A method and system for identifying and warning faults of power transmission lines

Abstract

The present application discloses a fault identification and early warning method and system for a power transmission line, the method comprising: receiving a fault flashover signal, determining the occurrence area of the fault flashover signal, and obtaining a target image; determining whether there is abnormal discharge in the transmission line in the occurrence area; if so, performing image recognition to obtain a fault source to be tested; obtaining the inclination of the tower in the occurrence area and the electromagnetic field and radar changes; determining the movement path of the current fault source, determining the fault source category through image capture, and determining the target fault source; obtaining meteorological data through micro-meteorological sensors and network meteorological weather, and obtaining the future and real-time dancing amplitude and dancing frequency of the transmission line; combining the dancing amplitude, dancing frequency, tree obstacles, super-high engineering vehicles, floating objects, and lightning strikes, which are greater than the warning value, starting image capture to determine the target probability and image of the target fault source causing a secondary fault on the transmission line, determining the high-risk interval segment, and sending the warning picture and video to the background in real time.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of fault warning technology, and in particular to a method and system for identifying and warning faults of power transmission lines. Background Art

[0002] Transmission lines often traverse extensive geographical areas, encompassing complex terrain such as mountains, forests, and rivers. These environmental conditions present significant challenges for routine maintenance and inspection. First, the complexity of the line corridors means inspectors face a variety of natural obstacles, such as steep slopes, dense vegetation, and unstable ground conditions. This results in long inspection cycles for manual inspections, making it difficult to detect and address potential faults in a timely manner. This leads to poor inspection effectiveness and, in turn, unsafe transmission line maintenance.

[0003] Therefore, in the current maintenance of transmission lines, relying solely on traditional manual inspection methods is difficult to meet the safety requirements of modern power systems for transmission lines. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for identifying and warning faults of power transmission lines, which are used to effectively improve the safety of power transmission line maintenance.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, a method for identifying and warning faults of a power transmission line is provided, the method comprising:

[0007] In response to receiving a fault flashover signal, determining an occurrence area of the fault flashover signal and acquiring a target image of the occurrence area;

[0008] determining whether there is abnormal discharge in the power transmission lines in the occurrence area;

[0009] In the case where abnormal discharge occurs in the power transmission line in the occurrence area, performing image recognition on the target image to obtain a first image recognition result, wherein the first image recognition result includes at least one fault source to be detected;

[0010] In response to receiving the target parameters sent by the laser radar, obtaining the inclination of the transmission line tower in the occurrence area and the electromagnetic field change of the transmission line at the current moment;

[0011] determining a movement path of a current fault source according to the target parameter, and determining a fault source category of the current fault source according to the movement path, the target parameter, the inclination of the tower, and the electromagnetic field change;

[0012] Determining a target fault source among the fault sources to be tested according to the fault source category, wherein the target fault source is used to characterize the current fault source;

[0013] Acquiring meteorological data for a future time period, and predicting the dancing amplitude and dancing frequency of the transmission line for the future time period based on the meteorological data for the future time period;

[0014] Determine the target probability of the target fault source causing a secondary fault on the transmission line based on the dancing amplitude, the dancing frequency, and the motion path, and determine a high-risk section of the transmission line;

[0015] Output the target probability and the high-risk section of the transmission line.

[0016] In a possible implementation of the first aspect, determining whether abnormal discharge occurs in the power transmission line in the occurrence area includes:

[0017] In response to receiving the electromagnetic field signal sent by the electromagnetic field sensor, performing a fast Fourier transform on the electromagnetic field signal to obtain a frequency spectrum;

[0018] Extracting spectrum features of the spectrum graph, wherein the spectrum features include amplitude distribution, spectrum graph symmetry, phase distribution, spectrum energy distribution, frequency bandwidth, fundamental frequency harmonic components, and spectrum tilt;

[0019] In the discharge type feature library, determining a target spectrum feature that matches the amplitude distribution, the symmetry of the spectrum graph, and the fundamental frequency harmonic components according to a preset similarity algorithm, and determining a target discharge type corresponding to the target spectrum feature;

[0020] Acquiring a reference characteristic of the target discharge type, and determining whether the target discharge type is abnormal discharge according to the reference characteristic;

[0021] Among them, the benchmark features include benchmark phase distribution, benchmark spectral energy distribution, benchmark frequency bandwidth and benchmark spectral tilt. When there are non-fundamental harmonic components and at least two of the benchmark features are inconsistent with the corresponding spectral features, the target discharge type is determined to be abnormal discharge.

[0022] In another possible implementation of the first aspect, determining a motion path of the current fault source according to the target parameter, and determining a fault source category of the current fault source according to the motion path, the target parameter, the inclination of the tower, and the electromagnetic field change includes:

[0023] Obtain the line height and line location of the transmission line in the occurrence area;

[0024] determining a movement path of the current fault source according to the target parameters, and determining an influence factor and an influence range of the current fault source on the transmission line according to the movement path, the target parameters, the inclination of the tower, the line height, and the line position, wherein the target parameters include height data, length data, and speed data of the current fault source;

[0025] Determine at least one type of fault source to be tested based on the impact factor and the impact range;

[0026] According to the change in the electromagnetic field, a fault source category of a current fault source is determined in at least one of the fault source categories to be detected.

[0027] In another possible implementation of the first aspect, determining a movement path of the current fault source according to the target parameter, and determining an impact factor and an impact range of the current fault source on the transmission line according to the movement path, the target parameter, the inclination of the tower, the line height, and the line position, includes:

[0028] Constructing a motion model of the current fault source according to the height data, length data and speed data of the current fault source;

[0029] Using the motion model, simulating the position change of the current fault source within a preset time period;

[0030] Determine the spatial occupancy area at each time point by combining the height data and the length data;

[0031] Determine the movement path of the current fault source based on the position change of the current fault source within a preset time period and the spatial occupied area at each time point;

[0032] Simulating the movement path of the current fault source, the line height, and the line position to obtain an intersection and an overlapping area between the current fault source and the transmission line, wherein the length of the overlapping area is the contact length, and the area of the overlapping area is used to represent the impact range;

[0033] Using a contact time calculation formula, according to the contact length and the speed data, calculate the contact time between the current fault source and the transmission line;

[0034] The direction of the force exerted by the current fault source on the transmission line is determined according to the inclination of the tower and the speed data, wherein the contact time and the direction of the force are used to characterize the influencing factor.

[0035] In another possible implementation of the first aspect, determining at least one category of a fault source to be detected by combining the impact factor and the impact range includes:

[0036] determining the nature of the current fault source according to the contact time and the direction of the force, wherein the nature of the current fault source includes instantaneous impact and continuous interference;

[0037] In the case where the nature of the current fault source is instantaneous impact, determining at least one type of fault source to be tested in combination with the impact range; or

[0038] In a case where the nature of the current fault source is continuous interference, determining at least one type of fault source to be tested in combination with the impact range;

[0039] The categories of fault sources to be tested include over-height vehicles, cranes, tree obstacles and foreign objects.

[0040] In another possible implementation of the first aspect, determining a fault source category in at least one of the to-be-detected fault source categories according to the change in the electromagnetic field includes:

[0041] Determining the properties of the electromagnetic field change, wherein the properties of the electromagnetic field change include instantaneous properties, slow properties, and continuous properties;

[0042] In a case where the property of the electromagnetic field change is instantaneous, determining that the current fault source is related to metal, and determining a fault source category related to metal in at least one of the fault source categories to be tested;

[0043] In the case that the property of the electromagnetic field change is slow and / or continuous, the current fault source is determined to be related to non-metal, and a fault source category related to non-metal is determined in at least one of the fault source categories to be tested.

[0044] In another possible implementation of the first aspect, the meteorological data includes wind direction data and wind speed data, and predicting the dancing amplitude and dancing frequency of the transmission line in the future time period based on the meteorological data in the future time period includes:

[0045] The wind direction data and the wind speed data of the future time period are input into a preset dynamic equation, and the dynamic equation is solved to obtain the dancing amplitude and dancing frequency of the conductor of the transmission line at each time point in the future time period.

[0046] In another possible implementation of the first aspect, determining a target probability of the target fault source causing a secondary fault on the transmission line and determining a high-risk section of the transmission line by combining the dancing amplitude, the dancing frequency, and the motion path includes:

[0047] determining a stress concentration area on the transmission line within a target area according to the dancing amplitude and the dancing frequency, wherein the target area includes the occurrence area, and the stress concentration area is used to characterize a high-risk section of the transmission line;

[0048] determining an amount of overlap between a stress concentration area on the transmission line and the movement path of the target fault source;

[0049] A value obtained by dividing the overlap number by the length of the power transmission line in the target area is used as the target probability of the target fault source.

