Fault positioning method and device for power supply line of coal mining machine, medium and equipment
By combining variational mode decomposition and envelope derivative energy operator with pigeon flock algorithm optimization, the problem of accurate fault point identification in the power supply line of underground coal mining machine was solved, and efficient fault location was achieved in complex environments.
Patent Information
- Application Number
- CN202511888651.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-15
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies struggle to quickly and accurately identify the location and type of faults in the power supply lines of underground coal mining machines, especially in complex coal mine power grid environments. The traveling wave method has a small wavefront amplitude that is difficult to identify, resulting in large fault location errors. Methods based on training data lack robustness.
The variational mode decomposition algorithm is optimized using the pigeon flock algorithm to adaptively decompose the current traveling wave. The instantaneous energy distribution map is extracted by combining the envelope derivative energy operator to capture energy mutation points. Fault location is achieved by calculating the distance between the fault point and the beginning of the power supply line.
It improves the accuracy and robustness of fault location in complex noise environments, eliminates the need to rely on historical operating data, and reduces fault location errors.
Smart Images
Figure CN121703569A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of coal mine intelligent power grid information technology, and in particular relates to a fault positioning method, device, medium and equipment for a power supply line of a coal mining machine. BACKGROUND
[0002] The working environment in a coal mine is poor, and under the long-term influence of external force, moisture and chemical pollution, the cable is prone to insulation damage, and then an electric leakage accident occurs, which may even cause the nearby motor to burn out, resulting in the shutdown of large key basic equipment such as ventilation and drainage, and has a bad influence on the safety production of the coal mine and the safety of the miners. Therefore, fault positioning and intelligent protection of the coal mine power grid play a crucial role in helping the safe, efficient, green and intelligent mining of coal resources. The biggest challenge is how to quickly and accurately identify the fault point position and determine the fault type after the power supply line fails.
[0003] After the coal mine power grid fails, a large amount of transient information is generated, and the frequency components, oscillation characteristics and spectral energy distribution of the transient information generated by different faults are different. At present, the methods that can be used for fault positioning mainly include impedance method and traveling wave method. The traveling wave method has high positioning accuracy and is not affected by factors such as electric arc and transition resistance, and in recent years it has obtained greater market space. However, in actual application, the wave head amplitude is small and difficult to identify, which brings errors to fault distance measurement. In addition, there are some methods based on training and artificial intelligence, mainly including expert system method, fuzzy set method, Petri network, Bayesian network and the like. Such methods need to extract historical operation data under various fault conditions as feature vectors in advance. Unfortunately, the historical operation data of the coal mine power grid is often difficult to obtain, resulting in poor accuracy and robustness of the prediction model.
[0004] Therefore, there is an urgent need for a method that can improve the accuracy of fault positioning of the power supply line of the coal mining machine. SUMMARY
[0005] Therefore, it is necessary to provide a fault positioning method, device, medium and equipment for a power supply line of a coal mining machine to solve the above technical problems. The method can improve the accuracy of fault positioning of the power supply line of the coal mining machine.
[0006] The present application adopts the following technical solutions: The present application provides a fault positioning method for a power supply line of a coal mining machine, comprising: obtaining an actual current traveling wave of the power supply line of the coal mining machine when a single-phase ground fault occurs under a target working condition; The number of decompositions and the penalty factor of the variational mode decomposition algorithm are optimized by the pigeon swarm algorithm, and the actual current traveling wave is adaptively decomposed by the optimized variational mode decomposition algorithm to obtain multiple IMF components at the head end of the actual current traveling wave and multiple IMF components at the tail end of the actual current traveling wave. An IMF component at the head end closest to the waveform at the head end of the actual current traveling wave is selected from the multiple IMF components at the head end of the actual current traveling wave, and an IMF component at the tail end closest to the waveform at the tail end of the actual current traveling wave is selected from the multiple IMF components at the tail end of the actual current traveling wave. The instantaneous energy distribution diagrams of the IMF component at the head end and the IMF component at the tail end are extracted by the envelope derivative energy operator. The energy mutation points are captured from the instantaneous energy distribution diagrams of the IMF component at the head end and the IMF component at the tail end, and the time corresponding to the energy mutation points is determined; the time corresponding to the energy mutation points is the time when the actual current traveling wave reaches the head end or the tail end. According to the time when the actual current traveling wave reaches the head end, the time when the actual current traveling wave reaches the tail end, the total length of the power supply line of the coal mining machine, and the wave speed, the distance from the single-phase grounding fault point to the head end of the power supply line of the coal mining machine is obtained.
[0007] Preferably, the actual current traveling wave of the power supply line of the coal mining machine when a single-phase grounding fault occurs under the target working condition is obtained, specifically including: In the simulation model of the long-distance variable frequency power supply system of the coal mining machine, the distributed parameters of the mine cable of the power supply line of the coal mining machine, the length of the mine cable of the power supply line of the coal mining machine, the curve of the load torque of the coal mining machine changing with time, and the type of the target working condition are set, and simulation is performed to obtain the simulation waveform of the coal mining machine under the target working condition. The noise interference characteristics under the electromechanical coupling characteristics generated when the single-phase grounding fault occurs in the power supply line of the coal mining machine are obtained, and the simulation waveform under the target working condition is superimposed with the noise interference characteristics under the target working condition to obtain the actual current traveling wave under the single-phase grounding fault state.
