands; generating a fault characteristic data chain according to the time sequence arrangement of the fault characteristic cycle bands, and the fault characteristic data chain includes the start time, the peak time and the recovery time of each fault characteristic cycle band.

[0011] In the above embodiments, cycle analysis is performed on the waveform data, key characteristic parameters are extracted to generate a fault characteristic data chain, and a complete record of the fault development process is established. This characteristic data chain contains key information such as the start, peak, and recovery times of the fault, providing an important basis for the accurate judgment of the fault type and the analysis of the fault development trend, and making the fault location and analysis more comprehensive and in-depth.

[0012] Combined with some embodiments of the first aspect, in some embodiments, the step of converting the key characteristic parameters into key characteristic codes specifically includes: dividing the waveform data of each cycle into N equally spaced sampling segments; calculating the waveform change rate between adjacent sampling points in each sampling segment; marking the sampling points with a waveform change rate greater than the preset change rate threshold as key sampling points; generating a position distribution sequence according to the distribution position of the key sampling points in the cycle; calculating the deviation between the actual waveform value and the standard sine waveform value at the key sampling points to generate a deviation value sequence; and performing combined coding on the position distribution sequence and the deviation value sequence to obtain the key characteristic code.

[0013] In the above embodiments, the waveform data of each cycle is divided into equally spaced samples, the waveform change rate is calculated, and key sampling points are marked to generate a position distribution sequence and a deviation value sequence. The key characteristic code is formed through combined coding, realizing the digital representation of waveform characteristics. This coding method retains the key change characteristics and deviation information of the waveform, making the extraction and comparison of fault characteristics more accurate and efficient.

[0014] Combined with some embodiments of the first aspect, in some embodiments, after the step of locating the fault cable section where the fault point is located according to the propagation timing characteristics and the fault type, the method further includes: generating a differential waveform spectrogram based on the difference data, where the differential waveform spectrogram is used to characterize the waveform frequency spectrum distribution during the fault process; dividing the fault characteristic data chain into N sub-chains according to the frequency spectrum distribution characteristics in the differential waveform spectrogram, with each sub-chain corresponding to an independent fault development stage; statistically analyzing the change trends of the position distribution sequence and the deviation value sequence of the key sampling points in each sub-chain to generate a fault evolution characteristic matrix; calculating the matching degree between the fault evolution characteristic matrix and a preset standard fault characteristic library to obtain matching degree data; selecting the K groups of historical fault data with the highest matching degree from the preset standard fault characteristic library according to the matching degree data; analyzing the position distribution sequence and the deviation value sequence in the K groups of historical fault data to extract common characteristics and generate a fault type characteristic template; and calculating the fitting degree between the fault characteristic data chain of the current fault and the fault type characteristic template, and when the fitting degree is greater than the preset fitting degree threshold, determining that the current fault type is the same as the fault type corresponding to the fault type characteristic template.

[0015] In the above embodiments, the fault feature data chain is divided according to the differential waveform spectrogram to generate a fault evolution feature matrix, which is then matched with the standard fault feature library. By extracting the common features of multiple groups of historical fault data to generate a feature template, an adaptive fault type recognition mechanism is established, enhancing the accuracy and adaptability of fault type judgment.

[0016] Combined with some embodiments of the first aspect, in some embodiments, after the step of locating the faulty cable section where the fault point is located according to the propagation timing characteristics and the fault type, the method further includes: establishing a fault development state transition diagram according to the timing relationship of each sub-chain in the fault evolution feature matrix, and the state transition diagram is used to represent the complete process of the fault from occurrence to development; calculating the transition probability between adjacent states in the fault development state transition diagram to generate a state transition probability matrix; updating the fault type feature template according to the state transition probability matrix.

[0017] In the above embodiments, a state transition diagram reflecting the complete process of fault development is established, the state transition probability matrix is calculated, and the fault type feature template is dynamically updated. This solution constructs a continuously optimized fault feature update mechanism, enabling the fault type feature template to adapt to the fault feature changes under different working conditions, and improving the accuracy and reliability of fault type judgment in various complex environments.

[0018] Combined with some embodiments of the first aspect, in some embodiments, the step of updating the fault type feature template according to the state transition probability matrix specifically includes: detecting the fitting degree between each sub-chain feature and the corresponding stage feature in the fault type feature template; when the fitting degree is lower than the preset fitting degree threshold, calculating the transition probability of the sub-chain feature in the state transition probability matrix; updating the fault type feature template according to the transition probability; and performing real-time monitoring and fault warning on the waveform data based on the updated fault type feature template and the state transition probability matrix.

[0019] In the above embodiments, the fitting degree between the sub-chain feature and the fault type feature template is detected in real time, and the feature template is dynamically updated according to the transition probability. By introducing the state transition probability matrix for calibration and optimization of the feature template, a more accurate correspondence relationship between the waveform features and the fault type is established, improving the accuracy rate of fault type judgment in a complex power supply system and reducing the deviation and misjudgment in the fault judgment process.

[0020] Second aspect, embodiments of the present application provide a coal mine power supply system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the coal mine power supply system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] Third aspect, embodiments of the present application provide a computer program product containing instructions. When the computer program product runs on a coal mine power supply system, it causes the coal mine power supply system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] Fourth aspect, embodiments of the present application provide a computer-readable storage medium, including instructions. When the instructions run on a coal mine power supply system, it causes the coal mine power supply system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the coal mine power supply system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. In the present application, the zero-sequence current value is collected in real time at each junction box, and the voltage and current waveform data before and after the fault point are obtained when a fault occurs. By calculating the distortion rate and phase offset of each phase voltage to determine the fault type, and combining the propagation timing characteristics of the zero-sequence current for positioning analysis, an active fault section positioning method is established, which realizes the accurate positioning of the cable section where the fault point is located in a complex power supply network, and improves the efficiency and accuracy of fault positioning.

[0025] 2. In the present application, a zero-sequence current propagation characteristic template in a preset fault characteristic library is established, and the matching degree analysis is carried out by calculating the actual propagation delay value and amplitude attenuation value with the template. Since faults at different positions will result in different propagation characteristics, through the calculation and comparison of the matching degree values, the fault positioning result is more persuasive, and the reliability of fault positioning is further improved.

[0026] 3. By performing cycle analysis on waveform data and extracting key characteristic parameters, this application generates a fault characteristic data chain, establishing a complete record of the fault development process. This characteristic data chain contains key information such as the start, peak, and recovery times of the fault, providing an important basis for the accurate judgment of fault types and the analysis of fault development trends, making fault location and analysis more comprehensive and in-depth. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic flowchart of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application; Figure 2 is a schematic diagram of system deployment of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application; Figure 3 is a schematic diagram of module connections inside a cable junction box of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application; Figure 4 is a voltage change diagram of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application; Figure 5 is another schematic flowchart of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application; Figure 6 is a schematic diagram of the structure of an entity device in a coal mine power supply system according to an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application, the singular forms "a", "an", "the above", "the", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations including one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0030] For ease of understanding, the following describes the process of the method provided in this embodiment. Please refer to Figure 1 , which is a schematic flowchart of a method for rapid location of grounding faults in a coal mine power supply system according to an embodiment of this application.

[0031] S101. Obtain the real-time zero-sequence current values at each junction box in the coal mine power supply system. The real-time zero-sequence current values are collected by zero-sequence current transformers installed at each junction box.

[0032] Among them, the coal mine power supply system refers to the overall power system network for power supply in the coal mine underground, including multiple distribution equipment and power supply lines. The junction box refers to a sealed box device for connecting and distributing cables, used to protect cable joints and achieve cable connection and distribution. The zero-sequence current value refers to the magnitude of the vector sum of three-phase currents, used to represent the degree of three-phase imbalance. The zero-sequence current transformer refers to an electromagnetic induction device for detecting zero-sequence current, which converts the primary-side current into the secondary-side current in proportion through the principle of electromagnetic induction.