[0050] In another possible implementation of the first aspect, determining the stress concentration area on the transmission line within the target area according to the dancing amplitude and the dancing frequency includes:

[0051] Calculating the stress values of the conductor of the transmission line at different dancing amplitudes and dancing frequencies using a preset stress calculation formula;

[0052] The position where the stress value is the largest is regarded as the stress concentration area.

[0053] In a second aspect, the present application provides a transmission line fault identification and early warning system, comprising:

[0054] a memory configured to store instructions; and

[0055] The processor is configured to call the instructions from the memory and implement the above-mentioned transmission line fault identification and early warning method when executing the instructions.

[0056] Through the above technical solution, the fault flashover signal on the transmission line is monitored in real time to locate the fault area, so as to quickly lock the fault area and significantly shorten the fault response time; by detecting abnormal discharge and performing image recognition on the target image, automatic inspection can be achieved in all-weather and complex terrain, effectively improving the accuracy and efficiency of fault identification; the target parameters, tower inclination and electromagnetic field changes detected by the lidar are combined with the movement path of the current fault source to determine the fault source category, which can more accurately locate the type of fault source and reduce false alarms; the dancing frequency and dancing swing amplitude are predicted through meteorological data, and the target probability of the target fault source causing a secondary fault on the transmission line is determined based on the dancing swing amplitude, dancing frequency and movement path, and the high-risk sections of the transmission line are determined, which can realize fault early warning, overcome the problems of long manual inspection cycle and poor effect, and effectively improve the safety and maintenance efficiency of the transmission line.

[0057] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A schematic diagram of a flow chart of a method for identifying and warning faults in a power transmission line provided in an embodiment of the present application;

[0059] Figure 2 A flowchart illustrating the steps of determining the impact factor and impact range of the current fault source on the transmission line provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0061] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0062] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0063] Figure 1 The following schematically shows a flow chart of a method for identifying and warning faults of a power transmission line according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for fault identification and early warning of a power transmission line, which may include the following steps.

[0064] S110, in response to receiving a fault flashover signal, determining an occurrence area of the fault flashover signal, and acquiring a target image of the occurrence area;

[0065] S120, determining whether there is abnormal discharge in the power transmission lines in the occurrence area;

[0066] S130: When abnormal discharge occurs in the power transmission lines in the occurrence area, perform image recognition on the target image to obtain a first image recognition result, where the first image recognition result includes at least one fault source to be detected;

[0067] S140, in response to receiving the target parameters sent by the laser radar, obtaining the inclination of the transmission line tower and the electromagnetic field change of the transmission line in the occurrence area at the current moment;

[0068] S150, determining a movement path of the current fault source according to the target parameters, and determining a fault source category of the current fault source according to the movement path, the target parameters, the inclination of the tower, and the electromagnetic field change;

[0069] S160: Determine a target fault source from the fault sources to be tested according to the fault source category, where the target fault source is used to characterize the current fault source;

[0070] S170: Acquire meteorological data for a future time period, and predict the dancing amplitude and dancing frequency of the power transmission line for the future time period based on the meteorological data for the future time period;

[0071] S180, determining a target probability of a target fault source causing a secondary fault on the transmission line based on the dancing swing amplitude, dancing frequency, and motion path, and determining a high-risk section of the transmission line;

[0072] S190: Output target probability and high-risk sections of transmission lines.

[0073] In the first embodiment, first respond to the received fault flashover signal. The fault flashover signal can be captured by a high-speed camera, that is, the high-speed camera can be used to quickly capture visual images when a fault flashover occurs in the transmission line. The fault flashover signal indicates that an electrical fault may have occurred at a certain position of the transmission line. After receiving the fault flashover signal, the occurrence area of the fault flashover signal can be determined by GPS positioning, and the target image of the occurrence area can be captured by a high-speed camera. The height camera is fixedly installed on the tower of the transmission line. After determining the occurrence area of the fault flashover signal, the high-speed camera in the occurrence area is quickly called to shoot and obtain the target image. The occurrence area refers to the transmission line area where the fault may occur as indicated by the fault flashover signal, including the transmission lines, towers and other facilities within the monitoring range of the high-speed camera.

[0074] After determining the occurrence area, the first step is to determine whether there is abnormal discharge in the occurrence area. Under normal circumstances, there is weak corona discharge on the transmission line. Abnormal discharge refers to discharge phenomena beyond the normal range, which may be caused by reasons such as insulator damage, line loosening, or the proximity of external objects. The presence of abnormal discharge can be determined by electromagnetic signals, sound waves, or electrical parameters. Taking electromagnetic field detection as an example, the steps to determine whether there is abnormal discharge can be: 1. Collect electromagnetic signals through broadband antennas and high-speed sampling equipment. The sampling rate usually needs to reach hundreds of MHz or even GHz level to capture fast discharge pulses. 2. Perform pre-processing such as filtering and denoising on the collected raw electromagnetic signals. 3. Extract the spectral characteristics of the electromagnetic signal, including amplitude, rise time, duration, etc. 4. Compare the spectral characteristics with the corresponding normal baseline characteristics to determine whether there is abnormal discharge.

[0075] If abnormal discharge is confirmed in the occurrence area, image recognition is performed on the target image to obtain a first image recognition result containing at least one fault source to be detected. The fault source to be detected can be an object, equipment failure, or environmental factors. Objects can include overheight vehicles, tree obstacles, kites, birds, etc. Equipment failures can include insulator damage, line loosening, metal component corrosion, etc. Environmental factors can include snow accumulation, ice formation, pollutant deposition, etc.

[0076] The specific implementation steps for image recognition can be as follows: first, preprocess the target image and extract its features using a pretrained deep convolutional neural network. Then, a preset target detection algorithm is used to locate and classify possible fault sources. Finally, at least one fault source and its category are output. Specifically, image preprocessing includes denoising, contrast enhancement, and image segmentation. Denoising is used to reduce noise in the target image; contrast enhancement is used to improve the clarity of the target image; and image segmentation is used to divide the target image into regions of interest (ROIs) to focus on monitoring the transmission lines in the affected area.

[0077] In this embodiment, upon receiving a fault flashover signal, the LiDAR in the area of occurrence simultaneously transmits the target parameters of the current fault source, which are received by the current executing entity. Upon receiving the target parameters, the inclination of the transmission line towers and the electromagnetic field changes of the transmission lines in the area of occurrence at the current moment are obtained. The target parameters include the height, length, and speed data of the current fault source. It should be noted that the current fault source mentioned in this embodiment is an unknown fault source, meaning its type is unknown and only the relevant target parameters of the current fault source can be obtained through the LiDAR.

[0078] Specifically, LiDAR can accurately measure the height, length, and speed of a fault source by emitting laser pulses and measuring the return time of the reflected light. LiDAR can also capture point cloud data of transmission line towers, which can be used to construct a 3D model of the towers.

[0079] The inclination of the transmission line tower can be calculated using the preset RANSAC algorithm. Specifically, the RANSAC algorithm is first used to fit the central axis in the tower point cloud data. Then, the central axis is compared with the vertical line to determine the angle θ. Finally, the angle θ is substituted into the following inclination calculation formula to calculate the inclination. The inclination calculation formula is:

[0080] Inclination=tan(θ)*100%.

[0081] For example, if θ=2° is calculated, the inclination is ≈3.49%.

[0082] The change in the electromagnetic field can be determined by an electromagnetic sensor, that is, the change in the electromagnetic field can be determined by the electric field strength E and the magnetic field strength H detected by the electromagnetic sensor.

[0083] This embodiment uses LiDAR technology to detect physical deformations such as tower tilt and conductor sag with millimeter-level accuracy. Meanwhile, electromagnetic field monitoring can capture electrical anomalies in the line in real time.

[0084] Since the target parameters include the height data, speed data and length data of the current fault source, the movement path of the current fault source can be determined according to the target parameters. After determining the movement path, the category of the current fault source can be determined based on the movement path, target parameters, the inclination of the tower and the electromagnetic field changes.

[0085] Specifically, the motion path of the fault source can be predicted using the current fault source's real-time coordinates and velocity data. For example, if the current fault source's three-dimensional coordinates are (x, y, z), the velocity vector is (vx, vy, vz), and the timestamp is t, a preset target tracking algorithm can be used to track the target, predict its future position, and obtain a smooth motion trajectory. The preset target tracking algorithm can be a Kalman filter algorithm.