[0008] Preferably, the noise interference characteristics under the electromechanical coupling characteristics generated when the single-phase grounding fault occurs in the power supply line of the coal mining machine are obtained, specifically including: A small-scale load simulation experiment platform is constructed; the small-scale load simulation experiment platform includes a frequency converter, a motor, and a dynamometer. The load torque in the curve of the load torque changing with time corresponding to the single-phase grounding fault of the power supply line of the coal mining machine is scaled proportionally, and the scaled result is determined as the command signal of the dynamometer. The dynamometer is controlled by a command signal to perform a loading experiment on the variable frequency drive system, obtaining a load torque curve and a motor current waveform with waveform distortion. The load torque curve represents the correspondence between time and the screw drum torque, as well as time and the cutting motor torque. The motor current waveform represents the relationship between time and the motor torque. q Shaft current and time and motor d The correspondence between shaft currents; a variable frequency drive system includes a motor and a frequency converter; Noise interference features are extracted from the distorted current waveform of the motor and the distorted torque curve of the load. The noise interference features include the time-varying features caused by the uneven distribution of coal and rock composition due to the drum cutting teeth during the operation of the coal mining machine, and the harmonic pollution features generated by the power electronic equipment.
[0009] Preferably, the method for obtaining the distribution parameters of the mining cable for the power supply line of the coal mining machine specifically includes: Obtain the structural parameters and material properties of the mining cable used in the power supply line of the coal mining machine; When using a monorail hoist to retract and extend the mining cable for the power supply line of the coal mining machine, take a single deformed structure of the mining cable for the power supply line of the coal mining machine. Based on structural and material property parameters, electromagnetic field analysis was performed on a single deformable structure using the electromagnetic field simulation software ANSYS to obtain the distribution parameters of the mining cable for the power supply line of the coal mining machine under the monorail hoisting and retraction method.
[0010] Preferably, the number of decompositions and the penalty factor of the variational mode decomposition algorithm are optimized using the pigeon flocking algorithm, specifically including: Initialize the parameters of the pigeon flocking algorithm and randomly initialize the positions and speeds of the pigeons; the position of the pigeon represents the number of decompositions and the penalty factor; The maximum value of the fuzzy entropy of all IMF components is used as the fitness function of the pigeon flock algorithm. The fitness value is calculated based on the fitness function. The position and speed of the pigeons are updated based on the fitness value and through the compass operator until the number of iterations of the compass operator is reached. When the number of iterations of the compass operator is reached, the fitness value calculated based on the fitness function is updated through the landmark operator to update the pigeon's position until the number of iterations of the compass operator is reached. The position corresponding to the number of iterations of the compass operator when the pigeon's position is updated by the landmark operator is determined as the optimized number of decompositions and the optimized penalty factor.
[0011] Preferably, the formula for calculating the distance from the single-phase grounding fault point to the beginning of the coal mining machine power supply line is: ; in, dThis refers to the distance from the point of single-phase grounding fault to the beginning of the power supply line. t 1 represents the moment when the actual traveling current wave reaches the beginning of the wave. t 2 represents the moment when the actual traveling current wave reaches its end. L The total length of the power supply line for the coal mining machine. v The wave velocity is given.
[0012] This invention provides a fault location device for a coal mining machine power supply line, comprising: The acquisition module is used to acquire the actual current traveling wave of the coal mining machine power supply line when a single-phase ground fault occurs under the target operating conditions; The decomposition module is used to optimize the number of decompositions and the penalty factor of the variational mode decomposition algorithm through the pigeon flock algorithm, and to adaptively decompose the actual current traveling wave through the optimized variational mode decomposition algorithm, so as to obtain multiple IMF components at the beginning of the actual current traveling wave and multiple IMF components at the end of the actual current traveling wave. The selection module is used to select the first IMF component that is closest to the waveform of the first end of the actual current traveling wave from multiple IMF components at the first end of the actual current traveling wave, and to select the last IMF component that is closest to the waveform of the last end of the actual current traveling wave from multiple IMF components at the last end of the actual current traveling wave. The extraction module is used to extract the instantaneous energy distribution maps of the first-end IMF component and the last-end IMF component respectively using the envelope derivative energy operator; The capture module is used to capture energy abrupt change points from the instantaneous energy distribution diagrams of the first and last IMF components and to determine the time corresponding to the energy abrupt change points; the time corresponding to the energy abrupt change points is the time when the actual current traveling wave arrives at the first or last end. The determination module is used to determine the distance from the start of the single-phase grounding fault point to the start of the coal mining machine power supply line based on the time when the actual current traveling wave arrives at the beginning, the time when the actual current traveling wave arrives at the end, the total length of the coal mining machine power supply line, and the wave velocity.
[0013] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for locating faults in the power supply line of a coal mining machine.
[0014] The present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned fault location method for the power supply line of the coal mining machine.