[0033] During the normal operation of the coal mine power supply system, it is necessary to continuously monitor the operation status of the system to detect possible faults in a timely manner. Specifically, zero-sequence current transformers are installed at each cable junction box in the system. These transformers use the principle of high-precision electromagnetic induction. The cable passes through the primary-side coil of the transformer, and a current signal proportional to the primary-side current is induced in the secondary-side coil. Through real-time sampling and signal processing, the zero-sequence current values at each junction box can be accurately obtained. The continuous monitoring of the zero-sequence current values provides basic data support for fault detection.

[0034] In some embodiments, the zero-sequence current can be collected in multiple ways: Optionally, a high-precision zero-sequence transformer is used to collect three-phase currents in real time. The analog signals collected are converted into digital signals through digital signal processing technology, and accurate zero-sequence current values are obtained after filtering and amplification processing. This solution first collects the original current signal through the transformer, then performs analog-to-digital conversion using a high-speed ADC, then eliminates interference and noise through digital filtering algorithms, and finally obtains the final zero-sequence current value through data processing; Optionally, an intelligent current detection module is arranged at the junction box, and this module integrates the functions of zero-sequence current collection and data processing. The module includes components such as current sensors, signal conditioning circuits, and data processing units, and can automatically complete the whole process from signal collection to data output. It can be understood that other ways can also be used to implement the zero-sequence current collection function, such as using different types of sensors or different signal processing methods, which are not limited here.

[0035] S102. When the real-time zero-sequence current value at any junction box exceeds the preset current threshold, take the sampling point where the real-time zero-sequence current value first exceeds the preset current threshold as the fault point, and collect the voltage waveform data and current waveform data within a preset time period before and after the fault point.

[0036] Among them, the preset current threshold represents the zero-sequence current alarm value set by the system, which is used to judge whether a grounding fault occurs. The fault point refers to the specific location where the grounding fault occurs on the cable. The voltage waveform data refers to the change curve of the system voltage during a period of time before and after the fault occurs. The current waveform data refers to the change curve of the system current during a period of time before and after the fault occurs. The sampling point represents the time point for discrete sampling in the continuous waveform.

[0037] When the zero-sequence current anomaly is detected, the system needs to immediately start the fault recording program to capture the key data during the fault process. Specifically, when the system detects that the zero-sequence current value at a certain junction box exceeds the preset threshold, it is considered that a grounding fault may have occurred. At this time, the system will immediately start to record the voltage and current waveform data during a certain period (usually 100ms - 600ms) before and after the fault occurs. These data completely record the generation, development, and disappearance process of the fault, and are of great value for subsequent fault feature analysis and fault point location.

[0038] In some embodiments, the acquisition and recording of fault data can be achieved in various ways: Optionally, a high-speed data acquisition system is adopted, which includes a high-precision sampling circuit, a large-capacity memory, and a real-time processing unit. This system can continuously acquire voltage and current signals at a very high sampling rate. When a fault is detected, the historical data in the memory and the real-time data for a subsequent period of time are saved to form a complete fault record; Optionally, an intelligent fault recorder is used, which has functions such as trigger acquisition, data caching, and waveform analysis. The device continuously samples but does not save data in the normal state, and only starts data recording when the fault trigger condition is detected, which can greatly save storage space. It can be understood that other methods can also be adopted to implement the acquisition and recording function of fault data, such as adopting different trigger mechanisms or different storage strategies, which are not limited here.

[0039] S103. Calculate the distortion rate and phase offset of each phase voltage according to the voltage waveform data and the current waveform data.

[0040] Among them, the voltage waveform data refers to the data sequence of the three-phase voltage changing with time collected before and after the fault occurs. The current waveform data refers to the data sequence of the three-phase current changing with time collected before and after the fault occurs. The distortion rate represents the deviation degree between the actual waveform and the standard sine waveform, and is used to measure the waveform distortion degree. The phase offset is the relative displacement amount between the actual waveform and the standard sine waveform on the time axis, and is used to represent the timing change characteristics of the waveform.

[0041] After obtaining the waveform data during the fault process, it is necessary to deeply analyze these data to extract key characteristic parameters. Specifically, the system first preprocesses the collected voltage and current waveform data, including operations such as data filtering and normalization. Then, through digital signal processing methods such as Fourier transform, the time-domain waveform is converted to the frequency domain for analysis. By calculating the fundamental wave component and each harmonic component of the waveform, the waveform distortion rate is obtained. At the same time, by analyzing the timing relationship between the zero-crossing points and peak points of the waveform, the phase offset is calculated. These parameters can effectively reflect the changes in the electrical characteristics of the system when a fault occurs.

[0042] In some embodiments, the calculation of waveform characteristic parameters can be achieved in various ways: Optionally, the fast Fourier transform (FFT) algorithm is used to process the waveform data. First, the waveform data is processed with a window function to reduce spectral leakage, then the FFT operation is performed to obtain the spectrum, the fundamental wave and harmonic amplitudes are calculated, and finally the distortion rate is calculated according to the amplitude ratio of each component, and the phase offset is calculated through the phase difference between adjacent cycles; Optionally, the wavelet transform method is used to analyze the waveform characteristics. First, a suitable wavelet basis function is selected to perform multi-scale decomposition on the waveform, then the mutation characteristics and periodic characteristics of the waveform are extracted at different scales, and finally the distortion rate and phase offset are calculated by synthesizing the analysis results of each scale. It can be understood that other methods can also be used to calculate the waveform characteristic parameters, such as using other signal processing algorithms or feature extraction methods, which are not limited here.

[0043] S104. Determine the fault type based on the change trends of the distortion rate and phase offset.

[0044] Among them, the fault type refers to the specific grounding fault form judged according to the change law of the electrical characteristics of the system. The change trend represents the change characteristics of the distortion rate and phase offset over time. The fault judgment model refers to the mathematical model or rule set used to map the waveform characteristic parameters to specific fault types.

[0045] After obtaining the waveform characteristic parameters, it is necessary to determine the specific fault type according to the change law of these parameters. Specifically, the system first analyzes the change trends of the distortion rate and phase offset during the fault occurrence process, including characteristics such as the change speed, change amplitude, and change duration. Then, these change characteristics are matched with the pre-established fault feature library, and the most likely fault type is determined through pattern recognition or rule judgment methods. This method for judging the fault type based on waveform characteristics has high accuracy and reliability.

[0046] In some embodiments, the determination of the fault type can be achieved in various ways: Optionally, the rule-based expert system method is adopted. First, a knowledge base containing various fault feature rules is established, and then the waveform feature parameters obtained by actual calculation are substituted into the rules for reasoning. The final fault type judgment result is obtained through rule matching and confidence calculation. Optionally, the machine learning method is used. A classification model is trained through a large amount of historical fault data to establish the mapping relationship from waveform feature parameters to fault types, and then the trained model is used to classify and judge new fault cases. It can be understood that other ways can also be adopted to determine the fault type, such as using other artificial intelligence algorithms or hybrid methods, which are not limited here.

[0047] S105. Conduct a chronological comparison and analysis of the zero-sequence current values collected at each junction box at the fault moment to obtain the propagation chronological characteristics of the zero-sequence current among each detection point.

[0048] Among them, the chronological comparison and analysis refers to the time-series comparison and analysis of the zero-sequence current values collected at different time points and different positions. The propagation chronological characteristics refer to the characteristics such as time delay and attenuation shown during the propagation process of the zero-sequence current signal on the cable line. The detection point refers to the position of the junction box where the zero-sequence current transformer is installed. The zero-sequence current propagation characteristics refer to the physical characteristics shown when the zero-sequence current propagates on the cable line, including propagation speed, attenuation law, etc.

[0049] After determining the fault type, it is necessary to analyze the propagation characteristics of the fault current to determine the fault location. Specifically, the system first collects the zero-sequence current data collected at each junction box at the moment of the fault, arranges these data in chronological order to form a complete record of the propagation process. Then, analyze the time difference and amplitude change of the zero-sequence current waveforms between adjacent detection points, and calculate the characteristic parameters such as propagation time delay and signal attenuation. These parameters can reflect the propagation law of the fault current on the line and provide an important basis for fault point location.

