Power transmission line fault monitoring method and related equipment

Abstract

The invention relates to the technical field of power system monitoring, in particular to a power transmission line fault monitoring method and related equipment.The method comprises the steps that voiceprint vibration signals, electrical parameter signals and line topological structure data of a power transmission line are collected in real time, denoising processing is conducted on the voiceprint signals through a wavelet filtering algorithm, and the power transmission line fault monitoring result is obtained; and identifying the discharge type and intensity by combining a partial discharge feature extraction technology. And based on the signal propagation speed and the time difference, fusing line topological structure parameters to realize accurate positioning of the partial discharge source. And finally, through multi-dimensional data fusion analysis, comprehensive monitoring of the fault type, strength and position of the power transmission line is completed, and a reliable basis is provided for safe operation of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system monitoring, and in particular to a transmission line fault monitoring method and related equipment. Background Art

[0002] Transmission lines often experience faults such as single-phase grounding. Partial discharge (PD) is an early sign of transmission line failure. Timely monitoring and locating PD can effectively prevent equipment failures and improve the reliability of the power system. Existing PD monitoring methods mainly include high-frequency current sensors, ultrasonic detection, and ultra-high frequency detection. However, these methods have the following problems: 1. Insufficient anti-interference ability: PD signals are generally weak and easily affected by external electromagnetic interference, resulting in false alarms or missed alarms. 2. Limited positioning accuracy: Existing positioning methods are affected by factors such as signal attenuation and reflection, resulting in reduced positioning accuracy. 3. High cost: The high cost of high-precision sensors, signal processing equipment, and fiber optic sensing systems limits their application in large-scale transmission lines. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a transmission line fault monitoring method and related equipment to address the deficiencies in the above-mentioned prior art, so as to solve the technical problems of insufficient anti-interference ability and limited positioning accuracy of existing detection means.

[0004] The purpose of the present invention is achieved by the following technical solutions: In a first aspect, the present invention provides a method for monitoring a power transmission line fault, comprising: Real-time acquisition of acoustic vibration signals, electrical parameter signals, and transmission line topology on the transmission line; Performing wavelet transform on the voiceprint vibration signal using a wavelet filtering algorithm to obtain a denoised voiceprint vibration signal; Performing feature extraction on the denoised voiceprint vibration signal to obtain voiceprint vibration signal features, and obtaining the type and discharge intensity of partial discharge based on the voiceprint vibration signal features; The location of the local discharge source is determined based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal, combined with the transmission line topology. It is used for fault monitoring of transmission lines according to the type of partial discharge, discharge intensity and location of partial discharge source; According to the type of partial discharge, discharge intensity and location of the partial discharge source, the fault monitoring of the transmission line is completed.

[0005] As a further improvement of the present invention, the real-time acquisition of the voiceprint vibration signal and the electrical parameter signal on the transmission line specifically includes: The acoustic vibration sensor is used to obtain the acoustic vibration signal caused by partial discharge on the transmission line in real time; the acoustic vibration sensor is set at any single end of the transmission line; The electrical parameter signals are acquired by using electrical parameter sensors, which are evenly arranged at the pole towers corresponding to the transmission lines.

[0006] As a further improvement of the present invention, the wavelet filtering algorithm adopts the Daubechies wavelet basis function algorithm.

[0007] As a further improvement of the present invention, a wavelet filtering algorithm is used to extract the partial discharge characteristic signal, specifically including: Perform wavelet transform on the acquired voiceprint vibration signal to obtain the initial wavelet coefficients; Performing soft threshold processing on the initial wavelet coefficients, setting the wavelet coefficients smaller than the soft threshold to zero, and shrinking the wavelet coefficients larger than the soft threshold to remove noise components, thereby obtaining wavelet coefficients; Performing wavelet reconstruction on the wavelet coefficients to obtain a denoised voiceprint vibration signal; The denoised voiceprint vibration signal is:

[0008] The formula of the initial wavelet coefficient specifically includes:

[0009] Where, is the denoised voiceprint vibration signal, is the wavelet coefficient, is the Daubechies wavelet basis function, To sum over all possible scale parameters, To sum over all possible translation parameters, To sum over all possible scales and translations, is the wavelet coefficient, is the normalization factor, is the voiceprint vibration signal to be analyzed, is the complex conjugate of the Daubechies wavelet function, is the time step of the voiceprint vibration signal, is the scale parameter, is the translation parameter, is the temporal matching degree between the voiceprint vibration signal to be analyzed and the complex conjugate of the Daubechies wavelet function, is the complex conjugate convolution formula of the voiceprint vibration signal to be analyzed and the Daubechies wavelet function.

[0010] As a further improvement of the present invention, the soft threshold is defined as:

[0011] Where, is the soft threshold, is the noise standard deviation, is the length of the voiceprint vibration signal, is the adjustment factor and is a constant.

[0012] As a further improvement of the present invention, after extracting features from the denoised voiceprint vibration signal, the method further includes performing feature classification processing on the extracted voiceprint vibration signal features, specifically including: Integrate the extracted voiceprint vibration signal features to obtain a feature vector; The trained support vector machine algorithm is used to perform partial discharge feature recognition on the feature vector, and classification is performed according to the recognition result to determine the type and discharge intensity of the partial discharge.

[0013] As a further improvement of the present invention, the location of the partial discharge source is determined based on the propagation speed and time difference between the extracted voiceprint vibration signal characteristics and the electrical parameter signal, specifically including: Taking the section of the tower without sensors as the starting point, the position formula of the partial discharge source is:

[0014] Among them, when P y <P x hour:

[0015] When P y >P x hour:

[0016] Where, is the location of the voiceprint vibration sensor; is the position of the electrical parameter sensor; is the location of the local discharge source; P y <P x The arrival time difference between the wave head of the vibration signal and the electrical parameter signal, where the wave head refers to the time from the signal mutation point to the first peak or trough; P y >P x The arrival time difference between the vibration signal and the electrical parameter signal wave head; is the propagation speed of the voiceprint vibration signal on the transmission line; is the propagation speed of the electrical parameter signal on the transmission line, P y <P x The time from the discharge power supply to the electrical parameter sensor; P y <P x The time it takes to discharge the power to the voiceprint vibration sensor, P y <P x The time difference from the voiceprint vibration sensor to the electrical parameter sensor, P y >P x The time it takes to discharge the power to the voiceprint vibration sensor, P y >P x The time it takes to discharge the power supply to the electrical parameter sensor. P y >P x The time difference from the electrical parameter sensor to the voiceprint vibration sensor.

[0017] As a further improvement of the present invention, after obtaining the type of partial discharge, the discharge intensity, and the location of the partial discharge source, the method further includes transmitting the obtained data on the type of partial discharge, the discharge intensity, and the location of the partial discharge source to a monitoring center in real time, and triggering an alarm signal according to a partial discharge threshold preset by the monitoring center.

[0018] In a second aspect, the present invention provides a transmission line fault monitoring system for implementing the above-mentioned transmission line fault monitoring method, comprising: Data acquisition module, which acquires the acoustic vibration signal, electrical parameter signal and transmission line topology on the transmission line in real time; A data preprocessing module performs wavelet transform on the voiceprint vibration signal using a wavelet filtering algorithm to obtain a denoised voiceprint vibration signal; A feature extraction module extracts features from the denoised voiceprint vibration signal to obtain voiceprint vibration signal features, and obtains the type and discharge intensity of partial discharge based on the voiceprint vibration signal features; The initial fault identification module determines the location of the local discharge source based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal, combined with the topology of the transmission line; The fault monitoring module completes the fault monitoring of the transmission line according to the type of partial discharge, discharge intensity and location of the partial discharge source.