[0015] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects: By optimizing the number of decompositions and penalty factor of the variational mode decomposition algorithm using the pigeon flock algorithm, and by adaptively decomposing the actual current traveling wave using the optimized variational mode decomposition algorithm, multiple IMF components at the beginning and end of the actual current traveling wave are obtained respectively, overcoming the limitations of control parameters on the decomposition effect of the variational mode decomposition algorithm, and exhibiting strong adaptability; from the multiple IMF components at the beginning of the actual current traveling wave, a first-end IMF component that is closest to the waveform at the beginning of the actual current traveling wave is selected, and from the multiple IMF components at the end of the actual current traveling wave, a last-end IMF component that is closest to the waveform at the end of the actual current traveling wave is selected; by extracting the energy using the envelope derivative operator... This method extracts the instantaneous energy distribution maps of the actual current traveling wave at both the beginning and end of the IMF (Initial Motion Flow) component. The envelope derivative energy operator has a noise reduction effect, making the waveform abrupt changes of high-frequency modal components more prominent. Combining these two methods provides strong robustness against noise interference, even when weak fault characteristics are submerged. Energy abrupt change points are captured from the instantaneous energy distribution maps of the actual current traveling wave at both the beginning and end, and the corresponding times are determined. Based on the arrival times of the actual current traveling wave at the beginning and end, the total length of the coal mining machine power supply line, and the wave velocity, the distance from the single-phase grounding fault point to the beginning of the coal mining machine power supply line is obtained. This method does not require the collection of historical operating data. This method can improve the accuracy of fault location in the coal mining machine power supply line. Attached Figure Description
[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:
[0017] Figure 1 A schematic flowchart of a fault location method for a coal mining machine power supply line provided by the present invention; Figure 2 A schematic diagram of the electromagnetic field distribution of the cable under the monorail hoisting and retraction method provided by the present invention; Figure 3 This is a schematic diagram of the coal cutting process provided by the present invention; Figure 4 The load characteristic curve of the spiral drum provided by the present invention; Figure 5 The experimental results provided by the present invention are shown in the following diagrams: (a) is the load torque curve, and (b) is the motor current waveform. Figure 6The diagram shows the results of high-frequency noise extraction and analysis provided by the present invention, wherein (a) is the noise signal, (b) is the power spectral density distribution diagram, and (c) is the power spectral probability distribution diagram. Figure 7 The present invention provides a schematic diagram of the current waveform at the first end when a single-phase ground fault occurs, wherein (a) is the recorded current waveform and (b) is the waveform near the wavefront; Figure 8 A flowchart for VMD parameter optimization based on pigeon flocking algorithm provided by the present invention; Figure 9 The diagram shows the PIO-VMD decomposition results of the actual current traveling wave provided by the present invention, wherein (a) is the decomposition result of the beginning of the actual current traveling wave, and (b) is the decomposition result of the end of the actual current traveling wave. Figure 10 The comparison results of the fuzzy entropy after VMD parameter optimization provided by the present invention are shown in the figure. (a) The figure shows the fuzzy entropy after VMD parameter optimization at the beginning of the actual current traveling wave, and (b) The figure shows the fuzzy entropy after VMD parameter optimization at the end of the actual current traveling wave. Figure 11 The present invention provides a schematic diagram of the instantaneous EDO energy amplitude at both ends, wherein (a) is the DEO energy amplitude at the beginning of the actual current traveling wave, and (b) is the DEO energy amplitude at the end of the actual current traveling wave. Figure 12 This invention provides a structural diagram of a long-distance frequency conversion power supply for a coal mining machine. Figure 13 A schematic diagram illustrating the positioning errors of five methods under different fault distances provided by this invention; Figure 14 A schematic diagram of a fault location device for a coal mining machine power supply line provided by the present invention; Figure 15 A schematic diagram of a computer device for implementing a fault location method for the power supply line of a coal mining machine, provided by the present invention. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0019] Devices such as desktop computers, servers, and laptops are capable of executing the solutions of this invention. For ease of explanation, the following description will focus on servers as the executing entity.
[0020] In recent years, researchers have proposed various detection methods for recognizing traveling wave fronts. Mathematical Morphology (MM) is an algorithm based on integral geometry and random sets. It utilizes mathematical morphological gradients to detect the distortion of traveling waves and extract wavefront features. The application of MM in fault detection and location of voltage source converter high-voltage direct current (VSC-HVDC) transmission lines was investigated. However, selecting structural elements with suitable shape and size is quite difficult when applying specific mathematical morphology algorithms. Inappropriate selection can significantly reduce the algorithm's robustness to noise and lead to low computational efficiency and slowed operation. Other classic methods include the DS filter, but the differentiation and smoothing of the signal by the DS filter alters the signal's amplitude and phase, potentially causing errors in the calculation of weak fault characteristics.
[0021] Transforming fault signals is an effective method for traveling wave front detection. A method based on Discrete Wavelet Transform (DWT) to detect the arrival time and polarity of the wavefront is proposed, which can identify the reflected wave at the fault point. However, DWT typically uses a sampling process of less than 2 times, resulting in the loss of one sample per calculation and causing discontinuities in fault characteristics. Maximum Overlap Discrete Wavelet Transform (MODWT) is an improved form of DWT. A method for fault signal analysis using MODWT during stable power fluctuations is proposed and experimentally verified under atypical operating conditions and with a wide range of fault parameters. However, wavelet transform requires selecting an appropriate mother wavelet and setting a feasible number of decomposition levels, lacking adaptability. Moreover, wavelet transform can cause fluctuations and distortions in wavelet coefficients, affecting the accuracy and stability of traveling wave front detection. Based on the Hilbert-Huang Transform (HHT), the wavefront time and the specific instantaneous amplitude at that time are determined, forming a fault location algorithm for high-voltage direct current transmission lines. Hilbert-Huang transform (HHT) is used to identify instantaneous frequency abrupt changes in traveling waves, enabling fault location in overhead lines, cables, and cable-overhead line hybrid lines. However, the Hilbert-Huang transform suffers from difficulties in eliminating mode mixing and endpoint effects, and requires complex recursive calculations.
[0022] In addition, there are some training- and artificial intelligence-based methods, mainly including expert system methods, fuzzy set methods, Petri networks, and Bayesian networks. These methods require pre-extracting historical operating data under various fault scenarios as feature vectors. Unfortunately, historical operating data for coal mine power grids is often difficult to obtain, resulting in insufficient feature diversity and strong subjectivity, leading to poor accuracy and robustness of the prediction models.
[0023] To address the problems of low adaptability, difficulty in traveling wave front identification, and inaccurate line parameter calculation in existing research and field applications in coal mines, this invention proposes a traveling wave front detection method based on Pigeon Inspired Optimization - Variational Mode Decomposition - Envelope Derivative Operator (PIO-VMD-EDO). This method is also a fault location method for the power supply line of a coal mining machine, and is applied to the power supply line of the coal mining machine's cutting section. By extracting the noise signal characteristics under the coupling effect of the nonlinear load of the coal mining machine and the high-order harmonics of the frequency converter, and combining this with the actual field calculation of the distributed parameters of the coal mining machine's power supply line, the current waveform during a single-phase ground fault is simulated. Furthermore, the Pigeon Inspired Optimization (PIO) algorithm is employed to optimize the control parameters of the Variational Mode Decomposition (VMD) algorithm. Combined with the Envelope Derivative Operator (EDO), the dominant frequency component of the fault traveling wave under strong noise background is calibrated and enhanced, accurately identifying the wavefront arrival time and achieving precise location of single-phase grounding faults. Finally, comparisons with other methods verify the advantages of the proposed method in wavefront detection and fault location.