[0050] In some embodiments, the analysis of the zero-sequence current propagation characteristics can be achieved in various ways: Optionally, the waveform correlation analysis method is adopted. First, synchronize the zero-sequence current waveforms at each detection point in time, then calculate the cross-correlation function of the waveforms between adjacent detection points, determine the propagation time delay through the time position of the cross-correlation peak, determine the signal attenuation degree through the peak amplitude ratio, and finally obtain the complete propagation characteristics by synthesizing the analysis results of multiple detection points. Optionally, the signal processing and feature extraction method is used. First, perform wavelet decomposition on the zero-sequence current waveform, extract the signals in the characteristic frequency band, and then analyze the propagation law of the characteristic signals in time and space, and describe the propagation characteristics by establishing a mathematical model. It can be understood that other ways can also be adopted to analyze the zero-sequence current propagation characteristics, such as using other signal analysis methods or propagation models, which are not limited here.

[0051] S106. Locate the faulty cable section where the fault point is located according to the propagation timing characteristics and fault type.

[0052] Among them, the cable section refers to the cable line between two adjacent junction boxes. The propagation characteristic template refers to the set of propagation delay reference values and amplitude attenuation reference values of zero-sequence current between adjacent detection points under different fault types. The fault feature library represents a database containing various typical fault cases and their characteristic parameters. The matching degree value refers to the degree of coincidence between the actual propagation characteristics and the feature template.

[0053] After obtaining the propagation characteristics of the zero-sequence current, it is necessary to accurately locate the fault point in combination with the fault type information. Specifically, the system first retrieves the zero-sequence current propagation characteristic template corresponding to the current fault type from the preset fault feature library. Then, the matching degree between the actually measured propagation delay value and amplitude attenuation value and the reference value in the template is calculated to obtain the matching degree value of each cable section. Finally, the most likely faulty section is determined according to the matching degree value. This method makes full use of the correlation between the propagation characteristics of the zero-sequence current and the fault location, and can quickly and accurately locate the fault point.

[0054] In some embodiments, the location of the fault point can be achieved in various ways: Optionally, a location algorithm based on propagation characteristics is adopted. First, the theoretical propagation delay is calculated according to the propagation speed of the zero-sequence current on the line, then the actually measured propagation delay is compared with the theoretical value, the fault point location is solved by establishing a mathematical equation, and finally, it is verified and corrected in combination with the amplitude attenuation characteristics; Optionally, a pattern recognition method is used. First, a feature library containing a large number of historical fault cases is established, and then the propagation characteristics of the current fault are pattern-matched with the templates in the feature library, and the most matching fault pattern is determined through similarity calculation, so as to obtain the fault point location. It can be understood that other methods can also be used to achieve the location of the fault point, such as adopting other location algorithms or hybrid methods, which are not limited here.

[0055] This step specifically includes: Obtain the zero-sequence current propagation characteristic template corresponding to the fault type from the preset fault feature library. The zero-sequence current propagation characteristic template includes the propagation delay reference value and amplitude attenuation reference value of the zero-sequence current between adjacent detection points under different fault types.

[0056] Among them, the preset fault feature library is a data set storing fault feature data of different types. The zero-sequence current is one-third of the vector sum of the three-phase currents. The propagation characteristic template describes the propagation law of the zero-sequence current in the cable. The propagation delay is the time required for the zero-sequence current to propagate between adjacent detection points. The amplitude attenuation is the degree of amplitude reduction of the zero-sequence current during propagation. The detection point is the position where the current sensor is installed. Extract the zero-sequence current propagation characteristic template from the fault feature library. First, according to the identified fault type, locate the corresponding feature template in the feature library. Each feature template contains two key parameter sets: the propagation delay reference value matrix and the amplitude attenuation reference value matrix. The propagation delay reference value matrix records the propagation time of the zero-sequence current between any two adjacent detection points under different fault position conditions. The amplitude attenuation reference value matrix records the amplitude attenuation ratio of the zero-sequence current during propagation. These reference values are standard values obtained based on the statistical analysis of a large number of historical fault cases. The extracted feature template is used as a reference basis for subsequent fault location.

[0057] Calculate the actual propagation delay value and the actual amplitude attenuation value between each adjacent junction box according to the propagation time sequence characteristics. The actual propagation delay value is obtained through the time difference between the zero-crossing points of the zero-sequence current waveforms at adjacent junction boxes, and the actual amplitude attenuation value is obtained through the ratio of the maximum amplitudes of the zero-sequence currents at adjacent junction boxes.

[0058] Among them, the propagation time sequence characteristic is a characteristic describing the propagation time law of the zero-sequence current. The adjacent junction box is an adjacent connection point on the cable line. The zero-crossing point is the moment when the zero-sequence current waveform crosses the zero value. The maximum amplitude is the peak value of the zero-sequence current waveform. The time difference is the interval between the occurrence times of two events. Calculate the propagation parameters according to the collected zero-sequence current data. For each pair of adjacent junction boxes, first extract their zero-sequence current waveform data. By finding the zero-crossing points of the waveform, record the moments t1 and t2 of the first positive zero-crossing points at adjacent junction boxes, and calculate the time difference Δt = t2 - t1 to obtain the actual propagation delay value. Then identify the maximum amplitudes A1 and A2 of the two waveforms, and calculate the amplitude ratio k = A2 / A1 to obtain the actual amplitude attenuation value. Repeat the above calculation process for all pairs of adjacent junction boxes on the cable line to obtain a complete set of actual propagation parameters.

[0059] Calculate the matching degree between the actual propagation delay value and the actual amplitude attenuation value and the propagation delay reference value and the amplitude attenuation reference value in the zero-sequence current propagation characteristic template to obtain the matching degree value of each cable section.

[0060] Among them, the matching degree is an index to measure the similarity between the actual propagation parameter and the reference value. A cable section is the cable segment between two adjacent junction boxes. The matching degree calculation rule defines the method of parameter comparison. The matching degree value represents the degree of parameter matching. The calculated actual propagation parameter is matched with the reference value in the characteristic template. For each cable section, the matching degrees of two parameters, namely the propagation delay and the amplitude attenuation, are calculated separately. The formula for calculating the delay matching degree is: η1 = 1 - |Δt - Δtref| / Δtref, where Δtref is the reference delay value. The formula for calculating the amplitude attenuation matching degree is: η2 = 1 - |k - kref| / kref, where kref is the reference attenuation value. The comprehensive matching degree η = w1·η1 + w2·η2, where w1 and w2 are weight coefficients and w1 + w2 = 1. The comprehensive matching degree value of each cable section is recorded.

[0061] Determine the faulty cable section where the fault point is located according to the matching degree value.

[0062] Among them, the fault point is the specific location where the fault occurs. The faulty section is the cable segment containing the fault point. The positioning result is an estimate of the location of the fault point. The positioning accuracy represents the accuracy of fault point positioning. The faulty section is determined based on the calculated matching degree value. First, the matching degree values of all cable sections are sorted, and the section with the highest matching degree is most likely to contain the fault point. When the matching degree value of a certain section is significantly higher than that of other sections (usually more than 20% higher), then this section is determined as the faulty section. If there are multiple sections with similar matching degrees, other features (such as the magnitude of the fault current, the degree of waveform distortion, etc.) need to be combined for comprehensive judgment. After determining the faulty section, a more accurate position estimate can be further made within the section according to the matching degree value. After locating the faulty cable section, the location information of the faulty cable section is sent to the target client, and the target client includes mobile phones, computers, etc. used by the operation and maintenance personnel.

[0063] Next, the overall architecture of the grounding fault rapid positioning system for the coal mine power supply system provided in this embodiment will be described. Please refer to Figure 2 , which is a schematic diagram of the system deployment of the grounding fault rapid positioning method for the coal mine power supply system in the embodiment of this application.

