Methods, equipment and media for locating faults in coal mine power grids

Abstract

This invention relates to the field of power grid fault technology, and more particularly to a method for locating faults in coal mine power grids. The method includes: acquiring transient current traveling wave signals generated when a fault occurs; performing a first-layer PSO (Progressive Optimal Sampling) to adaptively find the optimal parameter combination for VMD (Dynamic Mode Decomposition); processing the transient current traveling wave signal based on MRSVD (Magnetic Resonance Spectroscopy) to obtain the VMD input signal; inputting the input signal into the VMD based on the optimal parameter combination to obtain intrinsic mode function (IMF) components; calculating the kurtosis-permutation entropy index of the IMF components; selecting the IMF component with the highest value as the optimal mode component; extracting the initial traveling wave arrival time using a symmetric differential energy operator; performing a second-layer PSO; correcting the initial traveling wave arrival time through a physical constraint optimization model; adjusting the traveling wave velocity using an adaptive wave velocity estimation strategy to obtain the corrected traveling wave front time and traveling wave velocity; and calculating the location of the fault point based on the corrected traveling wave front time and traveling wave velocity. This invention achieves rapid and accurate fault point location with high robustness.

Description

Technical Field

[0001] This invention relates to the field of power grid fault technology, and in particular to a method, equipment and medium for locating faults in coal mine power grids. Background Technology

[0002] The underground environment of coal mines is complex, with cable lines having multiple branches and intricate structures. These cables contain a large number of power electronic devices that are susceptible to moisture and mechanical damage, leading to frequent faults such as single-phase grounding and phase-to-phase short circuits. After a fault occurs, quickly and accurately locating the fault point is crucial for rapidly isolating the fault, restoring power supply, ensuring safe mine production, and preventing major accidents.

[0003] Currently, the traveling wave fault location method is commonly used for power grid fault location and detection. This method locates faults by detecting the time difference between the arrival times of transient voltage / current traveling wave signals generated at different measurement points during a fault. In principle, it has the advantage of being unaffected by transition resistance or system operating mode, and theoretically offers high location accuracy. However, in practical applications in underground coal mines, this method faces two major challenges: 1) Strong noise interference: There are a large number of power electronic devices and frequency converters underground, and the background electromagnetic noise is strong, which can easily drown out the weak initial traveling wave front, making it difficult to accurately capture the arrival time of the wave front.

[0004] 2) Multi-branch structure: The underground power grid has a tree-like or radial multi-branch structure. Traveling waves will be reflected at the branch points, generating complex aliasing signals, which makes it easy to misjudge using traditional single-ended or simple double-ended methods.

[0005] In existing technologies, wavelet transform and empirical mode decomposition (EMD) are commonly used to solve the wavefront extraction problem. However, wavelet transform depends on the choice of basis functions, while EMD suffers from mode aliasing and endpoint effects, resulting in significant performance degradation under strong noise. To address the multi-branch problem, some methods attempt to utilize multi-end information or construct complex time-difference matrices. However, these methods often involve high computational costs, poor real-time performance, and are still affected by the uncertainty of traveling wave velocity, leading to slow efficiency, low accuracy, and poor robustness in fault location in underground coal mines. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0007] Therefore, this invention provides a method for locating faults in coal mine power grids, which can effectively suppress strong noise interference, adapt to complex multi-branch structures, and overcome the uncertainty of wave velocity to achieve high-precision fault location, realize rapid and accurate fault point location, and has high robustness.

[0008] A coal mine power grid fault location method according to an embodiment of the present invention includes the following steps: S1, use a traveling wave acquisition device to collect transient current traveling wave signals generated when a fault occurs at both ends of the coal mine power grid line M and N; S2, execute the first layer of particle swarm optimization algorithm to adaptively find the optimal parameter combination θ=[K,α] for variational mode decomposition algorithm, where K is the number of modes and α is the penalty factor; S3, Based on MRSVD processing of the transient current traveling wave signal, obtain the input signal of the variational mode decomposition algorithm; S4, based on the optimal parameter combination θ=[K,α], decompose the input signal using the variational mode decomposition algorithm to obtain several intrinsic mode function components (IMFs). K K = 1…K; S5, Calculate the IMF for each intrinsic mode function component. K Kurtosis-permutation entropy index KP k The intrinsic mode function component with the highest kurtosis-permutation entropy index value is selected as the optimal mode component. The energy mutation point of the optimal mode component is extracted by the symmetric differential energy operator to detect the time point when the fault current traveling wave arrives at both ends M and N of the coal mine power grid line. This time point is the initial traveling wave arrival time. S6, set the initial traveling wave arrival time as the observed value, execute the second-layer particle swarm optimization algorithm and correct the initial traveling wave arrival time through the physical constraint optimization model, and simultaneously use an adaptive wave velocity estimation strategy to iteratively and dynamically adjust the traveling wave velocity to obtain the corrected traveling wave head time and traveling wave velocity. S7 calculates the location of the fault point based on the corrected traveling wave head time and traveling wave velocity.