[0086] Next, the fault source is classified based on the motion path, target parameters, tower inclination, and electromagnetic field changes. In one embodiment, the speed and direction of the motion path can be analyzed to determine the motion characteristics of the current fault source, such as whether it is fast and irregular or slow and regular. The target parameters can then be used to determine the magnitude of the current fault source; the tower inclination can be used to determine whether the current fault source has physically collided with the transmission line conductors or tower; and the electromagnetic field changes can be used to determine whether the current fault source is metal or non-metal. In summary, the fault source category can be determined.

[0087] In another embodiment, the motion path, target parameters, tower inclination, and electromagnetic field changes may be input into a pre-trained classifier to obtain a probability distribution of fault source categories.

[0088] After determining the fault source category, the most matching target fault source can be identified among the fault sources under test. This can be done by comparing motion characteristics, size, collision status, and metal content. Alternatively, cosine similarity can be used to calculate the similarity of category vectors to determine the most matching target fault source among the fault sources under test. It should be noted that since the category of the target fault source is known, it is considered a known fault source.

[0089] To accurately predict the dynamic behavior of transmission lines under future meteorological conditions, first, meteorological data for the future time period must be obtained, including wind speed and direction (usually expressed as a 10-minute average). This meteorological data includes forecast data for the next 24-72 hours, with a time resolution of hourly or every three hours.

[0090] Next, the dynamic behavior of the transmission line (gait amplitude and frequency) is predicted using meteorological data. The steps can be: 1. Use the finite element method to construct a dynamic model of the conductor. 2. Calculate wind forces based on meteorological data. 3. Use numerical integration methods to solve the dynamic equations and calculate the galloping amplitude and frequency.

[0091] Because the current fault source may not be instantaneous, it is necessary to combine the dancing amplitude, dancing frequency, and fault source motion path to determine the target probability of the target fault source causing a secondary fault on the transmission line. This, in turn, identifies high-risk sections to quantify potential risks and identify high-risk line sections requiring special attention. Secondary faults can include direct contact between the fault source and the transmission line, electrical discharge caused by the fault source, and mechanical stress caused by the fault source exceeding the line's tolerance.

[0092] In one embodiment, the steps for determining the target probability may be: 1. Superimposing the motion path of the target fault source with the predicted dancing area of the transmission line. 2. For each time step, calculating the minimum distance between the fault source and the transmission line. 3. Calculating the probability of electrical discharge based on the electric field strength model and the material properties of the fault source. 4. Calculating the dynamic stress of the line based on the dancing swing amplitude and frequency, and using the fatigue accumulation model to evaluate the probability of failure. 5. Integrating the risk over the entire prediction time period to obtain the target probability. 7. Using a density clustering algorithm (such as DBSCAN) to identify high-risk areas. 8. Determining the high-risk interval segment based on the clustering results and the preset risk threshold. Finally, use a heat map to visualize the risk distribution and intuitively display the high-risk interval segment.

[0093] In a second embodiment, a method for identifying and warning faults in a power transmission line may further include: receiving a fault flashover signal, determining an occurrence area of the fault flashover signal, and obtaining a target image of the occurrence area; determining whether there is abnormal discharge in the transmission line in the occurrence area; if abnormal discharge is present, performing image recognition on the target image to obtain a first image recognition result; receiving target parameters, obtaining the inclination of a tower in the transmission line tower area at the occurrence area at the current moment, as well as changes in electromagnetic field and radar monitoring; determining a motion path of a current fault source based on the target parameters, and determining the fault source category through image capture; determining a target fault source from among the fault sources to be tested based on the fault source category; obtaining future meteorological data monitored and compared with sensor module data through micrometeorological sensors and network meteorological weather, and predicting and obtaining in real time the swing amplitude and swing frequency of the power transmission line in the future and in real time; and automatically initiating image capture to determine the target probability and image data of the target fault source causing a secondary fault on the transmission line based on the swing amplitude, swing frequency, and motion path, tree obstacles, super-high engineering vehicles, floating objects, and lightning strikes, if the swing amplitude, swing frequency, and motion path are greater than a set warning value, determining a high-risk section of the transmission line, and sending warning images and videos to the backend in real time.

[0094] Specifically, compared to the first embodiment, micro-meteorological sensors and network weather data can be used to obtain future meteorological data for comparison with sensor module monitoring. Radar monitoring can also be used to detect changes in tower inclination and electromagnetic fields, enabling real-time monitoring of transmission line status. Furthermore, image capture can be used to identify the fault source type, taking into account factors such as tree obstacles, over-height construction vehicles, floating objects, and lightning strikes. Finally, image capture can be automatically initiated, and after determining the target probability and image data of the target fault source causing a secondary fault on the transmission line, early warning images and videos can be sent to the backend in real time.

[0095] In specific implementation, tree obstacles such as tree growth and tree fall caused by strong winds may come into contact with transmission lines, causing short circuits or ground faults; if the height of engineering vehicles working near transmission lines exceeds the safe distance, they may collide with the lines, causing line damage; strong winds may blow light floating objects such as plastic bags and kites onto transmission lines, causing short circuits or ground faults; direct lightning strikes on transmission lines may cause line damage or tripping, etc. Therefore, factors such as tree obstacles, super-high engineering vehicles, floating objects, and lightning strikes need to be considered in the fault identification and early warning methods of transmission lines, so as to more comprehensively assess the potential risks of transmission lines, issue early warnings and take corresponding protective measures, improve the safety and reliability of transmission lines, and ensure the stable operation of transmission lines in various complex environments.

[0096] This embodiment locates the fault area by real-time monitoring of fault flashover signals on the transmission line, so as to quickly lock the fault area and significantly shorten the fault response time; by detecting abnormal discharges and performing image recognition on the target image, automatic inspection can be achieved in all-weather and complex terrain conditions, effectively improving the accuracy and efficiency of fault identification; the target parameters, tower inclination and electromagnetic field changes detected by the lidar are combined with the movement path of the current fault source to determine the fault source category, which can more accurately locate the type of fault source and reduce false alarms; the dancing frequency and dancing swing amplitude are predicted through meteorological data, and the target probability of the target fault source causing a secondary fault on the transmission line is determined based on the dancing swing amplitude, dancing frequency and movement path, and the high-risk sections of the transmission line are determined, which can realize fault early warning, overcome the problems of long manual inspection cycles and poor results, and effectively improve the safety and maintenance efficiency of the transmission line.

[0097] In one implementation of this embodiment, determining whether abnormal discharge occurs in the power transmission line in the occurrence area includes the following steps:

[0098] S210, in response to receiving the electromagnetic field signal sent by the electromagnetic field sensor, performing a fast Fourier transform on the electromagnetic field signal to obtain a spectrum diagram;

[0099] S220, extracting spectrum features of the spectrum graph, where the spectrum features include amplitude distribution, spectrum graph symmetry, phase distribution, spectrum energy distribution, bandwidth, fundamental frequency harmonic components, and spectrum tilt;

[0100] S230: Determine, in the discharge type feature library, target spectrum features that match the amplitude distribution, spectrum symmetry, and fundamental frequency harmonic components according to a preset similarity algorithm, and determine a target discharge type corresponding to the target spectrum features;

[0101] S240, obtaining a reference feature of the target discharge type, and determining whether the target discharge type is abnormal discharge according to the reference feature;

[0102] Among them, the benchmark features include benchmark phase distribution, benchmark spectral energy distribution, benchmark frequency bandwidth and benchmark spectral tilt. When there are non-fundamental harmonic components and at least two benchmark features are inconsistent with the corresponding spectral features, the target discharge type is determined to be abnormal discharge.

[0103] In this embodiment, a fast Fourier transform (FFT) is first performed on the electromagnetic field signal to obtain a spectrogram. Specifically, the electromagnetic field sensor receives an electromagnetic field signal in the time domain. The electromagnetic field signal is a continuous analog signal that needs to be sampled and quantized to be converted into a discrete digital signal. The sampling frequency should be no less than twice the highest frequency of the signal to avoid spectral aliasing. For electromagnetic field signals from power lines, the sampling frequency can range from several kHz to several MHz. Next, an FFT algorithm (Fast Fourier Transform) is applied to this discrete time series data. The FFT is a method for calculating the discrete Fourier transform (DFT). The core concept is to decompose a DFT of length N into several shorter DFTs and then recursively calculate the shorter DFTs. In specific implementation, a radix-2 FFT algorithm can be used, requiring the input sequence length to be a power of 2. If the original data length does not meet the requirements, it can be adjusted by zero padding or truncation. The output of the FFT is an array of complex numbers, which contains the amplitude and phase information of the signal at different frequencies. By calculating the modulus of the complex number, the amplitude information in the spectrogram can be obtained; by calculating the argument of the complex number, the phase information can be obtained. The resulting spectrogram represents frequency on the horizontal axis and amplitude or power spectral density on the vertical axis.