[0064] This figure shows the overall layout of the grounding fault rapid positioning system for the coal mine power supply system. On the coal mine power supply line, multiple cable junction boxes are distributed, and a grounding current detection module is installed in each junction box. These detection modules perform digital carrier communication with the power monitoring system in the monitoring center through power cables. The grounding current detection module at each junction box is built-in with a zero-sequence current transformer to obtain the real-time zero-sequence current value at each junction box in the coal mine power supply system. When a fault occurs, the detection module uploads the collected data to the power monitoring system through digital carrier communication, providing data support for subsequent fault analysis and positioning.

[0065] The following shows the connection method of the grounding current detection module provided in this embodiment inside the cable junction box. Please refer to Figure 3 , which is a schematic diagram of the module connection inside a cable junction box for the method of quickly locating grounding faults in the coal mine power supply system according to the embodiment of the present application.

[0066] Figure 3 shows the specific connection method of the grounding current detection module in the cable junction box. The three-phase cable passes through the zero-sequence current mutual induction device, and the output side of the zero-sequence current mutual induction device is connected to the module for detecting the zero-sequence current in the three-phase cable. At the same time, the cable shielding layer and the wiring ground stake are connected to the module, which constitute a power line carrier communication channel to ensure data transmission between the detection module and the power monitoring system. The zero-sequence current mutual induction device is the key component for obtaining the real-time zero-sequence current value, and its accurate acquisition lays the foundation for subsequent fault judgment and location, while the power line carrier communication channel ensures that the fault data can be transmitted in a timely and accurate manner.

[0067] The following presents the voltage change situation during single-phase grounding faults provided in this embodiment. Please refer to Figure 4 , which is a voltage change diagram for the method of quickly locating grounding faults in the coal mine power supply system according to the embodiment of the present application.

[0068] Figure 4 (a) is a three-phase voltage change diagram when a single-phase grounding fault occurs. The abscissa is time (t / s), showing the time process before and after the fault occurs; the ordinate is voltage (U), intuitively presenting the amplitude change of the three-phase voltage. The three curves in the figure represent the voltages of phase A, phase B, and phase C respectively. During normal operation, the three-phase voltages are relatively stable; after a single-phase grounding fault occurs, the voltage of the faulty phase drops significantly, and the voltages of the non-faulty phases increase somewhat.

[0069] Figure 4 (b) is a zero-sequence voltage change diagram when a single-phase grounding fault occurs. The abscissa is also time (t / s), and the ordinate is the zero-sequence voltage (U0). Under normal conditions, the zero-sequence voltage is close to zero; at the moment when a single-phase grounding fault occurs, the zero-sequence voltage rises rapidly and remains in a specific fluctuation state for a period of time afterwards. By analyzing the change of the zero-sequence voltage, it can assist in judging the fault occurrence time.

[0070] The following further describes the more specific process of the method provided in this embodiment. Please refer to Figure 5 , which is another process schematic diagram for the method of quickly locating grounding faults in the coal mine power supply system according to the embodiment of the present application.

[0071] S501. Locate the faulty cable section where the fault point is located according to the propagation timing characteristics and the fault type.

[0072] Among them, the propagation time sequence feature refers to the time delay and attenuation characteristics shown when the zero-sequence current propagates on the cable line. The fault type represents the specific grounding fault form judged according to the electrical characteristics. The cable section refers to the part of the cable line between two adjacent junction boxes. The propagation characteristic template contains the reference values of the propagation time delay and the amplitude attenuation between adjacent detection points for the zero-sequence current under different fault types.

[0073] After obtaining the zero-sequence current propagation characteristic template corresponding to the current fault type from the preset fault feature library, calculate the actual propagation time delay value and the actual amplitude attenuation value. The actual propagation time delay value is obtained through the time difference between the zero-crossing points of the zero-sequence current waveforms at adjacent junction boxes. The specific calculation method is to determine the exact zero-crossing moment after interpolating the waveform data, and then calculate the time difference between the zero-crossing points of adjacent measurement points. The actual amplitude attenuation value is obtained through the ratio of the maximum amplitudes of the zero-sequence current at adjacent junction boxes, that is, the maximum values of the zero-sequence current detected at each measurement point during the fault are divided in turn according to the propagation direction. Calculate the matching degree between the calculated actual propagation time delay value and actual amplitude attenuation value and the reference values in the propagation characteristic template to obtain the matching degree values of each cable section. The matching degree calculation uses the weighted Euclidean distance method, considering the matching degrees of both the time delay feature and the attenuation feature. Finally, determine the faulty cable section where the fault point is located according to the matching degree value, and select the section with the highest matching degree as the fault location.

[0074] S502. Respectively obtain the waveform information of each cycle in the voltage waveform data and the current waveform data, and calculate the key characteristic parameters of each cycle according to the waveform information. The key characteristic parameters include the amplitude mutation point parameter, the phase mutation point parameter, and the waveform distortion parameter.

[0075] Among them, a cycle refers to a complete cycle of an alternating current electrical signal. The waveform information contains the complete change process of the signal within one cycle. The amplitude mutation point parameter represents the moment and the degree of change when the waveform amplitude changes significantly. The phase mutation point parameter represents the moment and the degree of change when the waveform phase changes significantly. The waveform distortion parameter is used to describe the deviation degree of the waveform from the standard sine wave.

[0076] Perform cycle segmentation on the collected voltage waveform data and current waveform data, and each cycle is used as an independent analysis unit. Determine the starting point of the cycle through the zero-crossing detection method, and divide the continuous waveform data into multiple single-cycle waveforms. Extract the characteristic parameters for each cycle waveform: First, calculate the instantaneous amplitude sequence of the waveform, detect the amplitude mutation points by setting a threshold, and record the moment of mutation and the amplitude change amount; then calculate the phase difference between adjacent sampling points, determine the phase mutation points through threshold detection, and record the moment of mutation and the phase change amount; finally, compare the actual waveform with the standard sine wave of the same frequency, and calculate the root mean square error to obtain the waveform distortion parameter. These characteristic parameters together constitute a complete description of the fault waveform characteristics.

[0077] S503. Divide the waveform data of each cycle into N equally-spaced sampling segments.

[0078] Among them, the equally-spaced sampling segment refers to several waveform segments obtained by dividing a period according to equal time intervals. The sampling point refers to the time point for discrete sampling in the continuous waveform. The value of N is the preset number of sampling segments, which is used to determine the sampling accuracy.

[0079] Divide the complete cycle waveform into N sampling segments equally in time. The time length of each sampling segment is equal, and is the cycle duration divided by N. For an AC signal with a power frequency of 60 Hz, one cycle is 20 ms. If N is taken as 100, the duration of each sampling segment is 0.2 ms. Within each sampling segment, determine the number and positions of the sampling points according to the sampling frequency. For example, when the sampling frequency is 10 kHz, there are 2 sampling points in a 0.2 ms sampling segment. Through this equally-spaced division method, it ensures the uniform sampling of the waveform characteristics and provides a standardized data basis for subsequent feature extraction and analysis. In practical applications, the selection of the value of N needs to balance the requirements of the calculation amount and the analysis accuracy, and usually it is selected between 60 and 200.

[0080] S504. Convert the key feature parameters into key feature codes, and the key feature codes are used to characterize the waveform information of each cycle.

[0081] Among them, the key feature parameters include the amplitude mutation point parameters, phase mutation point parameters, and waveform distortion parameters of the waveform. The key feature code is a digital sequence obtained by encoding the waveform feature parameters. The position distribution sequence represents the distribution positions of the key sampling points within the cycle. The deviation value sequence represents the difference between the actual waveform value at the key sampling point and the standard sine waveform value.