[0009] The beneficial effects of this invention are as follows: The coal mine power grid fault location method of this invention adopts a two-layer particle swarm optimization algorithm that separates parameter optimization and time correction optimization. The first layer focuses on improving the signal decomposition quality to lay the foundation for wavefront extraction, while the second layer ensures the physical rationality of the location results. The two layers work together to overcome the limitations of single optimization. Furthermore, through MRSVD-VMD combined filtering and optimal component selection of the KP index, weak traveling wavefront signals can be accurately extracted from a background of strong noise, improving the anti-interference capability and accuracy of wavefront calibration. The physical characteristics of the transmission line are incorporated into the optimization model as hard constraints to ensure that the location results have both mathematical optimality and physical rationality, overcoming the influence of wave velocity uncertainty and multi-branch structure to achieve rapid and accurate fault location.

[0010] According to an embodiment of the present invention, step S3 specifically includes the following steps: S31, the matrix constructed from the transient current traveling wave signal is subjected to layer-by-layer singular value decomposition using MRSVD to obtain the approximate component A at the j-th decomposition scale. j and detail component D j j = 1, 2, ...; S32, Calculate the detail components D for each layer. j kurtosis value K j And compare it with a preset threshold; If all calculated kurtosis values ​​K j If all values ​​are less than the preset threshold, then the deepest approximation component A is used. lmax As the input signal for the variational mode decomposition algorithm; Conversely, all kurtosis values ​​K are... j Arrange them in descending order, and select the detail component D corresponding to the decomposition layer Lopt with the largest kurtosis value. Lopt As the input signal for the variational mode decomposition algorithm.

[0011] According to an embodiment of the present invention, in step S5, the time for detecting the arrival of the fault current traveling wave at both ends M and N of the coal mine power grid line by extracting the energy mutation point of the optimal mode component through the symmetric differential energy operator specifically includes: Calculate the energy spectrum of the symmetric difference energy operator for the optimal modal components. ; Set dynamic threshold , where μ and σ are the mean and standard deviation of the symmetric difference energy operator, respectively, and λ is the coefficient; energy spectrum The time point corresponding to the first maximum value exceeding the dynamic threshold η is taken as the time when the fault current traveling wave arrives at both ends M and N of the coal mine power grid line, denoted as T. M and T N .

[0012] According to an embodiment of the present invention, step S6, setting the initial traveling wave arrival time as the observed value, executing the second-layer particle swarm optimization algorithm, and correcting the initial traveling wave arrival time through a physical constraint optimization model, specifically includes the following steps: Model the optimization problem and define the optimization variables. , Let the arrival times of the wavefronts be the values ​​to be optimized, and a penalty function be introduced to handle the constraints, minimizing the deviation between the arrival times of the wavefronts to be optimized and the observed values. The expression is as follows: ; in, Let the penalty function be defined as:

[0013] It is a very large positive number. ( The function is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For optimization variables And estimated wave speed calculation Fault distance, The theoretical maximum value of wavefront time difference. The minimum wave velocity is preset, and L is the distance between the two ends of the cable from the M end to the N end of the coal mine power grid line; The second-level particle swarm optimization algorithm searches the optimization problem to find the solution that minimizes the objective function f(T). This is the corrected traveling wave head time.