[0104] Next, key spectral features are extracted from the spectrogram. These spectral features include amplitude distribution, spectrogram symmetry, phase distribution, spectral energy distribution, bandwidth, fundamental frequency harmonic content, and spectral tilt. Amplitude distribution reflects the distribution of signal energy across frequencies and can be quantified by calculating the sum or average of amplitudes within different frequency bins. Spectrogram symmetry can be assessed by comparing the similarity between the positive and negative frequency components. A completely symmetrical spectrogram indicates that the original signal is likely a real-valued signal. Phase distribution provides information about the relative time delays of the signal's frequency components and can be obtained by calculating the phase angle at each frequency point. Spectral energy distribution, obtained by calculating the sum of the squares of the amplitudes at each frequency point in the spectrogram, reflects the distribution of the signal's total energy in the frequency domain. Bandwidth is the frequency range in which signal energy is concentrated and can be measured using metrics such as 3dB bandwidth or equivalent noise bandwidth. Fundamental frequency harmonic content refers to the amplitudes of the fundamental frequency and its integer multiples that appear in the spectrum and can be extracted by identifying the amplitudes at specific frequency points. Spectral tilt reflects the energy distribution of high-frequency components relative to low-frequency components and can be quantified by calculating the energy ratio between the high-frequency and low-frequency regions. Feature extraction can utilize digital signal processing techniques such as filtering, interpolation, and peak detection. For example, to extract the fundamental frequency's harmonic components, a peak detection algorithm can first identify significant peaks in the spectrum and then verify whether these peaks form a harmonic relationship. Bandwidth can be calculated using the cumulative energy method, which accumulates energy starting from the lowest frequencies until it reaches the frequency range corresponding to a certain percentage (e.g., 95%) of the total energy.

[0105] The discharge type feature library contains spectral features for various discharge types, and each discharge type has its own characteristic vector, including amplitude distribution, spectral symmetry, and fundamental frequency harmonic components. The matching process uses a pre-defined similarity algorithm, such as the Euclidean distance algorithm. Specifically, when using the Euclidean distance algorithm, the extracted spectral features and the features in the database are treated as points in a multidimensional space, and the distance between them is calculated. The database feature with the smallest distance is the matching result.

[0106] It should be noted that the matched target spectrum features may correspond to normal discharges or abnormal discharges. Since the features of different discharge types may overlap, relying solely on this implementation may not be completely accurate in classification. Therefore, it is necessary to combine other information to further determine whether the discharge is abnormal.

[0107] After determining the target discharge type, it is further determined whether the discharge is an abnormal discharge. First, the baseline features of the target discharge type are obtained, including the baseline phase distribution, baseline spectral energy distribution, baseline bandwidth, and baseline spectral tilt. The baseline features are used to represent the typical characteristics of this type of discharge under normal conditions. The judgment process mainly includes two aspects: one is to check whether there are non-fundamental harmonic components, and the other is to compare the consistency of the actual features with the baseline features. The presence of non-fundamental harmonic components may mean that an abnormality has occurred during the discharge process. This can be determined by analyzing whether there are significant peaks in the spectrum that are not integer multiples of the fundamental frequency. The comparison of feature consistency can use statistical methods, such as calculating the degree of difference between the actual features and the baseline features. For example, the mean square error or standard deviation can be used to quantify the difference. If the mean square error exceeds a preset threshold, the features are considered inconsistent. When non-fundamental harmonic components are present and at least two features are inconsistent with the baseline, it is determined to be an abnormal discharge. Please refer to the prior art for the calculation of the mean square error or standard deviation, and this application will not elaborate on this.

[0108] This embodiment determines whether abnormal discharge exists through the spectral characteristics of the spectrum diagram, thereby achieving accurate identification of abnormal discharge. It also adopts a multi-feature comprehensive judgment method to improve the accuracy of identification, reduce the possibility of false alarms and missed alarms, and help improve the safety and reliability of transmission lines and reduce the risk of faults.

[0109] In one implementation of this embodiment, determining the motion path of the current fault source according to the target parameter, and determining the fault source category of the current fault source according to the motion path, the target parameter, the inclination of the tower, and the electromagnetic field change includes the following steps:

[0110] S310, obtaining the line height and line location of the transmission line in the occurrence area;

[0111] S320, determining a movement path of the current fault source according to target parameters, and determining an impact factor and impact range of the current fault source on the transmission line according to the movement path, target parameters, tower inclination, line height, and line location, where the target parameters include height data, length data, and speed data of the current fault source;

[0112] S330, determining at least one fault source category to be tested based on the impact factor and the impact range;

[0113] S340: Determine the fault source category of the current fault source in at least one fault source category to be detected according to the change in the electromagnetic field.

[0114] In this embodiment, the line height refers to the vertical distance from the ground, and the line location includes the spatial distribution of the transmission line in a geographic coordinate system. Specifically, once the occurrence area is determined, the line height and line location of the transmission line can be promptly retrieved from a database. The database stores the line heights and line locations of transmission lines at different locations.

[0115] The movement path of the current fault source is determined according to the target parameters. Specifically, first, a movement model of the current fault source is constructed according to the target parameters (including the height data, length data and speed data of the fault source). Next, the movement path of the current fault source is compared with the spatial position of the transmission line, and the change of the shortest distance between the two over time is calculated, which can be achieved by calculating the shortest distance from a point to a line segment. At the same time, the inclination of the tower is taken into account, because the inclination of the tower will change the actual position and tension distribution of the line. The influencing factors include contact time and force direction, where the force direction is determined by the current fault source movement direction and the tower inclination; the contact time refers to the time when the current fault source contacts the transmission line in the occurrence area. The determination of the impact range can be achieved by setting a critical distance. If the distance between the current fault source and the line is less than the critical value, it is determined that the current fault source enters the impact range.

[0116] After determining the influencing factors and the scope of influence, the category of the fault source to be tested can be determined based on the influencing factors and the scope of influence. In one embodiment, the category of the fault source to be tested can be obtained by inputting the relevant data of the influencing factors and the scope of influence into a pre-trained machine learning model. The machine learning model can be a random forest model. The specific construction steps of the random forest model are as follows: 1. Combine the influencing factors (such as the direction of the force and the contact time) and the scope of influence (the area of the affected area) into a feature vector. 2. Use historical data to train the random forest model. Each decision tree randomly selects a feature subset when the node splits to improve the generalization ability of the model. 3. Input the feature vector of the current fault source into the trained model to obtain the probability of each category. 4. The category above the preset threshold is used as the category of the fault source to be tested.

[0117] Based on the category of the fault source to be measured determined in S330, combined with the electromagnetic field change information, the specific category of the current fault source can be determined. First, the electromagnetic field change data is obtained by installing an electromagnetic field sensor array around the transmission line, and the characteristics of the electromagnetic field change are extracted. Next, these electromagnetic field characteristics are compared with the category of the fault source to be measured determined in step S330 to determine the fault source category of the current fault source. Among them, the characteristics of the electromagnetic field change include frequency domain characteristics, time domain characteristics, and time-frequency characteristics. The time domain characteristics include characteristics such as spectrum peaks and harmonic ratios obtained by fast Fourier transform (FFT). The time domain characteristics include statistical quantities such as the mean, variance, peak factor, skewness, and kurtosis of the signal. The time-frequency characteristics include time-frequency distribution characteristics obtained by wavelet transform or Hilbert-Huang transform.

[0118] This implementation method greatly improves the accuracy of identification by gradually narrowing the range of possible fault sources and comprehensively considering physical and electromagnetic characteristics. It can not only determine the type of fault source, but also evaluate its potential impact on the transmission line, providing strong support for preventive maintenance and emergency response, and helping to improve the safety and reliability of transmission lines.

[0119] Figure 2 The flowchart of the steps for determining the impact factor and impact range of the current fault source on the transmission line provided by the embodiment of the present application is shown as follows: Figure 2 As shown, in one implementation of this embodiment, the movement path of the current fault source is determined according to the target parameters, and the influence factor and influence range of the current fault source on the transmission line are determined according to the movement path, the target parameters, the inclination of the tower, the line height and the line position, including the following steps:

[0120] S410: Construct a motion model of the current fault source based on the height data, length data, and speed data of the current fault source:

[0121] S420: Using a motion model to simulate the position change of the current fault source within a preset time period;

[0122] S430, combining the height data and the length data to determine the space occupied area at each time point;

[0123] S440, determining a movement path of the current fault source based on a position change of the current fault source within a preset time period and a spatial occupied area at each time point;

[0124] S450: Simulate the movement path, line height, and line position of the current fault source to obtain the intersection and overlapping area between the current fault source and the transmission line. The length of the overlapping area is the contact length, and the area of the overlapping area is used to represent the impact range.