[0082] The process of converting the waveform features into feature codes first calculates the waveform change rate between adjacent sampling points within each sampling segment. The change rate is obtained by dividing the amplitude difference between adjacent sampling points by the sampling time interval. Mark the sampling points with a waveform change rate greater than the preset change rate threshold as key sampling points, record the relative positions of these key sampling points in the cycle, and generate a position distribution sequence. For each key sampling point, calculate the deviation between its actual waveform value and the standard sine waveform value at the same moment, and generate a deviation value sequence. Combine and encode the position distribution sequence and the deviation value sequence according to a predetermined rule to obtain the final key feature code. The encoding rule adopts binary encoding, encodes the position information and the deviation value respectively and then splices them to form a binary feature code with a fixed length.

[0083] In some embodiments, this step specifically includes the following steps: Divide the waveform data of each cycle into N equally-spaced sampling segments.

[0084] In this step, a cycle refers to a complete signal period. Waveform data is a sequence of sampled values that describes how the signal changes over time. An equally-spaced sampling segment is a time period obtained by evenly dividing the cycle. The length of a sampling segment is the number of sampling points contained in each sampling segment. The sampling interval is the time interval between adjacent sampling points. The waveform data of each cycle is processed by equally-spaced segmentation. First, determine the starting point and ending point of the cycle, and calculate the total number of sampling points M contained in the cycle. According to the preset number of segments N, calculate the length L of each sampling segment, where L = M / N (rounded down). Starting from the starting point of the cycle, divide a sampling segment every L sampling points, and the length of the last sampling segment is determined according to the actual remaining number of sampling points. The sampling segment numbers range from 0 to N - 1, and each sampling segment records the index position of its starting sampling point. This equally-spaced segmentation method ensures uniform sampling and analysis of the waveform.

[0085] Calculate the waveform change rate between adjacent sampling points in each sampling segment.

[0086] Among them, the waveform change rate represents the speed at which the waveform amplitude changes over time. Adjacent sampling points are two sampling points that are continuous in time. The change rate calculation is a numerical calculation method based on the amplitude difference of sampling points. The difference interval is the time step used when calculating the change rate. Calculate the local change rate for the waveform data within each sampling segment. Use the first-order difference method to calculate the change rate. For each pair of adjacent sampling points (i, i + 1) within the sampling segment, the calculation formula is: change rate = (amplitude[i + 1] - amplitude[i]) / sampling time interval. The unit of the change rate is amplitude / time. To eliminate the influence of noise, perform a moving average filter on the calculated change rate sequence, and the filter window length is 3 - 5 sampling points. Record the filtered change rate value at each sampling point to form the change rate sequence of this sampling segment.

[0087] Mark the sampling points with a waveform change rate greater than the preset change rate threshold as key sampling points.

[0088] Among them, the change rate threshold is the standard value for judging whether a sampling point is a key point. A key sampling point is a characteristic position where the waveform changes significantly. The marking process is the identification of the attributes of sampling points. The characteristic position refers to the time position of the key sampling point in the waveform. Traverse the change rate sequence of each sampling segment to identify key sampling points. Compare the change rate value of each sampling point with the preset threshold. When the absolute value of the change rate is greater than the threshold, mark this sampling point as a key sampling point. The marking uses a binary flag, where 1 represents a key sampling point and 0 represents an ordinary sampling point. At the same time, record the time position and amplitude information of each key sampling point. To avoid overly dense key points, when adjacent sampling points both meet the threshold condition, select the point with a larger absolute value of the change rate as the key sampling point.

[0089] Generate a position distribution sequence according to the distribution positions of the key sampling points in the cycle.

[0090] Among them, the position distribution sequence describes the spatial distribution of the key sampling points within the cycle. The distribution position is the relative time position of the key sampling points. Serialization is the process of converting position information into a sequence form. Position encoding is the digital representation of position information. Generate a position distribution sequence based on the marked key sampling points. First, count the number of key sampling points in each sampling segment to obtain density distribution information. Calculate the phase angle of each key sampling point relative to the starting point of the cycle, where the phase angle ranges from 0 to 360 degrees. Quantize the phase angle into an 8-bit binary number to form the position encoding. Combine the position encodings of all key sampling points in chronological order to generate a complete position distribution sequence. This encoding method not only retains the accurate position information of the key points but also realizes the compact representation of data.

[0091] Calculate the deviation between the actual waveform value and the standard sine waveform value at the key sampling points to generate a deviation value sequence.

[0092] Among them, the standard sine waveform is the reference waveform in the ideal state. The deviation value represents the difference between the actual waveform and the standard waveform. The actual waveform value is the actual signal amplitude obtained by sampling. The deviation value sequence records the deviation information of all key sampling points. Calculate the deviation between the waveform value and the standard value at the key sampling points. First, generate the standard sine waveform according to the fundamental frequency and amplitude parameters of the cycle. For each key sampling point, calculate the difference between its actual waveform value and the standard sine waveform value at the corresponding moment to obtain the deviation value. Normalize the deviation value, using the maximum amplitude of the actual waveform as the normalization reference. Organize the normalized deviation values of all key sampling points in chronological order to generate a deviation value sequence. This deviation calculation method can reflect the degree and characteristics of waveform distortion.

[0093] Combine and encode the position distribution sequence and the deviation value sequence to obtain the key feature code.

[0094] Among them, the feature code is the digital code describing the waveform features. Combination encoding is the encoding method of combining multiple feature sequences. The encoding rule defines the specific way of sequence combination. Feature compression is the encoding technology for reducing data redundancy. Generate the feature code by combining and encoding the position distribution sequence and the deviation value sequence. Adopt the segmented combination encoding method, and the encoding of each sampling segment consists of two parts: position encoding and deviation encoding. The position encoding uses 8-bit binary to represent the position information, and the deviation encoding uses 8-bit binary to represent the normalized deviation value. The two parts of encoding are combined by bit splicing to form a 16-bit segment feature code. Connect the feature codes of all sampling segments in sequence to generate the complete key feature code. This combination encoding method realizes the efficient representation and storage of waveform features.

[0095] S505. Use the preset standard feature code as the reference feature code, compare the key feature code with the reference feature code to obtain the difference degree data.

[0096] Among them, the preset standard feature code refers to the feature code corresponding to the standard waveform in the normal operating state. The reference feature code is used as the reference standard for comparison. The difference degree data represents the degree of difference between the actual feature code and the reference feature code. The coding distance refers to the number of different bits between two binary feature codes.

[0097] Calculate the difference degree between the key feature code obtained by converting the actual waveform and the preset standard feature code. The difference degree calculation uses the Hamming distance method, that is, count the number of bits with different values at the same position of the two feature codes. The specific calculation process is to perform an exclusive OR operation on the two feature codes, and the number of 1s in the result is the Hamming distance. At the same time, considering that the importance of different feature parameters is different, a weight coefficient is introduced when calculating the overall difference degree. Different weights are assigned to the differences in the position distribution sequence and the deviation value sequence respectively, and the weighted sum is used to obtain the final difference degree data. This weighted calculation method highlights the influence of important feature parameters and improves the accuracy of difference degree calculation.

[0098] S506. According to the difference degree data, screen out the weekly bands with a difference degree greater than the preset difference threshold, and mark the weekly bands as fault feature weekly bands.

[0099] Among them, the difference degree threshold is the preset difference degree judgment standard. The fault feature weekly band refers to a continuous weekly wave sequence showing obvious fault features. The difference statistic represents the cumulative effect of the difference degree data. The marking information includes the start position and end position of the fault feature weekly band.

[0100] Perform screening analysis on the calculated difference degree data, and mark the weekly waves with a difference degree greater than the preset threshold. To avoid misjudgment caused by sudden interference, the sliding window method is used to smooth the difference degree data. The window length is set to 3 weekly waves, and the average value of the difference degree within the window is calculated as the difference statistic of the central weekly wave. When the difference statistic of a certain weekly wave exceeds the preset threshold and the adjacent weekly waves also show large differences, the weekly wave and its adjacent abnormal weekly waves are jointly marked as a fault feature weekly band. For each identified fault feature weekly band, record its start weekly wave position, end weekly wave position and maximum difference degree value, and these information are used for subsequent fault analysis.