[0014] According to an embodiment of the present invention, step S6, which iteratively and dynamically adjusts the traveling wave velocity using an adaptive wave velocity estimation strategy, specifically includes the following steps: The reference wave speed is set based on the line type. ; When executing the second-layer particle swarm optimization algorithm, based on the current particle position Combined with reference wave speed Preliminary fault location calculated ; By simulating the attenuation characteristics of wave velocity with propagation distance using a piecewise function, the wave velocity is dynamically adjusted in the second-layer particle swarm optimization algorithm iteration to obtain the corrected traveling wave velocity. .

[0015] According to an embodiment of the present invention, in step S7, the formula for calculating the fault location is:

[0016]

[0017] in, This is the distance from the fault point to the measurement point M.

[0018] According to one embodiment of the present invention, in step S2, a comprehensive quality evaluation function F(θ) is constructed as the fitness function of the first-layer particle swarm optimization algorithm:

[0019] in, For all intrinsic mode function components (IMF) K The mean of the envelope entropy, As a measure of the uniformity of the center frequency spacing, For reconstruction error, , , Let be the weight coefficient, and satisfy... =1.

[0020] According to one embodiment of the present invention, in step S5, each intrinsic mode function component (IMF) KKurtosis-permutation entropy index KP k The calculation formula is:

[0021] Among them, Kurtosis For kurtosis, Let be the permutation entropy.

[0022] A computer device according to an embodiment of the present invention is characterized in that it comprises: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the coal mine power grid fault location method as described in any one of claims 1 to 8.

[0023] According to an embodiment of the present invention, a computer-readable storage medium is characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the coal mine power grid fault location method as described in any one of claims 1 to 8.

[0024] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0025] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0027] Figure 1 This is a flowchart of Embodiment 1 of the present invention; Figure 2 This is a schematic diagram comparing the original traveling wave and the traveling wave with added noise, provided in Embodiment 1 of the present invention. Figure 3 This is a diagram showing the wavefront extraction effect under strong noise conditions provided in Embodiment 1 of the present invention. Figure 4 This is a comparison chart of fault location accuracy under strong noise conditions with wavelet transform method and traditional VMD algorithm provided in Embodiment 1 of the present invention; Figure 5 This is a comparison chart of the ranging errors of Embodiment 1 of the present invention, wavelet transform method, and traditional VMD algorithm.

[0028] Figure 6 This is a schematic diagram of the computer device structure according to Embodiment 2 of the present invention.

[0029] In the diagram, 10 is a computer device; 1002 is a processor; 1004 is a memory; and 1006 is a transmission device. Detailed Implementation

[0030] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0031] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0032] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0033] Example 1 This application provides a method for locating faults in a coal mine power grid, such as... Figure 1 As shown, the method includes the following steps: S1, a traveling wave acquisition device is used to collect the transient current traveling wave signal generated when a fault occurs at both ends M and N of the coal mine power grid line; further, traveling wave acquisition devices are installed at the beginning (M end) and end (N end) of the coal mine power grid line, and the transient current traveling wave signal generated when the fault occurs is collected by the traveling wave acquisition device, respectively. and .

[0034] S2, execute the first layer particle swarm optimization algorithm (PSO) to adaptively find the optimal parameter combination θ=[K,α] for variational mode decomposition algorithm (VMD), where K is the number of modes and α is the penalty factor, so that the decomposition effect of variational mode decomposition algorithm is optimal under the comprehensive index.

[0035] S3, based on MRSVD processing of transient current traveling wave signals, obtains the input signal for the variational mode decomposition algorithm. S4, based on the optimal parameter combination θ=[K,α], decompose the input signal using the variational mode decomposition algorithm to obtain several intrinsic mode function components (IMFs). K K = 1…K S5, Calculate the IMF for each intrinsic mode function component. K Kurtosis-permutation entropy index KP k The intrinsic mode function component (IMF) with the highest kurtosis-permutation entropy index value is selected as the optimal mode component. The energy abrupt change point of the optimal mode component is extracted using a symmetric differential energy operator to detect the arrival time of the fault current traveling wave at both ends M and N of the coal mine power grid line; this time point is the initial traveling wave arrival time. Among these, each IMF... K Kurtosis-permutation entropy index KP k The calculation formula is:

[0036] Among them, Kurtosis For kurtosis, This refers to the permutation entropy. Components with lower permutation entropy are less susceptible to noise contamination. The calculation parameter for permutation entropy is the embedding dimension. Time delay This needs to be preset. It is achieved through the kurtosis-permutation entropy index KP. k By screening intrinsic mode function components with obvious impact characteristics and low noise pollution, strong noise interference can be effectively removed, weak signals can be purified, and positioning accuracy can be improved.