[0125] S460: Using a contact time calculation formula, calculate the contact time between the current fault source and the transmission line based on the contact length and speed data;

[0126] S470. Determine the direction of the force exerted by the current fault source on the transmission line based on the inclination and speed data of the tower, wherein the contact time and the direction of the force are used to characterize the influencing factors.

[0127] First, the physical characteristics of the fault source, including its shape and aerodynamic properties, need to be determined. Height data provides the initial position of the fault source, length data is used to determine its geometric dimensions, and velocity data provides its initial motion state.

[0128] When constructing the motion model, this embodiment uses a three-dimensional coordinate system to describe the current location of the fault source. Taking into account the influence of gravity, a parabolic model can be used to describe the fault source's motion trajectory. Horizontally, assuming negligible air resistance, the fault source maintains constant motion. Vertically, the fault source experiences uniform acceleration due to gravity. The specific motion equation can be expressed as:

[0129] x(t)=x0+υ x ×t;

[0130] y(t)=y0+υ y ×t-0.5×g×t 2 ;

[0131] Among them, (x0, y0) is the initial position, v x and v y are the initial velocity components in the horizontal and vertical directions, respectively, and g is the acceleration due to gravity (about 9.8m / s 2 ).

[0132] Next, the motion model constructed above is extrapolated over time to simulate the position changes of the current fault source within a preset time period. Using numerical integration methods, the motion equations are discretized and solved to obtain the position coordinates of the current fault source at different time points.

[0133] During implementation, the simulation timeframe and time step must be determined. The timeframe depends on the expected impact of the fault source and can range from a few seconds to several minutes. The time step can be in the order of 0.01 to 0.1 seconds.

[0134] Specifically, for each time step, the corresponding x and y coordinates are calculated to obtain the position of the current fault source at each time point.

[0135] Combined with the height data and length data of the current fault source, the spatial occupied area of the current fault source at each time point is determined, which is mainly used to expand the point-like location information into a spatial area with actual size.

[0136] During implementation, the first step is to select an appropriate geometric model based on the shape of the current fault source. For slender current fault sources (such as tree branches), a line segment model can be used; for larger current fault sources (such as overheight vehicles), a rectangular or elliptical model can be used. Taking the line segment model as an example, the implementation steps are as follows:

[0137] 1. For each time point t, obtain the position coordinates (x(t), y(t)) calculated in S420.

[0138] 2. Calculate the coordinates of the two endpoints of the line segment based on the length L of the current fault source and the current motion direction θ(t):

[0139] Endpoint 1: (x1(t), y1(t))=(x(t)-0.5L*cos(θ(t)), y(t)-0.5L*sin(θ(t)));

[0140] Endpoint 2: (x2(t), y2(t))=(x(t)+0.5L*cos(θ(t)), y(t)+0.5L*sin(θ(t)));

[0141] 3. Connect the two endpoints to form a line segment representing the current fault source.

[0142] Similarly, for the rectangular model, the implementation steps are as follows:

[0143] 1. Get the position coordinates (x(t), y(t)) as the center of the rectangle.

[0144] 2. Based on the length L and width W of the current fault source and the current direction θ(t), calculate the coordinates of the four vertices of the rectangle:

[0145] Vertex 1: (x(t)-0.5L*cos(θ(t))+0.5W*sin(θ(t)), y(t)-0.5L*sin(θ(t))-0.5W*cos(θ(t)));

[0146] Vertex 2: (x(t)+0.5L*cos(θ(t))+0.5W*sin(θ(t)), y(t)+0.5L*sin(θ(t))-0.5W*cos(θ(t)));

[0147] Vertex 3: (x(t)+0.5L*cos(θ(t))-0.5W*sin(θ(t)), y(t)+0.5L*sin(θ(t))+0.5W*cos(θ(t)));

[0148] Vertex 4: (x(t)-0.5L*cos(θ(t))-0.5W*sin(θ(t)), y(t)-0.5L*sin(θ(t))+0.5W*cos(θ(t))).

[0149] 3. Connect the four vertices to form a rectangle representing the current fault source.

[0150] Through the above implementation, the specific spatial occupied area of the current fault source at each time point can be obtained.

[0151] Next, the complete motion path of the current fault source is determined by comprehensively considering its position changes over a preset time period and the spatially occupied area at each time point. Specifically, the position coordinate sequence obtained in S420 and the spatially occupied area information calculated in S430 are first integrated. For each time point t, the center point coordinates (x(t), y(t)) and the geometric description of the spatially occupied area, such as the coordinates of line segment endpoints or rectangle vertices, are known.

[0152] For the center point trajectory, a spline interpolation method (such as cubic spline interpolation) can be used to connect the center coordinates of all time points to generate a smooth curve. The interpolation function can be expressed as:

[0153] x(t)=a i +b i (tt i )+c i (tt i ) 2 +d i (tt i ) 3 ;

[0154] y(t)=e i +f i (tt i )+g i (tt i ) 2 +h i (tt i ) 3 ;

[0155] Among them, i represents the interval between adjacent time points, a i to h i is the interpolation coefficient.

[0156] For the spatial occupied area, a linear interpolation method is used to connect the boundary points of the spatial occupied area at adjacent time points to form a continuous envelope surface. A preset smoothing algorithm is then applied to the generated trajectory to smooth out possible noise and discontinuities. The envelope surface represents all the spaces that the current fault source may occupy during the motion process. Finally, a parameterized equation is used to represent the final motion path:

[0157] R(t)=(x(t), y(t), S(t));

[0158] Among them, (x(t), y(t)) are the coordinates of the center point, and S(t) is the spatial occupied area at time t.

[0159] The above implementation method can generate a continuous, smooth motion path that takes actual size into consideration, which can better reflect the actual motion situation of the current fault source than a point sequence.

[0160] Afterwards, the motion path, line height, and line position of the current fault source can be simulated to determine potential intersections and overlapping areas, and accurately quantify the interaction between the current fault source and the transmission line. Specifically, for each time step, check whether the spatial occupied area of the current fault source intersects with the transmission line. Specifically, a preset line segment intersection algorithm (such as a line segment intersection algorithm) can be used to detect the intersection, and for the time period when the intersection is detected, calculate the overlapping part of the spatial occupied area of the current fault source and the transmission line. The polygon clipping algorithm can be used to calculate the area of the overlapping area. The total contact length is obtained by adding up the projected lengths of all overlapping areas on the transmission line. For each overlapping area, the contact length can be obtained by calculating the maximum span of the overlapping polygon in the line direction.

[0161] The contact time is calculated based on the contact length determined in S450 and the speed data of the current fault source. The contact time is obtained by dividing the contact length by the relative speed of the current fault source. First, the effective speed of the current fault source relative to the transmission line is determined. Assume that the speed vector of the current fault source is v = (v x , v y ), the direction vector of the transmission line is I=(I x , I y ), then the relative velocity can be calculated by projection:

[0162] υ=|υ.l| / |l|=|υ x *l x +υ y *l y | / √(lx 2 +ly 2 );

[0163] Next, use the contact time calculation formula:

[0164] T=L / v ; Where T is the contact time and L is the contact length.

[0165] However, the above basic formula assumes that the speed of the current fault source remains constant throughout the contact process, which may not be true in reality. To improve the calculation accuracy, a segmented calculation method can be used. The entire contact process is divided into several small segments, and the average speed of each segment is calculated. The total contact time is then accumulated to obtain the following formula:

[0166] T t =∑L i / υ i ;

[0167] Among them, L i is the contact length of segment i, v i is the average relative velocity of the segment.

[0168] Based on the tower inclination and speed data, we can determine the direction of the force acting on the transmission line from the current fault source, as well as the impact of the tower inclination on the force on the transmission line. The tower inclination will change the tension distribution and geometry of the transmission line, thereby affecting the line's response to external forces. Assuming the tower inclination angle is θ, the force on the transmission line can be decomposed into two components: horizontal and vertical. The horizontal component is F h , the vertical component is F v , the formula is as follows:

[0169] F h =F*cos(θ);

[0170] F υ =F*sin(θ);

[0171] Where F is the total force applied by the current fault source.

[0172] Next, consider the velocity data of the current fault source. The velocity not only determines the magnitude of the impact force, but also determines the direction of the force. Assume that the velocity vector of the current fault source is v = (v x , v y ), then its direction of movement can be calculated:

[0173] α=atan2(υ y ,υ x );

[0174] Among them, atan2 is a function used to calculate the azimuth angle α.