[0019] In a third aspect, the present invention provides a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions that, when executed by a computing device, cause the computing device to execute the above-mentioned power transmission line fault monitoring method.

[0020] In a fourth aspect, the present invention provides a computing device, comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the above-mentioned transmission line fault monitoring method.

[0021] The beneficial effects of the present invention include: A power transmission line fault monitoring method provided herein removes noise through a wavelet filtering algorithm, effectively improving the detection accuracy of partial discharge signals. This method utilizes the propagation characteristics of voiceprint vibration signals and transmission line electrical parameter signals, combined with the topology of the transmission line, to achieve high-precision fault location.

[0022] Furthermore, the wavelet filtering algorithm adopts the Daubechies wavelet basis function, where the Daubechies wavelet basis function is the core of the filter, which is used to decompose the signal in order to distinguish and remove the noise components in the signal, separate the noise and the useful signal, and finally output the processing result with the noise removed and the signal clarity improved after signal processing and reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 1 is a flow chart of a method for monitoring a power transmission line fault according to an embodiment of the present invention; Figure 2 In the embodiment of the present invention, P y <P x The corresponding sensor position is placed in the reference diagram; Figure 3 In the embodiment of the present invention, P y >P x The corresponding sensor position is placed in the reference diagram; Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose and technical solution of the present invention clearer and easier to understand, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] The technical solutions of the present invention will be described clearly and completely below with reference to the accompanying drawings and specific embodiments. The described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments.

[0027] Example 1 like Figure 1 As shown, this embodiment provides a transmission line fault monitoring method for real-time online monitoring of transmission line faults, fault location and early warning, so as to achieve the purpose of quickly and effectively identifying whether a transmission line fault has occurred, distinguishing the fault type and locating the fault. In addition, the present invention uses digital output as a more convenient way for on-site operation and maintenance personnel to respond to transmission line faults.

[0028] To achieve the above objectives, the technical solution of this embodiment is as follows.

[0029] First, the acoustic vibration signals, electrical parameter signals and transmission line topology on the transmission line are obtained in real time.

[0030] In this embodiment, the voiceprint vibration signal is obtained by the voiceprint vibration sensor, and the electrical parameter signal is obtained by the electrical parameter sensor. The topology of the transmission line can be obtained by a high-resolution camera carried by a drone and a remote sensing satellite.

[0031] Specifically, the voiceprint vibration sensor is installed on any single end side of the power transmission line. The voiceprint vibration sensor in this embodiment adopts a high-sensitivity microphone or an acceleration sensor.

[0032] The soundprint vibration in this embodiment refers to the vibration generated by local discharge on the transmission line, which in turn generates sound waves. The sound waves generated by the vibration are collected by a high-sensitivity microphone.

[0033] Electrical parameter signals refer to the parameters and data used to describe various electrical characteristics of a power system. This data is used to monitor, analyze, and control the power system's operating status. Electrical parameter signals include voltage, current, power, power factor, frequency, impedance, reactance, harmonics, phase angle, and electrical energy. These signals are typically collected directly from voltage and current transformers on transmission line towers. Electrical parameter sensors are uniformly located on transmission line towers.

[0034] Secondly, the wavelet filter algorithm is used to perform wavelet transform on the voiceprint vibration signal to obtain a denoised voiceprint vibration signal.

[0035] The wavelet filtering algorithm in this embodiment adopts the Daubechies wavelet basis function algorithm. The specific operation steps include: Perform wavelet transform on the acquired voiceprint vibration signal to obtain the initial wavelet coefficients. The initial wavelet coefficients are:

[0036] in, is the wavelet coefficient, is the normalization factor, is the voiceprint vibration signal to be analyzed, is the complex conjugate of the Daubechies wavelet function, is the time step of the voiceprint vibration signal, is the scale parameter, is the translation parameter, is the temporal matching degree between the voiceprint vibration signal to be analyzed and the complex conjugate of the Daubechies wavelet function, is the complex conjugate convolution formula of the voiceprint vibration signal to be analyzed and the Daubechies wavelet function.