[0101] S507. Generate a fault feature data chain according to the time sequence arrangement of the fault feature weekly bands. The fault feature data chain includes the start time, peak time and recovery time of each fault feature weekly band.

[0102] Among them, the fault feature data chain is an ordered data structure containing information on multiple fault feature weekly bands. The starting moment refers to the time point when the fault feature starts to appear. The peak moment refers to the time point when the fault feature reaches its maximum degree. The recovery moment refers to the time point when the fault feature disappears. The chronological arrangement represents a data sequence organized in chronological order.

[0103] Perform chronological analysis and organization on the identified fault feature weekly bands. First, arrange all the fault feature weekly bands in chronological order of occurrence to form a preliminary chronological chain. For each weekly band, determine three critical moments by analyzing its difference degree change curve: the moment when the difference degree starts to exceed the threshold is recorded as the starting moment, the moment when the difference degree reaches the local maximum is recorded as the peak moment, and the moment when the difference degree returns below the threshold is recorded as the recovery moment. Organize this critical moment information in chronological order to construct a complete fault feature data chain. Each node in the data chain contains moment information and the corresponding difference degree value, and the nodes are connected through chronological relationships to form a complete data structure describing the fault development process.

[0104] S508. Generate a difference waveform spectrogram based on the difference degree data, and this difference waveform spectrogram is used to characterize the waveform spectrum distribution during the fault process.

[0105] Among them, the difference waveform spectrogram is a graph that visualizes the distribution characteristics of the difference degree data in the frequency domain. The spectrum distribution represents the energy distribution of the signal on different frequency components. The spectral component refers to the amplitude of the signal at a specific frequency. The spectral feature refers to the characteristic pattern shown in the spectrogram.

[0106] The process of converting the difference degree data into a spectral representation form uses the short-time Fourier transform method. First, segment the difference degree data sequence according to a fixed length, and multiply each segment of data by a Hanning window function to reduce spectral leakage. Perform a fast Fourier transform on each windowed data segment to obtain the spectral components within that time period. Arrange the spectra of adjacent time periods in chronological order to form a two-dimensional graph of the spectrum changing with time, that is, the difference waveform spectrogram. The horizontal axis of the spectrogram represents time, the vertical axis represents frequency, and the shade of the color in the graph represents the energy magnitude of different frequency components. This time-frequency analysis method can simultaneously reflect the changing laws of fault features in both the time domain and the frequency domain.

[0107] S509. According to the spectral distribution characteristics in the difference waveform spectrogram, divide the fault feature data chain into N sub-chains, and each sub-chain corresponds to an independent fault development stage.

[0108] Among them, the sub-chain is a continuous segment in the fault feature data chain that exhibits similar spectral characteristics. The fault development stage refers to each time period with different characteristics during the fault process. The spectral distribution characteristics include the distribution position and energy magnitude of the main frequency components. The division basis is the similarity judgment criterion based on spectral characteristics.

[0109] Segment the fault feature data chain by analyzing the spectral distribution characteristics in the differential waveform spectrogram. First, extract the main frequency components in the spectrogram, including the fundamental wave components and significant harmonic components at each moment. Calculate the similarity of the spectral distribution between adjacent time periods. The similarity calculation uses the cosine similarity method, that is, dividing the inner product of two spectral vectors by the product of their norms. When a significant change occurs in the similarity value, mark this moment as a segmentation point. Divide the fault feature data chain into multiple sub-chains according to the segmentation points, and each sub-chain corresponds to an independent stage in the fault development process. This segmentation method based on spectral features can accurately identify the characteristic changes in the fault development process.

[0110] S510. Statistically analyze the change trends of the position distribution sequence and deviation value sequence of the key sampling points in each sub-chain, and generate a fault evolution feature matrix.

[0111] Among them, the key sampling point refers to the sampling position that shows significant characteristics in the waveform. The position distribution sequence represents the time distribution of the key sampling points within a period. The deviation value sequence represents the difference between the actual value and the reference value at the key sampling points. The change trend refers to the change law of these sequences over time. The fault evolution feature matrix is a two-dimensional data structure that describes the changes of various characteristic parameters during the fault development process.

[0112] Statistically analyze the data in each sub-chain to extract fault evolution characteristics. First, calculate the position distribution of the key sampling points within each sub-chain, and obtain the position distribution sequence by counting the number of key sampling points in each sampling segment. Then calculate the deviation values at these key sampling points, and subtract the standard waveform value from the actual waveform value to obtain the deviation value sequence. Conduct trend analysis on the position distribution sequence and deviation value sequence, and calculate statistical features such as the mean, variance, and change rate of the sequences. Organize these statistical features into a matrix form according to a predetermined format. The rows of the matrix represent different characteristic parameters, the columns represent different time periods, and the matrix element values represent the statistical values of the corresponding characteristic parameters in specific time periods. This matrix representation method comprehensively describes the characteristic changes shown during the fault development process.

[0113] S511. Calculate the matching degree between the fault evolution feature matrix and the preset standard fault feature library to obtain matching degree data.

[0114] Among them, the preset standard fault feature library is a database containing the characteristic data of multiple typical fault cases. The matching degree calculation refers to comparing the similarity between the current fault characteristics and the standard characteristics in the feature library. The matching degree data represents the similarity degree between the current fault and each standard fault case. The feature vector is a data representation that converts the feature matrix into a one-dimensional array form.

[0115] Calculate the matching degree between the generated fault evolution feature matrix and the standard features in the feature library. First, convert the feature matrix into a feature vector. The conversion method is to expand the matrix by rows and perform normalization. For each standard fault case in the feature library, extract its feature vector and perform the same normalization. Use the cosine similarity method to calculate the similarity between the current fault feature vector and each standard feature vector. The calculation formula is the inner product of the two vectors divided by the product of their norms. Record the matching degree values of each standard case to form a complete matching degree data sequence. This matching method based on vector similarity can effectively measure the similarity between fault features.

[0116] S512. Select the K groups of historical fault data with the highest matching degree from the preset standard fault feature library according to the matching degree data.

[0117] Among them, the K groups of historical fault data refer to the K fault cases with the highest matching degree selected from the feature library. The matching degree threshold is used to screen the fault cases with a sufficiently high matching degree. The historical fault features include complete information such as fault type, development process, and treatment method. The similarity sorting is to arrange the fault cases in descending order according to the matching degree values.

[0118] Sort and screen the calculated matching degree data. First, sort all the standard fault cases in descending order according to the matching degree values to obtain the sorted fault case sequence. Select the K fault cases with the highest matching degree from the head of the sequence. The value of K is usually set to 3 to 5. For each selected fault case, extract its complete feature information, including the time, location, type, development process, treatment method, etc. when the fault occurred. These historical fault data provide an important reference basis for the analysis and treatment of the current fault. Selecting multiple groups of historical fault data with high matching degrees can analyze the features of the current fault from multiple perspectives and improve the accuracy of fault diagnosis.

[0119] S513. Analyze the position distribution sequence and deviation value sequence in the K groups of historical fault data, extract the common features, and generate a fault type feature template.

[0120] Among them, the common features refer to the same or similar features shown in multiple historical fault cases. The fault type feature template is a standard feature description constructed based on the common features. The position distribution feature represents the distribution law of key sampling points on the time axis. The deviation value feature represents the degree distribution of the waveform deviating from the normal state. Feature clustering is the process of classifying similar features.

[0121] Perform in-depth analysis and feature extraction on the selected K sets of historical fault data. First, align the position distribution sequences of each set of historical fault data, and use the dynamic time warping algorithm to eliminate the differences in time scales. Calculate the mean and standard deviation of the aligned sequences to obtain the statistical features of the position distribution. Perform the same alignment and statistical analysis process on the deviation value sequences. Conduct clustering analysis on the obtained statistical features, and use the K-means clustering method to identify the representative feature centers. Construct a fault type feature template based on the feature centers, and the template contains feature descriptions in two dimensions: position distribution and deviation value. This template generation method based on multi-case analysis can extract the essential features of the fault type.