[0175] Combining the tower tilt and the current fault source movement direction, the direction of the force in the transmission line coordinate system can be obtained:

[0176] β=α-θ;

[0177] The direction vector of the force can be expressed as: d = (cos(β), sin(β)).

[0178] This embodiment, by combining spatial occupancy area information, can more accurately assess the potential collision risk between the current fault source and the transmission line, effectively improving the safety and operating efficiency of the transmission line.

[0179] In one implementation of this embodiment, determining at least one type of fault source to be detected by combining the impact factor and the impact range includes the following steps:

[0180] S510. Determine the nature of the current fault source based on the contact time and the direction of the force. The nature of the current fault source includes instantaneous impact and continuous interference.

[0181] S520: When the nature of the current fault source is instantaneous impact, determine at least one type of fault source to be tested in combination with the impact range; or

[0182] S530: If the nature of the current fault source is continuous interference, determine at least one fault source category to be tested based on the impact range;

[0183] Among them, the categories of fault sources to be tested include over-height vehicles, cranes, tree obstacles and foreign objects.

[0184] This embodiment can determine the nature of the current fault source based on the contact time and the direction of the force, and classify it as a transient impact or a continuous interference. Specifically, if the contact time T is less than the critical contact time threshold, it tends to be judged as a transient impact; otherwise, it may be a continuous interference. The critical contact time threshold can be 0.5 seconds. Next, a direction factor D can be defined to represent the cosine value of the angle between the direction of the force and the normal direction of the transmission line. When D is close to 1, it means that the direction of the force is almost perpendicular to the line, which is more likely to cause a transient impact; when D is close to 0, it means that the direction of the force is almost parallel to the line, which is more likely to cause a continuous interference. D can be calculated using the following formula:

[0185] D = |F·n| / (|F|*|n|);

[0186] Where F is the force vector and n is the normal vector of the line at the contact point.

[0187] By analyzing the characteristics and impact range of the transient impact, the most likely candidate can be selected from the preset fault source categories (including overheight vehicles, cranes, tree obstacles, and foreign objects). For transient impacts, the impact range is usually manifested as a relatively concentrated area. This area can be described using the area A of the impact range and the shape factor S. The shape factor can be defined as the ratio of the square of the perimeter of the impact area to the area, that is, S = P 2 / A, where P is the perimeter. Different types of fault sources have different characteristic values. For example, an over-height vehicle may cause a larger but more regular impact area (smaller S value), while a foreign object such as a tree branch may cause a smaller but irregular impact area (larger S value).

[0188] A similarity function can be constructed to compare the observed impact range with the characteristics of various fault sources in the database:

[0189] S(i)=w1*exp(-|AA i | / σA)+w2*exp(-|SS i | / σS);

[0190] The function exp(x) represents the natural exponential function, that is, the exponential function with the real number e (e≈2.71828) as the base, A and S are the observed values, A i and S i is the characteristic value of the i-th type fault source in the database, σA and σS are standardized parameters, and w1 and w2 are weight coefficients.

[0191] For fault sources identified as persistent interference, the specific fault source category to be tested can be determined based on the impact range. Sustained interference typically manifests as a long-term effect on the transmission line, and its impact range may vary over time, so time series data needs to be considered.

[0192] First, a time series feature database is constructed, containing typical impact range change patterns and other relevant parameters for various potential persistent interference fault sources. For persistent interference, the following features can be used to describe the impact range change: 1. The time function A(t) of the impact range area; 2. The time function S(t) of the impact range shape factor; 3. The movement trajectory P(t) = (x(t), y(t)) of the impact center position; and 4. The time function F(t) of the interference force magnitude.

[0193] Time series similarity metrics, such as the Dynamic Time Warping algorithm, can be used to compare the observed impact range time series with the characteristic series of various fault sources in the database. The Dynamic Time Warping algorithm can handle time series of different lengths and rates to calculate the similarity between them.

[0194] The distance of the dynamic time warping algorithm can be expressed as:

[0195] DTW(X, Y)=min(∑d(x i ,y j ));

[0196] Where DTW(X, Y) represents the distance of the dynamic time warping algorithm, X and Y are two time series, d(x i ,y j ) is a single point distance metric.

[0197] In addition to time series similarity, some statistical features can also be extracted, such as: the mean and standard deviation of the impact range area; the rate of change of the shape factor; the total distance and average speed of the impact center movement; the maximum, minimum and average values of the interference force.

[0198] Machine learning algorithms, such as long short-term memory (LSTM) or temporal convolutional networks (TCN), can then be used to process time series data and statistical features. Machine learning algorithms can capture long-term dependencies in time series and determine the final list of fault source categories to be tested. The specific implementation steps are as follows: 1. Collect time series data for the affected area. 2. Use the DTW algorithm to calculate the similarity between the observed sequence and the characteristic sequences of various fault sources in the database. 3. Extract statistical features and construct a feature vector V. 4. Input the time series data and feature vector into the trained LSTM or TCN model. 5. The model outputs the probability distribution of each fault source category. 6. Combine the DTW similarity and the model output probability to determine the final list of fault source categories to be tested.

[0199] This implementation effectively distinguishes between two basic fault types: instantaneous impact and sustained interference by analyzing contact time and force direction. For instantaneous impact, combining impact range characteristics with machine learning techniques enables accurate identification of fast-moving fault sources such as overheight vehicles and cranes. For sustained interference, time series analysis and deep learning methods effectively handle complex, long-term impacts such as tree obstacles. This system not only adapts to different fault sources but also improves identification accuracy through continuous learning and updating, providing more comprehensive and accurate fault source identification results for transmission line safety management.

[0200] In one implementation manner of this embodiment, determining a fault source category in at least one fault source category to be detected according to changes in the electromagnetic field includes the following steps:

[0201] S610, determining the nature of the electromagnetic field change, where the nature of the electromagnetic field change includes instantaneous nature, slow nature, and continuous nature;

[0202] S620: If the electromagnetic field change is instantaneous, determine that the current fault source is related to metal, and determine a metal-related fault source category in at least one fault source category to be tested;

[0203] S630: When the electromagnetic field changes slowly and / or continuously, determine that the current fault source is related to non-metal, and determine a fault source category related to non-metal in at least one fault source category to be tested.

[0204] This embodiment first uses short-time Fourier transform (STFT) to perform time-frequency analysis on the processed signal. Short-time Fourier transform can provide spectral information of the signal in different time windows. By analyzing the STFT results, the following features can be obtained: 1. The instantaneous rate of change of the spectrum, which can be obtained by calculating the difference between the spectra of adjacent time windows. 2. The duration of the main frequency component, which can be obtained by tracking the existence time of a specific frequency component. 3. The change pattern of energy distribution, which can be obtained by the change of energy in different frequency bands over time.

[0205] Based on the above characteristics, we can define the following judgment criteria: Instantaneous properties: The instantaneous rate of change of the spectrum is high, and the duration of the main frequency component is short (usually less than 1 second). Slow properties: The rate of change of the spectrum is moderate, and the duration of the main frequency component is between 1 second and several minutes. Sustained properties: The rate of change of the spectrum is low, and the duration of the main frequency component is long (more than several minutes).

[0206] When metal objects interact with electromagnetic fields, they generate eddy currents and electromagnetic induction. Specifically, metal objects often cause significant instantaneous field strength changes and generate significant harmonics in specific frequency bands. Therefore, for electromagnetic field changes that are determined to be transient, the current fault source is determined to be metal-related, and the specific metal-related fault source category is identified within the fault source categories to be tested.

[0207] In practical applications, the specific type of metal object must also be considered. For example, an over-height vehicle may manifest as a larger peak amplitude and faster rise / fall times. A crane may manifest as a strong signal with a slightly longer duration and periodic variations. A foreign metal object may manifest as a smaller peak amplitude but with unique spectral characteristics.

[0208] The interaction between non-metallic objects and electromagnetic fields typically manifests as slow or continuous changes. This is because non-metallic objects do not generate the strong eddy currents and electromagnetic induction effects that metals do. Instead, non-metallic objects may affect the field intensity distribution by changing the electromagnetic properties of the surrounding medium (such as the dielectric constant) or by physically blocking the propagation of electromagnetic waves. Therefore, for electromagnetic field changes that are determined to be slow and / or continuous, the current fault source is determined to be non-metal-related, and the specific non-metal-related fault source category is identified within the fault source categories to be tested.

[0209] In practical applications, the characteristics of different non-metallic fault sources also need to be considered: Tree obstructions may exhibit seasonal variations (such as leaf growth cycles) and random fluctuations due to wind. Plastic foreign objects (such as kites) may exhibit intermittent, weak field strength variations. Meteorological factors (such as fog and rain) may cause slow variations in overall field strength and correlate with meteorological data.