[0037] S6: Set the initial traveling wave arrival time as the observed value, execute the second-layer particle swarm optimization algorithm, and correct the initial traveling wave arrival time using a physical constraint optimization model. Simultaneously, adopt an adaptive wave velocity estimation strategy to iteratively and dynamically adjust the traveling wave velocity, obtaining the corrected traveling wave front time and traveling wave velocity. S7 calculates the location of the fault point based on the corrected traveling wave head time and traveling wave velocity.

[0038] In this embodiment, in step S2, a comprehensive quality evaluation function F(θ) is constructed as the fitness function of the first-layer particle swarm optimization algorithm:

[0039] in, , , Let be the weight coefficient, and satisfy... =1, based on experience, it can be set to... =0.5, =0.3, =0.2. For all intrinsic mode function components (IMF) K The mean envelope entropy is used to measure the sparsity of the impact features. For the th Each intrinsic mode function component Its envelope entropy The calculation is as follows: The envelope signal is obtained by Hibert transform. ,Will Normalization to probability distribution ,but

[0040] .

[0041] To measure the uniformity of the center frequency spacing, let the center frequencies of each mode after the variational mode decomposition algorithm be... ,..., (Sorted in ascending order), calculate the sequence of adjacent center frequency differences.

[0042] but , that is, the standard deviation of the sequence. The smaller the value, the lower the degree of modal aliasing.

[0043] To account for reconstruction error and ensure the integrity of the decomposition, the calculation formula is as follows:

[0044] in, It is an L2 norm, and its function is to calculate the square root of the sum of the squares of all data points. The number of modes decomposed by the variational mode decomposition algorithm. For the first The number of modes of each eigenmode function component. This is the input signal.

[0045] When the first-layer particle swarm optimization algorithm searches for the influencing parameters of the variational mode decomposition algorithm, it updates the parameters by comparing the new particle fitness values ​​with the constructed comprehensive quality evaluation function F(θ) to find the optimal parameter combination θ=[K,α] for the variational mode decomposition algorithm. The optimization process is divided into two stages. The first stage is within a relatively large range (e.g., , The first stage performs a global search within the region; the second stage performs a fine search near the found optimal solution region to improve convergence accuracy and efficiency.

[0046] In this embodiment, step S3 specifically includes the following steps: S31, the matrix constructed from the transient current traveling wave signal is subjected to layer-by-layer singular value decomposition using MRSVD (Multi-resolution Singular Value Decomposition) to obtain the approximate component A at the j-th decomposition scale. j and detail component D j j = 1, 2, ...

[0047] S32, Calculate the detail components D for each layer. j kurtosis value K j And compare it with a preset threshold.

[0048] If all calculated kurtosis values ​​K j If all values ​​are less than the preset threshold, the noise is too strong, and the deepest approximation component A is used. lmax The input signal for the variational mode decomposition algorithm; the deepest approximate component A lmax It is obtained through layer-by-layer iterative decomposition using MRSVD, that is, after decomposing to a preset maximum number of layers, the approximate component A is output. lmax .

[0049] Conversely, all kurtosis values ​​K are... j Arrange them in descending order, and select the detail component D corresponding to the decomposition layer Lopt with the largest kurtosis value. Lopt As the input signal for the variational mode decomposition algorithm.

[0050] It should be noted that during MRSVD decomposition, a maximum decomposition level needs to be set to calculate the detail components D of each level. j kurtosis value K j In this embodiment, the kurtosis value of the detail component in the third layer is the largest, and this layer is selected as the input signal for the variational mode decomposition algorithm.

[0051] In this embodiment, step S5, specifically detecting the arrival time of the fault current traveling wave at both ends M and N of the coal mine power grid line by extracting the energy mutation point of the optimal mode component through the symmetric differential energy operator, includes: Calculate the energy spectrum of the symmetric difference energy operator for the optimal modal components. Defined as:

[0052] Where n is a discrete point, These are the optimal modal components. Energy spectrum. A local maximum peak value will be generated at the moment the traveling wave front arrives.