[0037] Perform soft threshold processing on the initial wavelet coefficients, set the wavelet coefficients smaller than the soft threshold to zero, and shrink the wavelet coefficients larger than the soft threshold to remove the noise component and obtain the wavelet coefficients. The soft threshold is defined as:

[0038] Where, is the soft threshold, is the noise standard deviation, is the length of the voiceprint vibration signal, is the adjustment factor, which is a constant. The adjustment factor is used to adjust the soft threshold according to the length of the voiceprint vibration signal. When the length N of the voiceprint vibration signal increases, the adjustment factor will also increase, and the corresponding soft threshold will also increase. That is, the soft threshold increases with the length of the voiceprint vibration signal.

[0039] Perform wavelet reconstruction on the wavelet coefficients to obtain the denoised voiceprint vibration signal. The denoised voiceprint vibration signal is:

[0040] Where, is the denoised voiceprint vibration signal, is the wavelet coefficient, is the Daubechies wavelet basis function, To sum over all possible scale parameters, To sum over all possible translation parameters, is the sum over all possible scales and translations.

[0041] The denoised voiceprint vibration signal is subjected to feature extraction to obtain the voiceprint vibration signal features, and the type and discharge intensity of the partial discharge are obtained based on the voiceprint vibration signal features. In addition, after the feature extraction of the denoised voiceprint vibration signal is performed, the feature classification processing of the extracted voiceprint vibration signal features is also included, specifically including: The extracted voiceprint vibration signal features are integrated to obtain the feature vector; the feature components are:

[0042] Where, is the characteristic component, is the first voiceprint vibration signal feature, is the second voiceprint vibration signal feature, is the nth voiceprint vibration signal feature.

[0043] A trained support vector machine algorithm is used to identify partial discharge features from feature vectors. Classification is performed based on the identified results, such as suspended discharge, surface discharge, tip discharge, and corona discharge. The vibration frequencies and response frequencies corresponding to different discharge types are significantly different, thereby determining the type and intensity of the partial discharge. The support vector machine algorithm in this embodiment uses a labeled partial discharge dataset to train the classifier.

[0044] According to the propagation speed and time difference between the extracted soundprint vibration signal characteristics and the electrical parameter signal, combined with the topological structure of the transmission line, the location of the partial discharge source is determined. Among them, the vibration signal collected by the soundprint vibration sensor is , where the wave head signal is , the collected electrical parameter signal is , where the wave head signal is , analyze and collect the time difference between the soundprint vibration signal and the electrical parameter signal wave head arrival time, and complete the calculation of the local discharge source position.

[0045] Taking the section of the tower without sensors as the starting point, the location formula of the partial discharge source is:

[0046] Among them, Figure 2 As shown, when P y <P x hour:

[0047] like Figure 3 As shown, when P y >P x hour:

[0048] Where, is the location of the voiceprint vibration sensor; is the position of the electrical parameter sensor; is the location of the local discharge source; P y <P x The arrival time difference between the wave head of the vibration signal and the electrical parameter signal, where the wave head refers to the time from the signal mutation point to the first peak or trough; P y >P x The arrival time difference between the vibration signal and the electrical parameter signal wave head; is the propagation speed of the voiceprint vibration signal on the transmission line; is the propagation speed of the electrical parameter signal on the transmission line; P y <P x The time from the discharge power supply to the electrical parameter sensor; P y <P x The time it takes to discharge the power to the voiceprint vibration sensor, P y <P x The time difference from the voiceprint vibration sensor to the electrical parameter sensor, P y >P x The time it takes to discharge the power to the voiceprint vibration sensor, P y >P x The time it takes to discharge the power supply to the electrical parameter sensor. P y >P x The time difference from the electrical parameter sensor to the voiceprint vibration sensor.