[0210] This embodiment can distinguish different types of interference sources by accurately capturing the temporal characteristics of electromagnetic field changes, and fully utilizes the physical characteristics of the interaction between metal objects and electromagnetic fields to achieve high-accuracy fault source classification, effectively ensuring the timeliness and accuracy of early warnings, and further effectively improving the safety of transmission lines.

[0211] In one implementation of this embodiment, the meteorological data includes wind direction data and wind speed data. Predicting the dancing amplitude and dancing frequency of the power transmission line in the future time period based on the meteorological data in the future time period includes the following steps:

[0212] S710: Input the wind direction data and wind speed data of the future time period into a preset dynamic equation, and solve the dynamic equation to obtain the dancing amplitude and dancing frequency of the conductor of the transmission line at each time point in the future time period.

[0213] The wind direction and speed data for the future time period are input into a preset dynamic equation, and the equation is solved to predict the dancing amplitude and dancing frequency of the transmission line conductor at each time point in the future time period. The dynamic equation in this embodiment is as follows:

[0214]

[0215] Where y is the vertical displacement of the wire at position x and time t, ρ is the linear density of the wire, A is the cross-sectional area of the wire, E is Young's modulus, I is the moment of inertia, T is the tension in the wire, and F(x, t) is the wind force acting on the wire.

[0216] The wind force F(x, t) can be expressed as:

[0217] F(x,t)=0.5×Cd×ρa×d×V(t)2 ×sin(θ(t));

[0218] Where Cd is the drag coefficient, ρa is the air density, d is the wire diameter, V(t) is the wind speed, and θ(t) is the angle between the wind direction and the perpendicular direction of the wire.

[0219] Substitute the wind direction data θ(t) and wind speed data V(t) for the future time period into the above equation. The finite element method is then used to solve the partial differential equations, discretizing the conductor into multiple units and transforming the original problem into a system of ordinary differential equations. The system of ordinary differential equations can then be iteratively solved using the time integration method to obtain the conductor displacement q(t) at each time point. From this displacement data, the dancing amplitude (maximum displacement amplitude) and the dancing frequency (the dominant frequency component of the displacement) can be extracted.

[0220] This implementation combines weather forecast data with precise physical models to accurately predict the amplitude and frequency of conductor galloping over future time periods. This not only accounts for variations in wind direction and speed, but also simulates the complex dynamic behavior of conductors. This allows grid managers to proactively identify potential risks and develop targeted preventative measures, significantly improving the safety and reliability of transmission lines and enhancing the overall operational efficiency of the grid.

[0221] In one implementation of this embodiment, combining the dancing amplitude, dancing frequency, and motion path, determining the target probability of a target fault source causing a secondary fault on a transmission line, and determining a high-risk section of the transmission line, includes the following steps:

[0222] S810, determining a stress concentration area on the transmission line within a target area based on the galloping amplitude and the galloping frequency, where the target area includes an occurrence area, and the stress concentration area is used to characterize a high-risk section of the transmission line;

[0223] S820, determining the number of overlaps between the stress concentration area on the transmission line and the movement path of the target fault source;

[0224] S830: Divide the number of overlaps by the length of the transmission lines in the target area to obtain a value as the target probability of the target fault source.

[0225] The stress concentration area in the target area is determined based on the dancing swing amplitude and dancing frequency of the transmission line. The stress concentration area is regarded as a high-risk section of the transmission line. The dancing swing amplitude reflects the maximum distance that the conductor deviates from the equilibrium position, while the dancing frequency indicates the speed at which the conductor vibrates. The dancing swing amplitude and dancing frequency together determine the dynamic stress borne by the conductor. Generally speaking, larger swing amplitudes and higher frequencies will result in greater stress. In order to determine the stress concentration area, the stress distribution can be calculated along the length of the conductor. The specific implementation steps can be 1. Discretize the conductor into multiple units. 2. For each unit, calculate the local dancing swing amplitude and frequency according to its position. 3. Use the preset stress calculation formula to calculate the dynamic stress of each unit. 4. Set a stress threshold and mark the area exceeding this threshold as a stress concentration area.

[0226] Afterwards, the number of overlaps between the stress concentration areas on the transmission line and the movement path of the target fault source is determined to quantify the likelihood of secondary faults.

[0227] First, the stress concentration area determined in S810 and the target fault source movement path need to be represented in the same spatial coordinate system. The stress concentration area can be represented as a series of line segments or intervals on the transmission line, and the movement path of the fault source can be represented as a continuous curve or a discrete point sequence. Next, spatial analysis is needed to determine the overlap. The specific implementation steps may be: 1. Represent the stress concentration area as a series of discrete points, each point represents a small area unit. If the fault source movement path is a continuous curve, it is also discretized into a point sequence. 2. Use spatial indexing technology, such as R-tree or quadtree, to organize the spatial data of the stress concentration area. 3. For each point on the fault source movement path, search for stress concentration area points nearby. If the distance between a point on the fault source path and a stress concentration area point is less than d, it is considered that an overlap occurs. 4. Count the number of times the overlap condition is met.

[0228] The target probability is calculated as follows:

[0229] P = N / L;

[0230] Where P is the target probability, N is the number of overlaps, and L is the total length of the transmission lines in the target area. In other words, the greater the number of overlaps, or the higher the overlap ratio relative to the total line length, the greater the probability of a secondary failure.

[0231] This embodiment can significantly improve the safety and reliability of transmission lines, reduce the possibility of accidents, and ultimately ensure the stable operation of the power system by determining the target probability of a target fault source causing a secondary fault on the transmission line and identifying high-risk sections of the transmission line.

[0232] In one implementation of this embodiment, determining the stress concentration area on the transmission line within the target area according to the dancing amplitude and the dancing frequency includes the following steps:

[0233] S910, using a preset stress calculation formula to calculate stress values of the conductor of the transmission line at different dancing amplitudes and dancing frequencies;

[0234] S920. The position with the maximum stress value is regarded as the stress concentration area.

[0235] This embodiment first uses a preset stress calculation formula to calculate the stress values of the transmission line conductor at different dancing amplitudes and dancing frequencies, thereby quantifying the mechanical stress experienced by the conductor under dynamic conditions through a physical model. Specifically, a dynamic stress model suitable for the transmission line conductor is first constructed. The dynamic stress model in this embodiment is constructed based on catenary theory, and the stress calculation formula is as follows:

[0236] σ t =σ s +σ d ;

[0237] Among them, σ t is the total stress, σ s is the static stress, σ d is the dynamic stress.

[0238] Static stress σ s It can be calculated by the catenary equation, which is as follows:

[0239] σ s=(w×L 2 ) / (8×d);

[0240] Where w is the weight per unit length of the conductor, L is the span length, and d is the conductor sag. d The influence of dancing amplitude and dancing frequency needs to be considered. The following formula can be used:

[0241] σ d =E×y×(2π×f) 2 ×A / g;

[0242] Where E is the Young's modulus of the wire, y is the dancing amplitude, f is the dancing frequency, A is the cross-sectional area of the wire, and g is the acceleration due to gravity.

[0243] In practical applications, the transmission line needs to be discretized and divided into multiple calculation units. For each unit, the dancing swing amplitude, frequency, and environmental parameters of different sub-regions of the target area are used for calculation. This can be achieved by the following steps: 1. Divide the transmission line into N equal-length units. 2. For each unit i (i = 1, 2, ..., N): a. Obtain the local dancing swing amplitude y of the unit i and frequency f i b. Obtain the environmental parameters of each sub-area (temperature, wind speed, etc.). c. Apply the above stress calculation formula to calculate σ f 3. Store the stress value of each unit and form a stress distribution map of the entire transmission line in the sub-area.

[0244] Next, through the stress distribution map, we can identify the key areas that are most prone to failure or damage, that is, the stress concentration areas. Specifically, first traverse all calculation units and find the location with the maximum stress value. You can use the comparison algorithm:

[0245] σ max =max(σ f ), i=1, 2, ..., N;

[0246] Where N is the total number of units.

[0247] Secondly, a stress threshold is set based on the yield strength or fatigue limit of the wire material. For example, 80% of the material's yield strength can be used as the threshold. Continuous regions where stress values exceed the stress threshold are defined as stress concentration regions. The steps are as follows: a. Initialize an empty list of stress concentration regions. b. Traverse all calculation units. When the stress value exceeds the stress threshold, a new stress concentration region begins. c. Traverse adjacent units until the stress value drops below the stress threshold, at which point the definition of the current stress concentration region ends. d. Repeat steps b and c until all units have been traversed and all stress concentration regions are obtained.