[0053] Set dynamic threshold , where μ and σ are the mean and standard deviation of the symmetric differential energy operator, respectively, and λ is a coefficient, usually taken as 3-5.

[0054] energy spectrum The time point corresponding to the first maximum value exceeding the dynamic threshold η is taken as the time when the fault current traveling wave arrives at both ends M and N of the coal mine power grid line, denoted as T. M and T N .

[0055] In this embodiment, step S6, setting the initial traveling wave arrival time as the observed value, executing the second-layer particle swarm optimization algorithm, and correcting the initial traveling wave arrival time using a physical constraint optimization model, specifically includes the following steps: Model the optimization problem and define the optimization variables. , Let the arrival times of the wavefronts be the values ​​to be optimized, and a penalty function be introduced to handle the constraints, minimizing the deviation between the arrival times of the wavefronts to be optimized and the observed values. The expression is as follows: ; in, Let the penalty function be defined as:

[0056] This is a very large positive number (penalty factor), ensuring that if the constraint is violated, the objective function value increases sharply, thus guiding the search towards the feasible region. ( The function is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For optimization variables And estimated wave speed calculation The fault distance is calculated using the following formula: , The theoretical maximum value of the wavefront time difference is calculated using the following formula: , The minimum wave velocity is preset, and L is the distance between the two ends of the cable from the M end to the N end of the coal mine power grid line; The second-level particle swarm optimization algorithm searches the optimization problem to find the solution that minimizes the objective function f(T). This is the corrected traveling wave head time.

[0057] In this embodiment, step S6, which iteratively and dynamically adjusts the traveling wave velocity using an adaptive wave velocity estimation strategy, specifically includes the following steps: The reference wave speed is set based on the line type. ; When executing the second-layer particle swarm optimization algorithm, based on the current particle position Combined with reference wave speed Preliminary fault location calculated ; By simulating the attenuation characteristics of wave velocity with propagation distance using a piecewise function, the wave velocity is dynamically adjusted in the second-layer particle swarm optimization algorithm iteration to obtain the corrected traveling wave velocity. This effectively overcomes the positioning error caused by the uncertainty of traveling wave velocity, further improving detection accuracy. Specifically, the piecewise function is:

[0058] Where β is the attenuation coefficient, used to simulate the characteristic that the wave speed decreases slightly as the propagation distance increases.

[0059] In this embodiment, in step S7, the location of the fault point is calculated based on the principle of two-end traveling wave ranging. The calculation formula is as follows:

[0060]

[0061] in, This is the distance from the fault point to the measurement point M.

[0062] To illustrate the accuracy and positioning effect of this embodiment, a cable fault simulation platform is constructed. A discharge sphere module is used as the key excitation source. By adjusting the position and gap of the discharge sphere, fault traveling wave signals with different fault locations and discharge characteristics can be flexibly simulated. Essentially, this process involves injecting a steeply leading pulse into the cable at the moment the sphere gap breaks down. This pulse propagates along the cable and reflects at impedance discontinuities (such as the fault point), forming a traveling wave signal containing fault characteristics. After the fault occurs, the generated traveling wave signal is transmitted to the workbench by a signal acquisition device. The signal data is then analyzed using the method described in this embodiment to perform fault location.

[0063] See Figure 2 As shown, the original traveling wave signal and the traveling wave signal after noise were compared, which intuitively demonstrated the degree of interference of the strong noise environment in the coal mine on the traveling wave signal.

[0064] See Figure 3 The diagram illustrates the wavefront extraction performance of this embodiment under strong noise conditions with a signal-to-noise ratio of 10 dB. It can be seen that although the original signal is severely contaminated by noise, the wavefront characteristics are clearly defined after processing in this embodiment, enabling accurate determination of the arrival time.

[0065] See Figure 4As shown, the fault location accuracy of this embodiment is compared with that of wavelet transform method and traditional VMD algorithm under strong noise conditions. It can be seen that the location accuracy of this embodiment is higher than that of wavelet transform method and traditional VMD algorithm.

[0066] See Figure 5 As shown, the distance measurement error of this embodiment is compared with that of the wavelet transform method and the traditional VMD algorithm. It can be seen that the distance measurement error of this embodiment is the smallest compared with the wavelet transform method and the traditional VMD algorithm.