[0024] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0025] Figure 1 This is a schematic flowchart of a fault location method for a coal mining machine power supply line according to the present invention, which specifically includes the following steps: S101: Obtain the actual current traveling wave of the coal mining machine power supply line when a single-phase ground fault occurs under the target operating conditions.
[0026] In an exemplary embodiment, obtaining the actual current traveling wave of the coal mining machine power supply line under a single-phase ground fault in the target operating condition specifically includes: setting the distribution parameters of the mining cable of the coal mining machine power supply line, the length of the mining cable of the coal mining machine power supply line, the curve of the load torque of the coal mining machine changing with time, and the target operating condition type in the simulation model of the coal mining machine long-distance frequency conversion power supply system, and performing simulation to obtain the simulation waveform of the coal mining machine under the target operating condition; obtaining the noise interference characteristics under the electromechanical coupling characteristics generated when the coal mining machine power supply line experiences a single-phase ground fault, and superimposing the simulation waveform under the target operating condition with the noise interference characteristics under the target operating condition to obtain the actual current traveling wave under the single-phase ground fault state.
[0027] In an exemplary embodiment, the method for obtaining the distribution parameters of the mining cable for the power supply line of the coal mining machine specifically includes: obtaining the structural parameters and material property parameters of the mining cable for the power supply line of the coal mining machine; When using a monorail hoist to retract and extend the mining cable for the power supply line of the coal mining machine, a single deformed structure of the mining cable is taken. Based on the structural parameters and material property parameters, electromagnetic field analysis is performed on the single deformed structure using the electromagnetic field simulation software ANSYS to obtain the distribution parameters of the mining cable for the power supply line of the coal mining machine under the monorail hoist retraction and extension method.
[0028] Specifically, the power supply line for the coal cutting section of the coal mining machine uses MYPT 1.9 / 3.3 3×185 mining cable. Electromagnetic field analysis was performed on it using ANSYS to obtain the distributed parameters of the straight MYPT 1.9 / 3.3 3×185 mining cable. The distributed parameters of the straight MYPT 1.9 / 3.3 3×185 mining cable are shown in Table 1.
[0029] Table 1 During the advancement of the longwall mining face, a monorail crane is used for cable deployment and retraction. A single deformed structure is analyzed, and its electromagnetic field distribution is characterized using ANSYS. The electromagnetic field distribution is as follows: Figure 2 As shown, Figure 2 This is an electromagnetic field distribution diagram of the cable under the monorail hoisting and retraction method provided by the present invention. After the cable is bent and deformed, its electromagnetic field distribution will change, and the capacitance, resistance and inductance values will also change. Table 2 shows the cable distribution parameters under the monorail hoisting and retraction method.
[0030] Table 2 The coal cutting process of the coal mining machine is as follows Figure 3As shown, due to the complex structure, uneven mechanical properties, and brittle fracture of coal and rock masses, the spiral drum will bear a large reaction force and impact load, which will increase the harmonic content in the coal mine power grid, leading to voltage fluctuations and flicker phenomena, affecting the power quality and operational safety of the power supply system.
[0031] Based on the structural parameters of the MG650 / 1630-WD electric traction coal mining machine, its spiral drum diameter is 2300mm, and the cutting teeth are arranged sequentially. When the coal mining machine cuts hard coal seams, the cutting speed of the spiral drum is set to 30r / min, and the traction speed of the coal mining machine is 6m / min. The stress analysis of the spiral drum is fully considered, taking into account factors such as mine pressure, cutting tooth type, and coal and rock physical properties. Figure 4 This is a schematic diagram of the load characteristic curve of the spiral drum provided by the present invention. Figure 4 It can be seen that the load torque of the spiral drum exhibits fluctuating changes, with time-varying and periodic characteristics.
[0032] A simulation model of a long-distance variable frequency power supply system for a coal mining machine was built using Matlab or Simulink, and based on... Figure 4 The load torque shown and the distributed parameters shown in Table 2 are set accordingly, and the simulated waveform output by the software simulation is in perfect form.
[0033] This invention focuses on fault location research in the power supply line of a coal mining machine. While existing fault detection methods are applicable to ground power distribution networks and high-voltage power transmission lines, their application to the power supply line of the coal mining machine's cutting section faces numerous challenges, primarily due to the following reasons related to the applicability of the scenario:
[0034] (1) During the operation of the coal mining machine, the uneven distribution of the drum cutting teeth and coal and rock composition leads to the load having large disturbance, strong impact and time-varying characteristics, which changes the time domain characteristics of the traveling wave in the power supply line and exhibits time-varying characteristics consistent with nonlinear load, affecting the identification accuracy of the traveling wave head.
[0035] (2) The widespread use of power electronic equipment has introduced new harmonic pollution into the already complex coal mine power grid. Against this backdrop, fault location and power quality issues are becoming increasingly intertwined. The weak characteristics and high-frequency transient features of faults are becoming more pronounced, increasing the difficulty of fault diagnosis and handling, and reducing the performance of fault location and relay protection.
[0036] (3) As the fully mechanized mining face moves forward, the cable will deform during the winding and unwinding operations, resulting in coils, coils, bends, etc., which will cause the actual distribution parameters of the power supply line to differ from the ideal situation. According to the traveling wave reflection principle, the uncertainty of the line parameters will inevitably bring errors to the fault location.