[0248] For each identified stress concentration area, the line length covered by the stress concentration area, the maximum stress value in the stress concentration area, and the rate of stress change in the stress concentration area are determined, and weights are assigned to the line length covered by the stress concentration area, the maximum stress value in the stress concentration area, and the rate of stress change in the stress concentration area respectively. The stress values used to characterize the stress concentration area can be obtained, the stress values are sorted, and the stress concentration area corresponding to the largest stress value is regarded as the key stress concentration area, indicating that the key stress concentration area has the greatest risk.

[0249] It should be noted that for special structural locations, such as insulator connections and special spans across obstacles, special attention still needs to be paid even if the calculated stress value is not the highest.

[0250] This refined analysis can provide precise guidance for transmission line maintenance and overhaul. For example, targeted inspection plans can be developed to prioritize high-risk stress concentration areas or install additional monitoring equipment in these areas. This approach can also be used to evaluate the effectiveness of improvement measures, quantifying safety improvements by comparing stress distribution before and after improvements.

[0251] By identifying stress concentration areas, this implementation can accurately locate high-risk areas on transmission lines, helping to improve the safety and reliability of transmission lines, reduce failure risks, optimize resource allocation, and ultimately achieve the goal of improving the stability of the entire power system.

[0252] The present application also provides a transmission line fault identification and warning system, including:

[0253] a memory configured to store instructions; and

[0254] The processor is configured to call instructions from the memory and implement the above-mentioned transmission line fault identification and early warning method when executing the instructions.

[0255] An embodiment of the present application further provides a machine-readable storage medium having stored thereon instructions for enabling a machine to execute the above-mentioned method for identifying and warning faults of power transmission lines.

[0256] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0257] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0258] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0259] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0260] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0261] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0262] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0263] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0264] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for identifying and warning faults in a power transmission line, characterized in that: include: In response to receiving a fault flashover signal, determining an occurrence area of the fault flashover signal and acquiring a target image of the occurrence area; determining whether there is abnormal discharge in the power transmission lines in the occurrence area; In the case where abnormal discharge occurs in the power transmission line in the occurrence area, performing image recognition on the target image to obtain a first image recognition result, wherein the first image recognition result includes at least one fault source to be detected; In response to receiving the target parameters sent by the laser radar, obtaining the inclination of the transmission line tower in the occurrence area and the electromagnetic field change of the transmission line at the current moment; determining a movement path of a current fault source according to the target parameter, and determining a fault source category of the current fault source according to the movement path, the target parameter, the inclination of the tower, and the electromagnetic field change; Determining a target fault source among the fault sources to be tested according to the fault source category, wherein the target fault source is used to characterize the current fault source; Acquiring meteorological data for a future time period, and predicting the dancing amplitude and dancing frequency of the transmission line for the future time period based on the meteorological data for the future time period; Determine the target probability of the target fault source causing a secondary fault on the transmission line based on the dancing amplitude, the dancing frequency, and the motion path, and determine a high-risk section of the transmission line; Output the target probability and the high-risk section of the transmission line.

2. The method according to claim 1, characterized in that The determining whether there is abnormal discharge in the transmission line of the occurrence area includes: In response to receiving the electromagnetic field signal sent by the electromagnetic field sensor, performing a fast Fourier transform on the electromagnetic field signal to obtain a frequency spectrum; Extracting spectrum features of the spectrum graph, wherein the spectrum features include amplitude distribution, spectrum graph symmetry, phase distribution, spectrum energy distribution, frequency bandwidth, fundamental frequency harmonic components, and spectrum tilt; In the discharge type feature library, determining a target spectrum feature that matches the amplitude distribution, the symmetry of the spectrum graph, and the fundamental frequency harmonic components according to a preset similarity algorithm, and determining a target discharge type corresponding to the target spectrum feature; Acquiring a reference characteristic of the target discharge type, and determining whether the target discharge type is abnormal discharge according to the reference characteristic; Among them, the benchmark features include benchmark phase distribution, benchmark spectral energy distribution, benchmark frequency bandwidth and benchmark spectral tilt. When there are non-fundamental harmonic components and at least two of the benchmark features are inconsistent with the corresponding spectral features, the target discharge type is determined to be abnormal discharge.

3. The method according to claim 1, characterized in that The determining of the movement path of the current fault source according to the target parameter, and determining the fault source category of the current fault source according to the movement path, the target parameter, the inclination of the tower, and the electromagnetic field change, includes: Obtain the line height and line location of the transmission line in the occurrence area; determining a movement path of the current fault source according to the target parameters, and determining an influence factor and an influence range of the current fault source on the transmission line according to the movement path, the target parameters, the inclination of the tower, the line height, and the line position, wherein the target parameters include height data, length data, and speed data of the current fault source; Determine at least one type of fault source to be tested based on the impact factor and the impact range; According to the change in the electromagnetic field, a fault source category of a current fault source is determined in at least one of the fault source categories to be detected.

4. The method according to claim 3, characterized in that Determining the movement path of the current fault source according to the target parameter, and determining the influence factor and influence range of the current fault source on the transmission line according to the movement path, the target parameter, the inclination of the tower, the line height, and the line position, includes: Constructing a motion model of the current fault source according to the height data, length data and speed data of the current fault source; Using the motion model, simulating the position change of the current fault source within a preset time period; Determine the spatial occupancy area at each time point by combining the height data and the length data; Determine the movement path of the current fault source based on the position change of the current fault source within a preset time period and the spatial occupied area at each time point; Simulating the movement path of the current fault source, the line height, and the line position to obtain an intersection and an overlapping area between the current fault source and the transmission line, wherein the length of the overlapping area is a contact length, and the area of the overlapping area is used to represent the impact range; Using a contact time calculation formula, according to the contact length and the speed data, calculate the contact time between the current fault source and the transmission line; The direction of the force exerted by the current fault source on the transmission line is determined according to the inclination of the tower and the speed data, wherein the contact time and the direction of the force are used to characterize the influencing factor.

5. The method according to claim 4, characterized in that The determining of at least one type of fault source to be detected by combining the influencing factor and the influencing range includes: determining the nature of the current fault source according to the contact time and the direction of the force, wherein the nature of the current fault source includes instantaneous impact and continuous interference; In the case where the nature of the current fault source is instantaneous impact, determining at least one type of fault source to be tested in combination with the impact range; or In a case where the nature of the current fault source is continuous interference, determining at least one type of fault source to be tested in combination with the impact range; The categories of fault sources to be tested include over-height vehicles, cranes, tree obstacles and foreign objects.

6. The method according to claim 5, characterized in that Determining a fault source category in at least one of the fault source categories to be detected based on the change in the electromagnetic field includes: determining a property of the electromagnetic field change, wherein the property of the electromagnetic field change includes an instantaneous property, a slow property, and a continuous property; In a case where the property of the electromagnetic field change is instantaneous, determining that the current fault source is related to metal, and determining a fault source category related to metal in at least one of the fault source categories to be tested; In the case that the property of the electromagnetic field change is slow and / or continuous, the current fault source is determined to be related to non-metal, and a fault source category related to non-metal is determined in at least one of the fault source categories to be tested.

7. The method according to claim 1, characterized in that The meteorological data includes wind direction data and wind speed data. The predicting of the dancing amplitude and dancing frequency of the power transmission line in the future time period based on the meteorological data in the future time period includes: The wind direction data and the wind speed data of the future time period are input into a preset dynamic equation, and the dynamic equation is solved to obtain the dancing amplitude and dancing frequency of the conductor of the transmission line at each time point in the future time period.

8. The method according to claim 1, characterized in that The step of determining a target probability of the target fault source causing a secondary fault on the transmission line by combining the dancing amplitude, the dancing frequency, and the motion path, and determining a high-risk section of the transmission line, includes: determining a stress concentration area on the transmission line within a target area according to the dancing amplitude and the dancing frequency, wherein the target area includes the occurrence area, and the stress concentration area is used to characterize a high-risk section of the transmission line; determining an amount of overlap between a stress concentration area on the transmission line and the movement path of the target fault source; A value obtained by dividing the overlap number by the length of the power transmission line in the target area is used as the target probability of the target fault source.

9. The method according to claim 8, characterized in that The step of determining the stress concentration area on the transmission line within the target area according to the dancing amplitude and the dancing frequency includes: Calculating the stress values of the conductor of the transmission line at different dancing amplitudes and dancing frequencies using a preset stress calculation formula; The position where the stress value is the largest is regarded as the stress concentration area.

10. A fault identification and early warning system for a power transmission line, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and implement the fault identification and early warning method for a power transmission line according to any one of claims 1 to 9 when executing the instructions.

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