[0067] Statistical results show that the average ranging error of the method of this invention is significantly lower than that of the wavelet transform method and the traditional VMD algorithm. This indicates that the method of this invention has significant advantages in strong noise backgrounds and can meet the accuracy and reliability requirements of fault location in underground coal mines.

[0068] In summary, the coal mine power grid fault location method of this embodiment adopts a two-layer particle swarm optimization algorithm that separates parameter optimization and time correction optimization. The first layer focuses on improving the signal decomposition quality to lay the foundation for wavefront extraction, while the second layer ensures the physical rationality of the location results. The two layers work together to achieve full-process assurance of "signal decomposition optimization + location result verification". Furthermore, through MRSVD-VMD combined filtering and optimal component selection of KP index, weak traveling wavefront signals can be accurately extracted from strong noise backgrounds, improving the anti-interference capability and accuracy of wavefront calibration. The physical characteristics of the transmission line are incorporated into the optimization model as hard constraints to ensure that the location results have both mathematical optimality and physical rationality, overcoming the influence of wave velocity uncertainty and multi-branch structure to achieve rapid and accurate fault location with high robustness.

[0069] Example 2 This application provides a computer device including a processor and a memory. The memory stores at least one instruction or at least one program, which is loaded and executed by the processor to implement a coal mine power grid fault location method as provided in the above method embodiments.

[0070] Figure 6 A schematic diagram of the hardware structure of a device for implementing a coal mine power grid fault location method provided in the embodiments of this application is shown. The device can participate in or include the apparatus or system provided in the embodiments of this application. Figure 6As shown, the computer device 10 may include one or more processors 1002 (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 1004 for storing data, and a transmission device 1006 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 6 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer device 10 may also include... Figure 6 The more or fewer components shown, or having the same Figure 6 The different configurations shown.

[0071] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuit may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer device 10 (or mobile device). As involved in the embodiments of this application, the data processing circuit serves as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0072] The memory 1004 can be used to store software programs and modules for application software, such as the program instruction / data storage device corresponding to a coal mine power grid fault location method in this embodiment of the application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 1004, thereby implementing the aforementioned method. The memory 1004 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1004 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer device 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0073] The transmission device 1006 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer device 10. In one example, the transmission device 1006 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 1006 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0074] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer device 10 (or mobile device).

[0075] Example 3 This application also provides a computer-readable storage medium, which can be disposed in a server to store at least one instruction or at least one program related to implementing a coal mine power grid fault location method in the method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the coal mine power grid fault location method provided in the above method embodiment.

[0076] Optionally, in this embodiment, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0077] Example 4 This invention also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform a coal mine power grid fault location method provided in the various optional embodiments described above.

[0078] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device, equipment, and storage medium embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0080] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0081] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for locating faults in a coal mine power grid, characterized in that, The method includes the following steps: S1, use a traveling wave acquisition device to collect transient current traveling wave signals generated when a fault occurs at both ends of the coal mine power grid line M and N; S2, execute the first layer of particle swarm optimization algorithm to adaptively find the optimal parameter combination θ=[K,α] for variational mode decomposition algorithm, where K is the number of modes and α is the penalty factor; S3, Based on MRSVD processing of the transient current traveling wave signal, obtain the input signal of the variational mode decomposition algorithm; S4, based on the optimal parameter combination θ=[K,α], decompose the input signal using the variational mode decomposition algorithm to obtain several intrinsic mode function components (IMFs). K K = 1…K; S5, Calculate the IMF for each intrinsic mode function component. K Kurtosis-permutation entropy index KP k The intrinsic mode function component with the highest kurtosis-permutation entropy index value is selected as the optimal mode component. The energy abrupt change point of the optimal mode component is extracted using a symmetric differential energy operator to detect the arrival time of the fault current traveling wave at both ends M and N of the coal mine power grid line. This time point is the initial arrival time of the traveling wave, denoted as T. M and T N ; S6, set the initial traveling wave arrival time as the observed value, execute the second-layer particle swarm optimization algorithm and correct the initial traveling wave arrival time through the physical constraint optimization model, and simultaneously use an adaptive wave velocity estimation strategy to iteratively and dynamically adjust the traveling wave velocity to obtain the corrected traveling wave head time and traveling wave velocity. S7, calculate the location of the fault point based on the corrected traveling wave head time and traveling wave velocity; Specifically, step S6, which involves setting the initial traveling wave arrival time as the observed value, executing the second-layer particle swarm optimization algorithm, and correcting the initial traveling wave arrival time using a physical constraint optimization model, includes the following steps: Model the optimization problem and define the optimization variables. , Let the arrival times of the wavefronts be the values ​​to be optimized, and a penalty function be introduced to handle the constraints, minimizing the deviation between the arrival times of the wavefronts to be optimized and the observed values. The expression is as follows: ； in, Let the penalty function be defined as: It is a very large positive number. ( The function is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For optimization variables And estimated wave speed Calculated fault distance, The theoretical maximum value of wavefront time difference, where L is the distance between the two ends of the cable from end M to end N of the coal mine power grid line; The second-level particle swarm optimization algorithm searches the optimization problem to find the solution that minimizes the objective function f(T). This is the corrected traveling wave head time.

