Traveling wave fault early warning and positioning method and system for 35kV wind power plant current collection line

Abstract

The invention discloses a 35kV wind power plant current collection line traveling wave fault early warning and positioning method and system, and relates to the technical field of traveling wave fault detection, and the method comprises the steps: obtaining a fault traveling wave signal in a detection region of a wind power plant current collection line; preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; performing waveform analysis on the target fault traveling wave signal, and determining feature information of the target fault traveling wave signal; determining a fault type of the detection area based on the feature information; obtaining a topological structure of the detection area; determining a fault position in the detection area based on the topological structure and the feature information; and based on the fault type and the fault position, performing fault early warning on the wind power plant current collection line, and generating a fault early warning scheme. According to the invention, the accuracy of wind power plant current collection line fault positioning can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of traveling wave fault detection, and in particular to a method and system for early warning and locating traveling wave faults in a 35kV wind farm collector line. Background Art

[0002] With the increasing demand for clean energy power supply, wind farms, as important clean energy power generation bases, their operating efficiency and stability are of great significance to ensuring power supply.

[0003] Wind farms are usually composed of many wind turbines scattered over a wide area. In order to efficiently transmit electricity, these wind turbines need to be connected to each other through carefully designed collector lines. These collector lines include not only overhead lines, but may also cover underground cables, forming a highly complex power system network. Due to the uneven distribution of wind turbines, in order to meet the power transmission needs between different units, the collector lines have to be frequently branched to adapt to various terrains and unit layouts. Although this design ensures the reliable transmission of electricity, it also increases the difficulty of line management and maintenance. When a part of the collector line fails, it becomes particularly difficult to accurately locate the fault point due to the large number of branches and complex line directions. Traditional troubleshooting methods, such as manual inspections and offline testing, are often time-consuming and inefficient, especially when facing large-scale wind farms, the limitations of this method are more prominent. In addition, since wind farms are usually located in remote areas and in harsh environments, these factors further aggravate the difficulty of troubleshooting.

[0004] Therefore, a solution is urgently needed to solve the above problems. Summary of the invention

[0005] The embodiments of the present application provide a 35kV wind farm collector line traveling wave fault early warning and positioning method and system, which are used to improve the accuracy of wind farm collector line fault positioning.

[0006] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions: In a first aspect, a method for early warning and locating traveling wave faults of a wind farm collector line is provided, the method comprising: Acquire a fault traveling wave signal within a detection area of ​​a wind farm collector line; Preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; Performing waveform analysis on the target fault traveling wave signal to determine characteristic information of the target fault traveling wave signal; Based on the characteristic information, determining the fault type of the detection area; Acquire the topological structure of the detection area; Determine the fault location within the detection area based on the topological structure and characteristic information; Based on the fault type and the fault location, issue a fault warning for the collector line of the wind farm and generate a fault warning plan.

[0007] In another possible implementation manner of the first aspect, the preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal includes: Perform noise reduction processing on the fault traveling wave signal by using a wavelet transform method; Perform filtering processing on the fault traveling wave signal after noise reduction processing by using a Kalman filter method to obtain a target fault traveling wave signal.

[0008] In another possible implementation manner of the first aspect, the characteristic information includes time-domain characteristics, frequency-domain characteristics, and transient characteristics. The waveform analysis of the target fault traveling wave signal to determine the characteristic information of the target fault traveling wave signal includes: Perform a Fourier transform on the target fault traveling wave signal to obtain a spectrogram of the target fault traveling wave signal; Calculate the waveform variance of the target fault traveling wave signal by using a preset variance formula; Obtain the waveform maximum value and waveform minimum value of the target fault traveling wave signal; Extract the spectral distribution and frequency components of the target fault traveling wave signal from the spectrogram; Perform transient change analysis on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal; Determine the frequency-domain characteristics of the target fault traveling wave signal according to the spectral distribution and the frequency components; Determine the time-domain characteristics of the target fault traveling wave signal according to the waveform variance, the waveform maximum value, and the waveform minimum value.

[0009] In another possible implementation manner of the first aspect, the performing transient change analysis on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal includes: Traverse the spectrogram of the target fault traveling wave signal by using a preset window to obtain the spectral characteristics of the target fault traveling wave signal within the window, where the spectral characteristics include spectral amplitude, spectral frequency, and spectral phase; Identify the mutation points of the target fault traveling wave signal through the spectral amplitude, the spectral frequency, and the spectral phase; At the position of the mutation point, use the Hilbert transform method to extract the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal. Determine the transient characteristics of the target fault traveling wave signal by combining the instantaneous amplitude and the instantaneous frequency.

[0010] In another possible implementation manner of the first aspect, determining the fault type of the detection area based on the feature information includes: Obtain the historical data set of the detection area; Train the fault type recognition model with the training set to obtain the trained fault type recognition model, where the training set is obtained based on the historical data set; Input the feature information into the trained fault type recognition model to obtain the fault type of the detection area.