[0049] The wave head refers to the portion of the waveform where the signal rises from zero to its maximum value during signal transmission. Specifically, the wave head is the time period from the current or voltage rising from zero to its peak value (amplitude). In other words, the wave head refers to the portion of the waveform from the mutation point to the first peak or trough.

[0050] According to the type of partial discharge, discharge intensity and location of the partial discharge source, the fault monitoring of the transmission line is completed.

[0051] Specifically, after obtaining the type of partial discharge, discharge intensity and location of the partial discharge source, the method also includes transmitting the obtained data on the type of partial discharge, discharge intensity and location of the partial discharge source to a monitoring center in real time, triggering an alarm signal according to a partial discharge threshold preset by the monitoring center, and recording the fault location at the same time.

[0052] Use IoT (Internet of Things) technologies (such as the MQTT (Message Queuing Telemetry Transport) protocol) to transmit data to a monitoring center in real time. Set a threshold for partial discharge (such as a discharge intensity threshold). When a partial discharge exceeds the threshold, an alarm signal (such as an audible alarm or SMS notification) is triggered.

[0053] Example 2 like Figure 2 As shown, this embodiment provides a transmission line fault monitoring system for implementing the transmission line fault monitoring method in the embodiment, and the system includes: Data acquisition module, which acquires the acoustic vibration signal, electrical parameter signal and transmission line topology on the transmission line in real time; A data preprocessing module performs wavelet transform on the voiceprint vibration signal using a wavelet filtering algorithm to obtain a denoised voiceprint vibration signal; In this embodiment, the voiceprint vibration signal is obtained by the voiceprint vibration sensor, and the electrical parameter signal is obtained by the electrical parameter sensor. The topology of the transmission line can be obtained by a high-resolution camera carried by a drone and a remote sensing satellite.

[0054] Specifically, the voiceprint vibration sensor is installed on any single end side of the power transmission line. The voiceprint vibration sensor in this embodiment adopts a high-sensitivity microphone or an acceleration sensor.

[0055] The soundprint vibration in this embodiment refers to the vibration generated by local discharge on the transmission line, which in turn generates sound waves. The sound waves generated by the vibration are collected by a high-sensitivity microphone.

[0056] Electrical parameter signals refer to the parameters and data used to describe the various electrical characteristics of a power system. These data are used to monitor, analyze, and control the power system's operating status. Typical electrical parameter signals include voltage, current, power, power factor, frequency, impedance, reactance, harmonics, phase angle, and electrical energy.

[0057] The feature extraction module extracts features from the denoised voiceprint vibration signal to obtain the voiceprint vibration signal features, and then obtains the type and discharge intensity of partial discharge based on the voiceprint vibration signal features; The wavelet filtering algorithm in this embodiment adopts the Daubechies wavelet basis function algorithm. The Daubechies wavelet basis function algorithm is an orthogonal wavelet basis function used to achieve multi-resolution signal analysis by designing filter coefficients with set mathematical conditions. The specific operation steps include: Perform wavelet transform on the acquired voiceprint vibration signal to obtain the initial wavelet coefficients. The initial wavelet coefficients are:

[0058] in, is the wavelet coefficient, is the normalization factor, is the voiceprint vibration signal to be analyzed, is the complex conjugate of the Daubechies wavelet function, is the time step of the voiceprint vibration signal, is the scale parameter, is the translation parameter, is the temporal matching degree between the voiceprint vibration signal to be analyzed and the complex conjugate of the Daubechies wavelet function, is the complex conjugate convolution formula of the voiceprint vibration signal to be analyzed and the Daubechies wavelet function.