[0037] In an exemplary embodiment, obtaining the noise interference characteristics under electromechanical coupling when a single-phase ground fault occurs in the power supply line of the coal mining machine specifically includes: constructing a small-scale load simulation experimental platform; the small-scale load simulation experimental platform includes a frequency converter, a motor, and a dynamometer; scaling the load torque in the curve of the load torque changing with time corresponding to the single-phase ground fault in the power supply line of the coal mining machine proportionally, and determining the proportional scaling result as the command signal of the dynamometer; controlling the dynamometer through the command signal to enable the dynamometer to perform a loading experiment on the frequency converter drive system, obtaining a load torque curve with waveform distortion and a motor current waveform with waveform distortion; the load torque curve represents the correspondence between time and the torque of the screw drum and the torque of the cutting motor; the motor current waveform represents the correspondence between time and the motor. q Shaft current and time and motor d The correspondence between shaft currents; the variable frequency drive system includes the motor and the frequency converter; noise interference characteristics are extracted from the motor current waveform with waveform distortion and the load torque curve with waveform distortion; the noise interference characteristics include the time-varying characteristics caused by the uneven distribution of coal and rock composition during the operation of the coal mining machine drum cutting teeth and the harmonic pollution characteristics generated by the power electronic equipment.
[0038] Specifically, to study the noise characteristics of the inverter output waveform under the load characteristics of a coal mining machine, a small-scale load simulation experimental platform was built. The motor and inverter constituted a variable frequency drive system, while the dynamometer was used to simulate the load of the variable frequency drive. Figure 4 The load torque shown is scaled proportionally and used as the command signal for the dynamometer to conduct a loading experiment on the variable frequency drive system. The measured results are as follows: Figure 5 As shown, Figure 5 Figure (a) shows the load torque curve. Figure 5 Figure (b) shows the motor current waveform. (From...) Figure 5 We can obtain, q The shaft current changes in line with the load torque, and is a non-ideal signal with a certain degree of waveform distortion. A large amount of high-frequency noise and the nonlinear load exert a combined effect, causing the local waveform to exhibit strong randomness and non-stationarity. When a power supply line fault occurs, this phenomenon will overlap with the transient signal of the traveling wave front, making the identification of wave front characteristics more difficult and directly affecting the accuracy of fault location.
[0039] Noise signals were extracted from the current waveform, and power spectrum analysis was performed using the autocorrelation method. The results are as follows: Figure 6 As shown in Figure (c), Figure 6 Figure (a) shows the noise signal. Figure 6 Figure (b) shows the power spectral density distribution. Figure 6 Figure (c) in the diagram is the power spectrum probability distribution.
[0040] Because the current waveform output by the software simulation is in a perfect form, it does not take into account the electromagnetic compatibility and anti-interference capabilities of power electronic equipment. Therefore, according to Figure 6 The power spectrum analysis results shown in Figure (b) construct a Gaussian white noise signal consistent with the experimental results and superimpose it with the ideal simulation waveform.
[0041] A single-phase ground fault occurs 1.8 km from the beginning of the power supply line, with a time interval of 0.8 s. Waveform recording devices are installed on the output side of the frequency converter (beginning) and the input side of the motor (end) of the power supply line. The waveform of the current at the beginning of the line is recorded at a sampling rate of 10 MHz under normal load conditions at the motor's rated frequency of 50 Hz. Figure 7 As shown. Figure 7 Figure (a) shows the recorded current waveform. Figure 7 Figure (b) shows the waveform near the wavefront. (From...) Figure 7 As shown in Figure (b), the power supply system contains power electronic equipment, which, combined with the nonlinear load of the coal mining machine's cutting section, alters the time-domain characteristics of the current waveform. Because the traveling wavefront amplitude is small, the fault characteristics are submerged in the strong interference of high-order harmonics and high-frequency noise, causing traditional wavefront detection and fault location methods to fail.
[0042] S102: The number of decompositions and the penalty factor of the variational mode decomposition algorithm are optimized by the pigeon flock algorithm, and the actual current traveling wave is adaptively decomposed by the optimized variational mode decomposition algorithm to obtain multiple IMF components at the beginning and the end of the actual current traveling wave.
[0043] In an exemplary embodiment, optimizing the number of decompositions and the penalty factor of the variational mode decomposition algorithm using a pigeon flocking algorithm specifically includes: initializing the parameters of the pigeon flocking algorithm and randomly initializing the positions and speeds of the pigeons; the position of the pigeon represents the number of decompositions and the penalty factor; using the maximum fuzzy entropy of all IMF components as the fitness function of the pigeon flocking algorithm, and calculating the fitness value based on the fitness function, updating the position and speed of the pigeons based on the fitness value and through a compass operator until the number of iterations of the compass operator is reached; when the number of iterations of the compass operator is reached, updating the position of the pigeons based on the fitness value calculated by the fitness function and through a landmark operator until the number of iterations of the compass operator is reached; the position corresponding to the point where the position of the pigeons updated through the landmark operator reaches the number of iterations of the compass operator is determined as the optimized number of decompositions and the optimized penalty factor.
[0044] To further clarify the fault characteristics of the power supply line, the Variational Mode Decomposition (VMD) algorithm was used to decompose the fault current waveform. Unlike the traditional recursive approach of Empirical Mode Decomposition (EMD), VMD transforms the signal decomposition into an optimization problem of constrained models, demonstrating excellent characteristics in reflecting the singularity features of the signal and exhibiting high decomposition efficiency.
[0045] Specifically, the number of decompositions K and penalty factor α The choice of parameters affects the decomposition effect of VMD, and the selection of parameters is irregular. To select the optimal parameter combination so that VMD can extract rich feature information, the Pigeon-Inspired Optimization (PIO) algorithm is used to intelligently match the VMD control parameters. The Pigeon-Inspired Optimization algorithm is a swarm intelligence optimization algorithm that has demonstrated good performance in many fields such as UAV formation, control parameter optimization, image processing, and medical imaging analysis and detection, showing significant advantages in convergence speed and search efficiency.
[0046] The VMD parameter optimization process based on the pigeon flocking algorithm is as follows: Figure 8 As shown, during VMD parameter optimization, the number of decompositions K and the penalty factor are used. α As the independent variable, the maximum value of the fuzzy entropy (FE) of all IMF components is used as the fitness function of the pigeon flock algorithm, thereby obtaining the optimal combination of the number of decompositions and the penalty factor.