[0122] S514. Calculate the fitness between the fault feature data chain of the current fault and the fault type feature template. When the fitness is greater than the preset fitness threshold, it is determined that the current fault type is the same as the fault type corresponding to the fault type feature template.

[0123] Among them, the fitness refers to the degree of coincidence between the current fault feature and the template feature. The fitness threshold is used to judge whether the fitting degree reaches an acceptable level. The fault feature data chain contains the complete feature information of the current fault. Feature matching refers to the process of comparing the current feature with the template feature.

[0124] Calculate the fitness between the feature data chain of the current fault and the fault type feature template. First, preprocess the current fault feature data chain, including data standardization and time alignment. Calculate the Euclidean distance between the current feature and the template feature. The smaller the distance, the higher the fitness. The specific calculation method is to calculate the distances of the position distribution feature and the deviation value feature respectively, and then perform weighted summation to obtain the overall fitness. The weight coefficient is determined according to the importance of the two types of features. Usually, the weight of the position distribution feature is higher. Compare the calculated fitness with the preset threshold. When the fitness exceeds the threshold, it is determined that the current fault is the same as the fault type corresponding to the template.

[0125] S515. According to the time sequence relationship of each sub-chain in the fault evolution feature matrix, establish a fault development state transition diagram, which is used to characterize the complete process of the fault from occurrence to development.

[0126] Among them, the state transition diagram is a directed graph structure that describes the state changes of the system. Nodes represent different fault states. Edges represent the transition relationships between states. The transition probability refers to the possibility of a state changing to another state. The state duration refers to the length of time the system maintains in a certain state.

[0127] Construct a complete fault development state transition diagram based on the fault evolution feature matrix. First, consider each sub-chain as an independent fault state, and analyze the feature change relationship between adjacent sub-chains. Determine the direction and intensity of state transition by calculating the feature difference between adjacent states. The transition between states is represented by a directed edge, and the weight of the edge is determined by the magnitude of the feature difference. For each state node, record the statistical value and duration of its feature parameters. Organize all state nodes and transition edges into a directed graph structure to form a complete state transition diagram. This graphical representation method intuitively shows the development process of the fault and the feature changes at each stage.

[0128] S516. Calculate the transition probability between adjacent states in the fault development state transition diagram to generate a state transition probability matrix.

[0129] Among them, the state transition probability represents the probability value of the system transitioning from one state to another. The state transition probability matrix is a two-dimensional matrix that describes the transition relationship between all states. Adjacent states refer to the states that appear successively before and after in the fault development process. The transition time interval refers to the time required for state conversion. The state duration represents the time the system persists in a certain state.

[0130] Perform probability calculation on the state transition relationship in the fault development state transition diagram. First, count the occurrence times and durations of each state node, and calculate the stability index of the state. For any two adjacent states, count the number of times of transition between them, and divide by the total occurrence times of the starting state to obtain the transition probability between these two states. Organize the transition probabilities between all state pairs into a matrix form in the order of state numbers. The rows and columns of the matrix represent the starting state and the target state respectively, and the matrix element value represents the corresponding transition probability. The diagonal elements represent the probability of the state remaining in the current state. This probability calculation method based on statistics can quantitatively describe the state conversion law in the fault development process.

[0131] S517. Update the fault type feature template according to the state transition probability matrix.

[0132] Among them, the fault type feature template is a standard model that describes the features of a specific type of fault. The state transition feature refers to the feature change shown during the state transition process. The template update rule defines how to adjust the template parameters according to the new transition probability. The feature weight refers to the importance degree of different feature parameters in the template.

[0133] Update the fault type feature template according to the calculated state transition probability matrix. First, analyze the main transition paths in the state transition probability matrix to identify the key state sequences during the fault development process. Calculate the weighted average of the state features in the key state sequences, where the weight coefficients are jointly determined by the stability of the states and the transition probabilities. Integrate the calculated weighted features with the original template features using a recursive update method, where the new feature value is equal to the weighted sum of the original feature value and the weighted features. The updated template not only retains the stability of the original features but also includes new state transition features, improving the adaptability and accuracy of the template.

[0134] S518. Detect the fitting degree between each sub-chain feature and the corresponding stage feature in the fault type feature template.

[0135] Among them, the sub-chain feature represents the set of features at a certain stage during the fault development process. The corresponding stage feature refers to the standard feature in the template corresponding to the timing position of the sub-chain. The fitting degree calculation rule defines the calculation method of feature similarity. The feature matching process refers to the process of comparing the actual feature with the standard feature.

[0136] Detect the fitting degree between the features of each sub-chain and the fault type feature template. First, determine the timing position of the sub-chain during the fault development process and extract the standard features of the corresponding stage from the feature template. Preprocess the sub-chain features and the standard features, including data standardization and time alignment. Calculate the Euclidean distance and cosine similarity between the two sets of features to comprehensively obtain the fitting degree index. The fitting degree calculation considers both the amplitude difference and the distribution form difference of the features. Perform the same fitting degree calculation process for each sub-chain to obtain a complete fitting degree sequence. This method of detecting the fitting degree section by section can evaluate the feature matching degree at each stage of the fault development in detail.

[0137] S519. When the fitting degree is lower than the preset fitting degree threshold, calculate the transition probability of the sub-chain feature in the state transition probability matrix.

[0138] Among them, the fitting degree threshold is the standard value for judging the feature matching degree. The sub-chain feature contains the complete feature information of a certain stage of the fault development. The state transition probability refers to the probability value of transitioning from one state to another. The feature deviation refers to the difference between the actual feature and the standard feature. The abnormal state transition indicates a state change that does not meet the expectation.

[0139] When the fitness of a certain sub-chain is detected to be lower than the preset threshold, it is necessary to analyze the transfer characteristics of this abnormal state. First, calculate the difference degree between the characteristics of this sub-chain and the characteristics of its adjacent front and rear sub-chains, and use the Euclidean distance method to quantify the degree of feature change. According to the difference degree value, find a similar transfer pattern in the state transition probability matrix, and calculate the state transition probability corresponding to the characteristics of this sub-chain. The specific calculation process is to match the sub-chain characteristics with the characteristics of each state in the matrix, determine the closest state, and then extract the transfer probability row vector of this state in the matrix. This probability-based analysis method can evaluate the development trend of abnormal states.

[0140] S520. Update the fault type feature template according to the transfer probability.

[0141] Among them, the transfer probability represents the likelihood of state transition. Template update means adjusting the template parameters according to the new feature information. Feature fusion is the process of combining new features with the original features. The update weight determines the influence degree of new features in the update process. Dynamic features refer to feature parameters that change over time.

[0142] Use the calculated transfer probability to update the fault type feature template. First, determine the weight coefficient for feature update according to the transfer probability. The higher the transfer probability of a feature, the greater the update weight assigned to it. For the feature parameters that need to be updated, use the weighted average method for feature fusion, and the new feature value is equal to the weighted sum of the original feature value and the current feature. The update process takes into account the temporal correlation of features, and the feature updates in adjacent time periods are passed through recursion. The updated template maintains the basic structure of the original features and at the same time contains the newly identified abnormal feature information.

[0143] S521. Perform real-time monitoring and fault warning on the waveform data based on the updated fault type feature template and the state transition probability matrix.

[0144] Among them, real-time monitoring means continuously analyzing and evaluating the waveform data. Fault warning is to issue a predictive warning before a fault occurs. The warning threshold is used to judge whether a warning signal needs to be issued. Feature trend refers to the change trend of feature parameters over time. The warning level represents the severity of the fault risk.