[0059] The initial wavelet coefficients are subjected to soft threshold processing, the wavelet coefficients smaller than the soft threshold are set to zero, and the wavelet coefficients larger than the soft threshold are shrunk to remove the noise component and obtain the wavelet coefficients. The soft threshold is defined as:

[0060] Where, is the soft threshold, is the noise standard deviation, is the length of the voiceprint vibration signal, is an adjustment factor used to adjust the soft threshold according to the length of the voiceprint vibration signal. When the length N of the voiceprint vibration signal increases, the adjustment factor also increases, and the corresponding soft threshold also increases. That is, the soft threshold increases with the length of the voiceprint vibration signal.

[0061] Perform wavelet reconstruction on the wavelet coefficients to obtain the denoised voiceprint vibration signal. The denoised voiceprint vibration signal is:

[0062] Where, is the denoised voiceprint vibration signal, is the wavelet coefficient, is the Daubechies wavelet basis function, To sum over all possible scale parameters, To sum over all possible translation parameters, is the sum over all possible scales and translations.

[0063] The initial fault identification module determines the location of the local discharge source based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal, combined with the topology of the transmission line; In addition, after feature extraction of the denoised voiceprint vibration signal, feature classification processing of the extracted voiceprint vibration signal features is also included, specifically including: The extracted voiceprint vibration signal features are integrated to obtain the feature vector; the feature components are:

[0064] Where, is the characteristic component, is the first voiceprint vibration signal feature, is the second voiceprint vibration signal feature, is the nth voiceprint vibration signal feature.

[0065] The trained support vector machine algorithm is used to identify the partial discharge features of the feature vectors, and classification is performed based on the identified results to determine the type and intensity of the partial discharge. The support vector machine algorithm in this embodiment uses a labeled partial discharge dataset to train a classifier.

[0066] According to the propagation speed and time difference between the extracted soundprint vibration signal characteristics and the electrical parameter signal, combined with the topological structure of the transmission line, the location of the partial discharge source is determined. Among them, the vibration signal collected by the soundprint vibration sensor is , where the wave head signal is , the collected electrical parameter signal is , where the wave head signal is The corresponding formula represents the signal function, and the formula parameters need to be extracted from some signal parameters.

[0067] Taking the section of the tower without sensors as the starting point, the location formula of the partial discharge source is:

[0068] Among them, Figure 2 As shown, when P y <P x hour:

[0069] like Figure 3 As shown, when P y >P x hour:

[0070] Where, is the location of the voiceprint vibration sensor; is the position of the electrical parameter sensor; is the location of the local discharge source; P y <P x The arrival time difference between the wave head of the vibration signal and the electrical parameter signal, where the wave head refers to the time from the signal mutation point to the first peak or trough; P y >P x The arrival time difference between the vibration signal and the electrical parameter signal wave head; is the propagation speed of the voiceprint vibration signal on the transmission line; is the propagation speed of the electrical parameter signal on the transmission line; P y <P x The time from the discharge power supply to the electrical parameter sensor; P y <P x The time it takes to discharge the power to the voiceprint vibration sensor, P y <P x The time difference from the voiceprint vibration sensor to the electrical parameter sensor, P y >P x The time it takes to discharge the power to the voiceprint vibration sensor, P y >P x The time it takes to discharge the power supply to the electrical parameter sensor. P y >P x The time difference from the electrical parameter sensor to the voiceprint vibration sensor.

[0071] The fault monitoring module completes the fault monitoring of the transmission line according to the type of partial discharge, discharge intensity and location of the partial discharge source.

[0072] Specifically, after obtaining the type of partial discharge, discharge intensity and location of the partial discharge source, the method also includes transmitting the obtained data on the type of partial discharge, discharge intensity and location of the partial discharge source to a monitoring center in real time, triggering an alarm signal according to a partial discharge threshold preset by the monitoring center, and recording the fault location at the same time.

[0073] Example 3 In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium. This computer-readable storage medium is a storage component in a terminal device, used to store programs and data corresponding to the power transmission line fault monitoring method in Example 1. It should be understood that the computer-readable storage medium herein encompasses both built-in storage media in the terminal device and extended storage media supported by the terminal device. It can be any tangible medium capable of containing or storing programs that can be used by, or in conjunction with, an instruction execution system, device, or component.