[0047] The PIO-VMD method is used to decompose the line mode components of the actual current traveling wave, and the IMF components are obtained as follows: Figure 9 As shown. At this point, the optimal parameter combination for VMD is: K=5. α =4379.
[0048] Under the same fault waveform, the parameter range is taken as follows: K∈(3,7). α ∈(2000,5000), analyze the sensitivity of control parameters to the VMD decomposition effect. The comparison results of fuzzy entropy after VMD parameter optimization are shown below. Figure 10 As shown, Figure 10 In the IMF component, FE refers to the maximum fuzzy entropy value. Compared to other parameter combinations, the FE value after VMD optimization using the PIO algorithm is the highest, which can maximize the integrity of fault information.
[0049] S103: Select the first IMF component from the multiple IMF components at the beginning of the actual current traveling wave that is closest to the waveform at the beginning of the actual current traveling wave, and select the last IMF component from the multiple IMF components at the end of the actual current traveling wave that is closest to the waveform at the end of the actual current traveling wave.
[0050] Specifically, such as Figure 9 As shown, from the multiple IMF components at the beginning of the actual current traveling wave, the IMF component that is closest to the waveform at the beginning of the actual current traveling wave is selected as the beginning IMF component, and from the multiple IMF components at the end of the actual current traveling wave, the IMF component that is closest to the waveform at the end of the actual current traveling wave is selected as the end IMF component. Figure 9 The traveling wave in this invention refers to the actual current traveling wave.
[0051] S104: Extract the instantaneous energy distribution maps of the first-end IMF component and the last-end IMF component using the envelope derivative energy operator.
[0052] Compared to the Teager Energy Operator (TEO), the Envelope Derivative Operator (EDO) uses the envelope of the signal derivative instead of frequency-weighted energy measurement, allowing for better tracking of signal and system energy changes. It also possesses non-negativity and noise resistance. The IMF components after PIO-VMD decomposition are processed by EDO to detect and amplify instantaneous energy abrupt changes, thereby capturing abrupt changes in the energy spectrum and accurately calibrating the traveling wavefront. The instantaneous EDO energy amplitude is shown below. Figure 11 As shown.
[0053] S105: Capture energy abrupt change points from the instantaneous energy distribution diagrams of the first and last IMF components and determine the time corresponding to the energy abrupt change points; the time corresponding to the energy abrupt change points is the time when the actual current traveling wave arrives at the first or last end.
[0054] from Figure 11 The observed arrival time of the traveling wave front at the beginning of the power supply line was point 5156, recorded as follows: t 1. The point reaching the end of the power supply line is point 5145, and the time is recorded as follows: t 2.
[0055] S106: Based on the time when the actual current traveling wave reaches the beginning of the line, the time when the actual current traveling wave reaches the end of the line, the total length of the coal mining machine power supply line, and the wave velocity, the distance from the single-phase grounding fault point to the beginning of the coal mining machine power supply line is obtained.
[0056] In an exemplary embodiment, the formula for calculating the distance from the single-phase ground fault point to the beginning of the coal mining machine power supply line is as follows: (1); in, d This refers to the distance from the point of single-phase grounding fault to the beginning of the power supply line. t 1 represents the moment when the actual traveling current wave reaches the beginning of the wave. t 2 represents the moment when the actual traveling current wave reaches its end. L The total length of the power supply line for the coal mining machine. v The wave velocity is given.
[0057] Specifically, t 1 and t 2. Substitute into formula (1). v In one embodiment of the present invention, the wave velocity is 98% of the speed of light. Using formula (1), the distance from the fault point to the beginning of the line can be obtained. d It is 1845 m, with an error of approximately The resolution is 45 m, and the capability to achieve a 10 MHz resolution also needs to be considered.
[0058] In one exemplary embodiment, the present invention provides as follows Figure 12 The diagram shows a long-distance frequency conversion power supply structure for a coal mining machine. (Example:) Figure 12 As shown, the voltage transmitted by the underground central substation is 10 kV, and the voltage output by the mobile substation is 3.3 kV. The high-voltage frequency converter is used to supply power to the cutting motor of the coal mining machine over a long distance of 3420 m.
[0059] To further verify the effectiveness of the traveling wave front detection method based on PIO-VMD-EDO proposed in this invention, simulation experiments were conducted with different fault distances. The results are shown in Table 3, which presents the fault location results at different fault distances. As can be seen from Table 3, the maximum relative error of the proposed method does not exceed 2.5%, demonstrating high fault location accuracy, and it is unaffected by the fault distance.
[0060] Table 3 Currently, the sampling frequency range of the traveling wave method is generally 1 to 10 MHz. Considering the relatively short power supply distance in coal mines and the fact that harmonic mitigation is not as effective as that of the surface power grid, a higher sampling frequency is compared to obtain more accurate wavefront identification. Sampling frequencies from 1 MHz to 20 MHz were used to simulate fault scenarios, and the ranging errors are shown in Table 4.
[0061] Table 4 As shown in Table 4, the fault location error rate decreases significantly as the sampling frequency increases from 1 MHz to 10 MHz. This is because a sufficiently high sampling rate is required to record the arrival times of extremely dense reflected traveling waves in short-distance fault conditions. When the sampling frequency increases from 10 MHz to 20 MHz, the improvement in ranging accuracy is not significant. In summary, a sampling frequency of 10 MHz is sufficient to meet the accuracy requirements of the method for detecting weak wavefronts in this invention. If a sampling frequency higher than 10 MHz is used, the cost of acquiring, storing, and processing larger-scale data in practical engineering applications will be extremely high.