[0145] Perform real-time monitoring and warning based on the updated feature template and the transfer probability matrix. First, extract the features of the collected waveform data and calculate the fitness between the current features and the template features. At the same time, predict the development trend of the features according to the state transition probability matrix and calculate the probability of transitioning to the fault state. When the fitness is lower than the warning threshold or the transfer probability exceeds the warning threshold, trigger the warning mechanism. The warning information includes the current state evaluation result, the predicted development trend, and the recommended measures to be taken. This monitoring and warning method based on the feature template and the probability model can detect potential fault risks in a timely manner.

[0146] The coal mine power supply system in the embodiments of the present invention application will be described from the perspective of hardware processing. Please refer to Figure 6 , which is a schematic structural diagram of an entity device of the coal mine power supply system in the embodiments of the present application.

[0147] It should be noted that Figure 6 The structure of the coal mine power supply system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0148] As Figure 6 shown, the coal mine power supply system includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603, such as executing the methods described in the above embodiments. In the RAM 603, various programs and data required for system operation are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0149] The following components are connected to the I / O interface 605: an input section 606 including an audio input device, a button switch, etc.; an output section 607 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. The drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that the computer program read from it can be installed into the storage section 608 as needed.

[0150] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program contains a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, various functions defined in the present invention are executed.

[0151] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.

[0152] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings.

[0153] Specifically, the coal mine power supply system of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the coal mine power supply system grounding fault rapid location method provided in the above embodiment is implemented.

[0154] As another aspect, the present invention also provides a computer-readable storage medium, which may be included in the coal mine power supply system described in the above embodiments; or it may exist independently without being assembled into the coal mine power supply system. The above storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of the coal mine power supply system, the coal mine power supply system implements the method for quickly locating the grounding fault of the coal mine power supply system provided in the above embodiments.

[0155] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0156] As used in the above embodiments, depending on the context, the term "when..." can be interpreted to mean "if...", or "after...", or "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted to mean "if determining...", or "in response to determining...", or "when detecting (the stated condition or event)", or "in response to detecting (the stated condition or event)".

[0157] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disc that can store program codes.

Claims

1. A method for quickly locating grounding faults in a coal mine power supply system, characterized in that, Applied to a coal mine power supply system, the method includes: Obtaining real-time zero-sequence current values at each junction box in the coal mine power supply system; When the real-time zero-sequence current value at any junction box exceeds a preset current threshold, taking the sampling point at which the real-time zero-sequence current value first exceeds the preset current threshold as the fault point, and collecting voltage waveform data and current waveform data within a preset time period before and after the fault point; Calculating the distortion rate and phase offset of each phase voltage based on the voltage waveform data and the current waveform data; Determining the fault type based on the change trends of the distortion rate and phase offset; Performing a timing comparison analysis on the zero-sequence current values collected at each junction box at the fault moment to obtain the propagation timing characteristics of the zero-sequence current between each detection point; Locating the faulty cable section where the fault point is located according to the propagation timing characteristics and the fault type.

2. The method according to claim 1, wherein The step of locating the faulty cable section where the fault point is located according to the propagation timing characteristics and the fault type specifically includes: Obtaining a zero-sequence current propagation characteristic template corresponding to the fault type from a preset fault characteristic library, where the zero-sequence current propagation characteristic template includes reference values for the propagation delay and amplitude attenuation between adjacent detection points under different fault types; Calculating the actual propagation delay value and actual amplitude attenuation value between each adjacent junction box according to the propagation timing characteristics, where the actual propagation delay value is obtained through the time difference between the zero-crossing points of the zero-sequence current waveforms at adjacent junction boxes, and the actual amplitude attenuation value is obtained through the ratio of the maximum amplitudes of the zero-sequence currents at adjacent junction boxes; Calculating the matching degree between the actual propagation delay value and the actual amplitude attenuation value and the reference values for the propagation delay and amplitude attenuation in the zero-sequence current propagation characteristic template to obtain the matching degree values of each cable section; Determining the faulty cable section where the fault point is located according to the matching degree values.

3. The method according to claim 1, characterized in that After the step of locating the faulty cable section where the fault point is located according to the propagation timing characteristics and the fault type, the method further includes: Respectively obtaining the waveform information of each cycle in the voltage waveform data and the current waveform data, and calculating key characteristic parameters for each cycle according to the waveform information, where the key characteristic parameters include amplitude mutation point parameters, phase mutation point parameters, and waveform distortion parameters; Converting the key characteristic parameters into key characteristic codes, where the key characteristic codes are used to characterize the waveform information of each cycle; Using a preset standard characteristic code as the reference characteristic code, comparing the difference between the key characteristic code and the reference characteristic code to obtain difference degree data; According to the difference degree data, screening out the cycle bands with a difference degree greater than a preset difference threshold, and marking the cycle bands as faulty characteristic cycle bands; Generating a faulty characteristic data chain according to the timing arrangement of the faulty characteristic cycle bands, where the faulty characteristic data chain includes the start time, peak time, and recovery time of each faulty characteristic cycle band.

4. The method according to claim 3, wherein The step of converting the key characteristic parameters into key characteristic codes specifically includes: Dividing the waveform data of each cycle into N equally spaced sampling segments; Calculate the waveform change rate of adjacent sampling points in each of the sampling segments; Mark the sampling points with the waveform change rate greater than the preset change rate threshold as key sampling points; Generate a position distribution sequence according to the distribution positions of the key sampling points in the cycle; Calculate the deviation between the actual waveform value and the standard sine waveform value at the key sampling points to generate a deviation value sequence; Perform combined coding on the position distribution sequence and the deviation value sequence to obtain the key feature code; 5. The method according to claim 3, wherein After the step of locating the faulty cable section where the fault point is located according to the propagation timing feature and the fault type, the method further includes: Generate a differential waveform spectrogram according to the difference degree data, and the differential waveform spectrogram is used to characterize the waveform frequency spectrum distribution during the fault process; Divide the fault feature data chain into N sub-chains according to the frequency spectrum distribution characteristics in the differential waveform spectrogram, and each sub-chain corresponds to an independent fault development stage; Statistically analyze the change trends of the position distribution sequence and the deviation value sequence of the key sampling points in each sub-chain to generate a fault evolution feature matrix; Calculate the matching degree between the fault evolution feature matrix and a preset standard fault feature library to obtain matching degree data; Select the K groups of historical fault data with the highest matching degree from the preset standard fault feature library according to the matching degree data; Analyze the position distribution sequence and the deviation value sequence in the K groups of historical fault data, extract common features, and generate a fault type feature template; Calculate the fitting degree between the fault feature data chain of the current fault and the fault type feature template. When the fitting degree is greater than the preset fitting degree threshold, it is determined that the current fault type is the same as the fault type corresponding to the fault type feature template.

6. The method according to claim 5, wherein After the step of locating the faulty cable section where the fault point is located according to the propagation timing feature and the fault type, the method further includes: Establish a fault development state transition diagram according to the timing relationship of each sub-chain in the fault evolution feature matrix, and the state transition diagram is used to characterize the complete process of the fault from occurrence to development; Calculate the transition probability between adjacent states in the fault development state transition diagram to generate a state transition probability matrix; Update the fault type feature template according to the state transition probability matrix; 7. The method according to claim 6, wherein The step of updating the fault type feature template according to the state transition probability matrix specifically includes: Detect the fitting degree between each sub-chain feature and the corresponding stage feature in the fault type feature template; When the fitting degree is lower than the preset fitting degree threshold, calculate the transition probability of the sub-chain feature in the state transition probability matrix; Update the fault type feature template according to the transition probability; Perform real-time monitoring and fault warning on the waveform data based on the updated fault type feature template and the state transition probability matrix.

8. A coal mine power supply system, characterized in that, The coal mine power supply system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the coal mine power supply system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium, comprising instructions, characterized in that, When the instructions run on the coal mine power supply system, it causes the coal mine power supply system to execute the method described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product runs on the coal mine power supply system, it causes the coal mine power supply system to execute the method described in any one of claims 1-7.

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