[0074] The computer-readable storage medium provides storage space for storing the terminal's operating system. Furthermore, this storage space stores one or more instructions suitable for loading and executing by a processor. These instructions may constitute one or more computer programs (including program code). It should be noted that more specific examples (non-exhaustive) of computer-readable storage media include: an electrical connection with one or more wires, a portable magnetic disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory (e.g., flash memory), optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.

[0075] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than the readable storage medium itself, which is capable of sending, propagating or transmitting programs for use by, or in conjunction with, an instruction execution system, apparatus or device. The program code contained on the readable storage medium may be transmitted via any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination thereof.

[0076] The program code used to perform the operations of the present invention may be written in any combination of one or more programming languages. Programming languages include object-oriented programming languages such as Java, C++, etc., as well as conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely, partially, or as a stand-alone software package on the user's computing device; it may be executed partially on the user's computing device and partially on a remote computing device; it may also be executed entirely on a remote computing device or server. Where a remote computing device is involved, the remote computing device may be connected to the user's computing device via any type of network, including a local area network or a wide area network; or it may be connected to an external computing device (e.g., via the Internet through an Internet service provider).

[0077] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the power transmission line fault monitoring method in the above-mentioned embodiment 1.

[0078] Example 4 See also Figure 4 The terminal device 600 is an electronic device that is implemented as a general-purpose computing device. The components of the electronic device may include, but are not limited to, at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), and a display unit 640.

[0079] The storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the steps shown in the power transmission line fault monitoring method in Example 1, which will not be repeated here.

[0080] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit 6201 and / or a cache storage unit 6202 , and may further include a read-only storage unit 6203 .

[0081] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0082] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0083] The electronic device 600 may also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network, a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, tape drives, and data backup storage platforms.

Claims

1. A method for monitoring a power transmission line fault, characterized in that: include: Real-time acquisition of acoustic vibration signals, electrical parameter signals, and transmission line topology on the transmission line; Performing wavelet transform on the voiceprint vibration signal using a wavelet filtering algorithm to obtain a denoised voiceprint vibration signal; Performing feature extraction on the denoised voiceprint vibration signal to obtain voiceprint vibration signal features, and obtaining the type and discharge intensity of partial discharge based on the voiceprint vibration signal features; The location of the local discharge source is determined based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal, combined with the transmission line topology. It is used to monitor transmission line faults based on the type of partial discharge, discharge intensity and location of the partial discharge source.

2. The method for monitoring power transmission line faults according to claim 1, wherein: The real-time acquisition of the voiceprint vibration signal and electrical parameter signal on the transmission line specifically includes: The acoustic vibration sensor is used to obtain the acoustic vibration signal caused by partial discharge on the transmission line in real time; the acoustic vibration sensor is set at any single end of the transmission line; The electrical parameter signals are acquired by using electrical parameter sensors, which are evenly arranged at the pole towers corresponding to the transmission lines.

3. The method for monitoring power transmission line faults according to claim 1, wherein: The wavelet filtering algorithm adopts the Daubechies wavelet basis function algorithm.