[0062] To verify the superiority of the proposed method in wavefront detection and fault location, the proposed traveling wavefront detection method was compared with Discrete Gaussian Wavelet Transform (DWT), Maximum Overlap Discrete Wavelet Transform (MODWT), Hilbert-Huang Transform (HHT), and Empirical Mode Decomposition (EMD). Taking a single-phase ground fault occurring 2000m from the fault head as an example, the decomposition effect of different wavefront detection methods was evaluated using signal-to-noise ratio (SNR) and Pearson correlation coefficient (PCC). The decomposition results are shown in Table 5.
[0063] Table 5 As shown in Table 5, the method of the present invention has the highest SNR and the PCC is closer to 1, indicating that the method of the present invention can not only reduce noise to the greatest extent, but also retain the useful information of the original signal, and reduce the interference of noise on feature extraction and wavefront recognition. Figure 13 These represent the ranging errors of the five methods at different fault distances, such as... Figure 13 As shown, the method provided by this invention has the smallest relative error.
[0064] (0) Because the coal mine power grid contains a large number of nonlinear loads and power electronic equipment, and generates stable high-order harmonics and high-frequency noise during operation, it overwhelms the weak transient fault characteristics after a single-phase ground fault occurs. At the same time, the distributed parameters of the power supply line will change during the cable retraction and extension operation, making fault location more difficult.
[0065] (2) The VMD parameters are optimized by using PIO, with FE as the optimization target of PIO, and the best combination of VMD control parameters is determined adaptively. Then, the traveling wave head is detected and enhanced according to EDO. The two are used together, and even when the fault characteristics are not obvious, it has good robustness against noise interference, effectively improving the fault location accuracy. The maximum relative error does not exceed 2.5%, and it is not affected by the fault distance.
[0066] (3) The method of the present invention was applied to the fault location of the power supply line of the coal cutting section. By comparing it with DWT, MODWT, HHT and EMD, and using the SNR and PCC of the five methods as evaluation indicators, its superiority in wave head detection and fault location was verified, further highlighting that the method has high noise robustness.
[0067] However, in practical engineering applications, the propagation speed of traveling waves is easily affected by conductor conductivity, insulation, and environmental factors such as geography, climate, and weather, which can also affect fault location accuracy. Therefore, how to limit the ranging error caused by the uncertainty of traveling wave velocity is one of the important research directions for the future.
[0068] When applying the fault location method for the power supply line of the coal mining machine provided by this invention, it is not necessary to rely on... Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this invention does not impose any restrictions on it.
[0069] The above describes a fault location method for a coal mining machine power supply line according to one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding fault location device for a coal mining machine power supply line, such as... Figure 14 As shown.
[0070] Figure 14 A schematic diagram of a fault location device for a coal mining machine power supply line provided by the present invention includes: The acquisition module 1401 is used to acquire the actual current traveling wave of the coal mining machine power supply line when a single-phase ground fault occurs under the target operating conditions.
[0071] The decomposition module 1402 is used to optimize the number of decompositions and the penalty factor of the variational mode decomposition algorithm through the pigeon flock algorithm, and to adaptively decompose the actual current traveling wave through the optimized variational mode decomposition algorithm to obtain multiple IMF components at the beginning and the end of the actual current traveling wave.
[0072] Selection module 1403 is used to select, from multiple IMF components at the beginning of the actual current traveling wave, a first-end IMF component that is closest to the waveform at the beginning of the actual current traveling wave, and from multiple IMF components at the end of the actual current traveling wave, a last-end IMF component that is closest to the waveform at the end of the actual current traveling wave.
[0073] Extraction module 1404 is used to extract the instantaneous energy distribution maps of the first-end IMF component and the last-end IMF component respectively using the envelope derivative energy operator.
[0074] The capture module 1405 is used to capture energy abrupt change points from the instantaneous energy distribution diagrams of the first-end IMF component and the last-end IMF component, and determine the time corresponding to the energy abrupt change point; the time corresponding to the energy abrupt change point is the time when the actual current traveling wave arrives at the first or last end.
[0075] The determination module 1406 is used to determine the distance from the start of the single-phase grounding fault point to the start of the coal mining machine power supply line based on the time when the actual current traveling wave arrives at the beginning, the time when the actual current traveling wave arrives at the end, the total length of the coal mining machine power supply line, and the wave velocity.
[0076] Specific limitations regarding the fault location device for the coal mining machine's power supply line can be found in the above-mentioned limitations on the fault location method for the coal mining machine's power supply line, and will not be repeated here. Each module in the aforementioned fault location device for the coal mining machine's power supply line can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0077] The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The provided method for locating faults in the power supply lines of coal mining machines.
[0078] The present invention also provides Figure 15 The schematic diagram of the computer device shown is as follows: Figure 15 As shown, at the hardware level, this computer device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above. Figure 1 The provided method for locating faults in the power supply lines of coal mining machines.
[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.
Claims
1. A method for fault location in the power supply line of a coal mining machine, characterized in that, include: Obtain the actual current traveling wave of the coal mining machine's power supply line when a single-phase ground fault occurs under target operating conditions; The number of decompositions and the penalty factor of the variational mode decomposition algorithm are optimized by the pigeon flock algorithm, and the actual current traveling wave is adaptively decomposed by the optimized variational mode decomposition algorithm to obtain multiple IMF components at the beginning and the end of the actual current traveling wave. From the multiple IMF components at the beginning of the actual current traveling wave, select the IMF component at the beginning that is closest to the waveform at the beginning of the actual current traveling wave; and from the multiple IMF components at the end of the actual current traveling wave, select the IMF component at the end that is closest to the waveform at the end of the actual current traveling wave. Instantaneous energy distribution maps of the first-end IMF component and the last-end IMF component are extracted using the envelope derivative energy operator. Capture energy abrupt change points from the instantaneous energy distribution diagrams of the first and last IMF components, and determine the time corresponding to each energy abrupt change point; the time corresponding to each energy abrupt change point is the time when the actual current traveling wave arrives at the first or last end. Based on the actual time when the traveling current wave reaches the beginning and the actual time when the traveling current wave reaches the end, the total length of the coal mining machine power supply line, and the wave velocity, the distance from the single-phase grounding fault point to the beginning of the coal mining machine power supply line is obtained.