2. The method for locating faults in a coal mine power grid as described in claim 1, characterized in that, Step S3 specifically includes the following steps: S31, the matrix constructed from the transient current traveling wave signal is subjected to layer-by-layer singular value decomposition using MRSVD to obtain the approximate component A at the j-th decomposition scale. j and detail component D j j = 1, 2, ...; S32, Calculate the detail components D for each layer. j kurtosis value K j And compare it with a preset threshold; If all calculated kurtosis values ​​K j If all values ​​are less than the preset threshold, then the deepest approximation component A is used. lmax As the input signal for the variational mode decomposition algorithm; Conversely, all kurtosis values ​​K are... j Arrange them in descending order, and select the detail component D corresponding to the decomposition layer Lopt with the largest kurtosis value. Lopt As the input signal for the variational mode decomposition algorithm.

3. The method for locating faults in a coal mine power grid as described in claim 1, characterized in that, In step S5, the time it takes for the fault current traveling wave to reach both ends M and N of the coal mine power grid line is specifically detected by extracting the energy mutation point of the optimal mode component using the symmetric differential energy operator: Calculate the energy spectrum of the symmetric difference energy operator for the optimal modal components. ; Set dynamic threshold , where μ and σ are the mean and standard deviation of the symmetric difference energy operator, respectively, and λ is the coefficient; energy spectrum The time point corresponding to the first maximum value point exceeding the dynamic threshold η is taken as the time when the fault current traveling wave arrives at both ends of the coal mine power grid line M and N.

4. The method for locating faults in a coal mine power grid as described in claim 1, characterized in that, In step S6, the iterative dynamic adjustment of the traveling wave velocity using an adaptive wave velocity estimation strategy specifically includes the following steps: The reference wave speed is set based on the line type. ; When executing the second-layer particle swarm optimization algorithm, based on the current particle position Combined with reference wave speed Preliminary fault location calculated ; By simulating the attenuation characteristics of wave velocity with propagation distance using a piecewise function, the wave velocity is dynamically adjusted in the second-layer particle swarm optimization algorithm iteration to obtain the corrected traveling wave velocity. .

5. The method for locating faults in a coal mine power grid as described in claim 4, characterized in that, In step S7, the formula for calculating the location of the fault point is: in, This is the distance from the fault point to the measurement point M.

6. The method for locating faults in a coal mine power grid as described in claim 1, characterized in that, In step S2, a comprehensive quality evaluation function F(θ) is constructed as the fitness function of the first-layer particle swarm optimization algorithm: in, For all intrinsic mode function components (IMF) K The mean of the envelope entropy, As a measure of the uniformity of the center frequency spacing, For reconstruction error, , , Let be the weight coefficient, and satisfy... =1.

7. The method for locating faults in a coal mine power grid as described in claim 1, characterized in that, In step S5, each intrinsic mode function component (IMF) K Kurtosis-permutation entropy index KP k The calculation formula is: Among them, Kurtosis For kurtosis, Let be the permutation entropy.

8. A computer device, characterized in that, include: processor; Memory, used to store executable instructions; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the coal mine power grid fault location method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to implement the coal mine power grid fault location method as described in any one of claims 1 to 7.

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