4. The method for monitoring power transmission line faults according to claim 3, wherein: The wavelet filtering algorithm is used to extract the partial discharge characteristic signal, including: Perform wavelet transform on the acquired voiceprint vibration signal to obtain the initial wavelet coefficients; Performing soft threshold processing on the initial wavelet coefficients, setting the wavelet coefficients smaller than the soft threshold to zero, and shrinking the wavelet coefficients larger than the soft threshold to remove noise components, thereby obtaining wavelet coefficients; Performing wavelet reconstruction on the wavelet coefficients to obtain a denoised voiceprint vibration signal; The denoised voiceprint vibration signal is: The formula of the initial wavelet coefficient specifically includes: Where, is the denoised voiceprint vibration signal, is the wavelet coefficient, is the Daubechies wavelet basis function, To sum over all possible scale parameters, To sum over all possible translation parameters, To sum over all possible scales and translations, is the wavelet coefficient, is the normalization factor, is the voiceprint vibration signal to be analyzed, is the complex conjugate of the Daubechies wavelet function, is the time step of the voiceprint vibration signal, is the scale parameter, is the translation parameter, is the temporal matching degree between the voiceprint vibration signal to be analyzed and the complex conjugate of the Daubechies wavelet function, is the complex conjugate convolution formula of the voiceprint vibration signal to be analyzed and the Daubechies wavelet function.

5. The method for monitoring power transmission line faults according to claim 4, wherein: The soft threshold is defined as: Where, is the soft threshold, is the noise standard deviation, is the length of the voiceprint vibration signal, is the adjustment factor and is a constant.

6. The method for monitoring power transmission line faults according to claim 1, wherein: After extracting features from the denoised voiceprint vibration signal, the extracted features of the voiceprint vibration signal are further classified, specifically including: Integrate the extracted voiceprint vibration signal features to obtain a feature vector; The trained support vector machine algorithm is used to perform partial discharge feature recognition on the feature vector, and classification is performed according to the recognition result to determine the type and discharge intensity of the partial discharge.

7. The method for monitoring power transmission line faults according to claim 2, wherein: The location of the partial discharge source is determined based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal. Specifically, the following steps are performed: Taking the section of the tower without sensors as the starting point, the position formula of the partial discharge source is: Among them, when P y <P x hour: When P y >P x hour: Where, is the location of the voiceprint vibration sensor; is the position of the electrical parameter sensor; is the location of the local discharge source; P y <P x The arrival time difference between the wave head of the vibration signal and the electrical parameter signal, where the wave head refers to the time from the signal mutation point to the first peak or trough; P y >P x The arrival time difference between the vibration signal and the electrical parameter signal wave head; is the propagation speed of the voiceprint vibration signal on the transmission line; is the propagation speed of the electrical parameter signal on the transmission line, P y <P x The time from the discharge power supply to the electrical parameter sensor; P y <P x The time it takes to discharge the power to the voiceprint vibration sensor, P y <P x The time difference from the voiceprint vibration sensor to the electrical parameter sensor, P y >P x The time it takes to discharge the power to the voiceprint vibration sensor, P y >P x The time it takes to discharge the power supply to the electrical parameter sensor. P y >P x The time difference from the electrical parameter sensor to the voiceprint vibration sensor.

8. The method for monitoring power transmission line faults according to claim 1, wherein: After obtaining the type of partial discharge, discharge intensity and location of the partial discharge source, the method also includes transmitting the obtained data on the type of partial discharge, discharge intensity and location of the partial discharge source to a monitoring center in real time, and triggering an alarm signal according to a partial discharge threshold preset by the monitoring center.

9. A power transmission line fault monitoring system, used to implement the power transmission line fault monitoring method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, which acquires the acoustic vibration signal, electrical parameter signal and transmission line topology on the transmission line in real time; A data preprocessing module performs wavelet transform on the voiceprint vibration signal using a wavelet filtering algorithm to obtain a denoised voiceprint vibration signal; A feature extraction module extracts features from the denoised voiceprint vibration signal to obtain voiceprint vibration signal features, and obtains the type and discharge intensity of partial discharge based on the voiceprint vibration signal features; The initial fault identification module determines the location of the local discharge source based on the propagation speed and time difference between the extracted acoustic vibration signal characteristics and the electrical parameter signal, combined with the topology of the transmission line; The fault monitoring module completes the fault monitoring of the transmission line according to the type of partial discharge, discharge intensity and location of the partial discharge source.

10. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to execute the power transmission line fault monitoring method according to any one of claims 1 to 8.

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