2. The method as described in claim 1, characterized in that, The acquisition of the actual current traveling wave of the coal mining machine power supply line when a single-phase ground fault occurs under the target operating conditions specifically includes: In the simulation model of the long-distance variable frequency power supply system of the coal mining machine, the distributed parameters of the mining cable of the coal mining machine power supply line, the length of the mining cable of the coal mining machine power supply line, the curve of the load torque of the coal mining machine changing with time and the target working condition type are set, and the simulation is carried out to obtain the simulation waveform of the coal mining machine under the target working condition. The noise interference characteristics under electromechanical coupling characteristics generated when a single-phase ground fault occurs in the power supply line of the coal mining machine are obtained, and the simulated waveform under the target working condition is superimposed with the noise interference characteristics under the target working condition to obtain the actual current traveling wave under the single-phase ground fault state.
3. The method as described in claim 2, characterized in that, The acquisition of noise interference characteristics under electromechanical coupling when a single-phase ground fault occurs in the power supply line of the coal mining machine specifically includes: A small-scale load simulation experimental platform is constructed; the small-scale load simulation experimental platform includes a frequency converter, a motor, and a dynamometer; The load torque in the curve of load torque changing with time when a single-phase ground fault occurs in the power supply line of the coal mining machine is scaled proportionally, and the result of the proportional scaling is determined as the command signal of the dynamometer. The command signal controls the dynamometer to perform a loading experiment on the variable frequency drive system, obtaining a load torque curve and a motor current waveform with waveform distortion. The load torque curve represents the correspondence between time and the screw drum torque, and between time and the cutting motor torque. The motor current waveform represents the relationship between time and the motor torque. q Shaft current and time and motor d The correspondence between shaft currents; the variable frequency drive system includes the motor and the frequency converter; Noise interference features are extracted from the motor current waveform and the load torque curve with waveform distortion; the noise interference features include time-varying features caused by the uneven distribution of coal and rock composition due to the drum cutting teeth during the operation of the coal mining machine and the harmonic pollution features generated by the power electronic equipment.
4. The method as described in claim 2, characterized in that, The method for obtaining the distributed parameters of the mining cable for the power supply line of the coal mining machine specifically includes: Obtain the structural parameters and material properties of the mining cable used in the power supply line of the coal mining machine; When using a monorail hoist to retract and extend the mining cable for the power supply line of the coal mining machine, take a single deformed structure of the mining cable for the power supply line of the coal mining machine. Based on the structural parameters and material property parameters, electromagnetic field analysis was performed on the single deformable structure using the electromagnetic field simulation software ANSYS to obtain the distribution parameters of the mining cable for the power supply line of the coal mining machine under the monorail hoisting and retraction mode.
5. The method as described in claim 1, characterized in that, The optimization of the number of decompositions and penalty factor of the variational mode decomposition algorithm using the pigeon flock algorithm specifically includes: Initialize the parameters of the pigeon flocking algorithm and randomly initialize the positions and speeds of the pigeons; the position of the pigeon represents the number of decompositions and the penalty factor; The maximum value of the fuzzy entropy of all IMF components is used as the fitness function of the pigeon flock algorithm. The fitness value is calculated based on the fitness function. The position and speed of the pigeons are updated based on the fitness value and through the compass operator until the number of iterations of the compass operator is reached. When the number of iterations of the compass operator is reached, the fitness value calculated based on the fitness function is updated through the landmark operator to update the pigeon's position until the number of iterations of the compass operator is reached. The position corresponding to the number of iterations of the compass operator when the pigeon's position is updated by the landmark operator is determined as the optimized number of decompositions and the optimized penalty factor.
6. The method as described in claim 1, characterized in that, The formula for calculating the distance between the single-phase grounding fault point and the beginning of the coal mining machine power supply line is as follows: ； in, d This refers to the distance from the point of single-phase grounding fault to the beginning of the power supply line. t 1 represents the moment when the actual traveling current wave reaches the beginning of the wave. t 2 represents the moment when the actual traveling current wave reaches its end. L The total length of the power supply line for the coal mining machine. v The wave velocity is given.
7. A fault location device for a coal mining machine power supply line, characterized in that, include: The acquisition module is used to acquire the actual current traveling wave of the coal mining machine power supply line when a single-phase ground fault occurs under the target operating conditions; The decomposition module is used to optimize the number of decompositions and the penalty factor of the variational mode decomposition algorithm through the pigeon flock algorithm, and to adaptively decompose the actual current traveling wave through the optimized variational mode decomposition algorithm to obtain multiple IMF components at the beginning of the actual current traveling wave and multiple IMF components at the end of the actual current traveling wave. The selection module is used to select the first IMF component that is closest to the waveform of the first end of the actual current traveling wave from multiple IMF components at the first end of the actual current traveling wave, and to select the last IMF component that is closest to the waveform of the last end of the actual current traveling wave from multiple IMF components at the last end of the actual current traveling wave. The extraction module is used to extract the instantaneous energy distribution maps of the first-end IMF component and the last-end IMF component respectively using the envelope derivative energy operator; The capture module is used to capture energy abrupt change points from the instantaneous energy distribution diagrams of the first and last IMF components and to determine the time corresponding to the energy abrupt change points; the time corresponding to the energy abrupt change points is the time when the actual current traveling wave arrives at the first or last end. The determination module is used to determine the distance from the start of the single-phase grounding fault point to the start of the coal mining machine power supply line based on the time when the actual current traveling wave arrives at the beginning, the time when the actual current traveling wave arrives at the end, the total length of the coal mining machine power supply line, and the wave velocity.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method as described in any one of claims 1 to 6.
9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method as described in any one of claims 1 to 6.