[0011] In another possible implementation manner of the first aspect, determining the fault location in the detection area based on the topological structure and feature information includes: Determine the fault point location of the target fault traveling wave signal through the feature information; Based on the fault point location, determine the traveling wave head transmission path of the target fault traveling wave signal; Based on the topological structure, construct an inherent distance difference matrix; Through the traveling wave head transmission path, use the variational mode decomposition method and the Teager energy operator method to mark the traveling wave head of the target fault traveling wave signal, and determine the initial traveling wave head of the fault in the detection area; Through the topological structure, determine the positions of the two end nodes of all line segments in the detection area; For any one of the line segments, based on the positions of the two end nodes, use the double - end detection method to respectively determine the first time point when the initial traveling wave head of the fault enters the line segment and the second time point when the initial traveling wave head of the fault leaves the line segment; Subtract the first time point from the second time point to determine the time difference of the line segment; Obtain the traveling wave propagation speed of the initial traveling wave head of the fault; Multiply the time difference of each line segment by the traveling wave propagation speed to obtain the fault distance of each line segment; Construct a fault distance difference matrix from the fault distances of all line segments; Subtract each matrix element in the fault distance difference matrix from the corresponding matrix element in the inherent distance difference matrix to obtain a fault branch determination matrix; Determine the fault location in the detection area according to the fault branch determination matrix and the topological structure of the detection area.

[0012] In another possible implementation of the first aspect, constructing the inherent distance difference matrix based on the topological structure includes: Through the topological structure, obtain all the terminal nodes and all the branch nodes in the detection area, where the branch nodes are located at the connection between the branch line of the wind farm collector line and the main line of the wind farm collector line, and the terminal nodes are located at the connection between the branch line of the wind farm collector line and the load device; Take each of the branch nodes as the first node and each of the terminal nodes as the second node; For any distribution line of the wind farm collector line, calculate the distance between the first node and the second node respectively to obtain the first distance; Calculate the distance between the second node and another adjacent first node respectively to obtain the second distance; Add the first distance and the second distance to obtain the distance between any two terminal nodes; Construct a matrix with the distances between any two terminal nodes to obtain the inherent distance difference matrix.

[0013] In another possible implementation of the first aspect, determining the fault location in the detection area according to the fault branch determination matrix and the topological structure of the detection area includes: Traverse each determination element in the fault branch determination matrix and determine whether the determination element exceeds a preset threshold; If the determination element exceeds the preset threshold, determine the node range of the target fault traveling wave signal; Obtain each line number in the topological structure of the detection area; Compare each line number with the node range. If the distribution line formed by the node range is the same line as the line number, obtain the fault location in the detection area.

[0014] In a second aspect, the present application provides a traveling wave fault early warning and location system for a wind farm collector line, including: A memory configured to store instructions; and A processor configured to call the instructions from the memory and be capable of implementing the above-mentioned traveling wave fault early warning and location method for a wind farm collector line when executing the instructions.

[0015] In a third aspect, the present application provides a computer storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for traveling wave fault early warning and location for a wind farm collector line described in the first aspect are implemented.

[0016] Through the above technical solution, by acquiring the fault traveling wave signal of the wind farm collector line and performing preprocessing, noise and interference can be removed, the signal-to-noise ratio of the signal can be improved, so as to more accurately reflect the fault characteristics and improve the accuracy of fault detection. Based on the topological structure of the detection area and the characteristic information of the fault traveling wave signal, the location where the fault occurs can be quickly determined, which helps to reduce the fault troubleshooting time and improve the maintenance efficiency. By analyzing the characteristic information of the fault traveling wave signal, the fault type can be accurately judged, and based on the judgment of the fault type and the fault location, a fault warning scheme can be generated, and the fault location can be determined specifically, improving the accuracy of fault location, which helps the wind farm management personnel to take measures in advance, discover and handle potential safety hazards in time, and enhance the overall operation safety of the wind farm.

[0017] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation section. Description of the Drawings

[0018] Figure 1 It is a schematic flow chart of the traveling wave fault warning and location method for the 35kV wind farm collector line provided by the embodiments of the present application; Figure 2 It is a schematic structural diagram of calculating the distance between any two terminal nodes provided by the embodiments of the present application. Specific Embodiments

[0019] To make the objectives, 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 with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation manners 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0020] It should be noted that if there are directional indications (such as up, down, left, right, front, back,...) involved in the embodiments of the present application, the directional indications are only used to explain the relative position relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0021] 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 should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on what can be achieved by those of ordinary skill in the art. When the combination of technical solutions results in contradictions or cannot be achieved, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0022] Figure 1 Schematically shows a flow diagram of a traveling wave fault early warning and location method for a 35 kV wind farm collector line according to an embodiment of the present application. As Figure 1 shown, the embodiment of the present application provides a traveling wave fault early warning and location method for a 35 kV wind farm collector line, and the method may include the following steps.

[0023] S110. Obtain a fault traveling wave signal within a detection area of a wind farm collector line; S120. Preprocess the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; S130. Perform waveform analysis on the target fault traveling wave signal to determine characteristic information of the target fault traveling wave signal; S140. Determine a fault type of the detection area based on the characteristic information; S150. Obtain a topological structure of the detection area; S160. Determine a fault location within the detection area based on the topological structure and the characteristic information; S170. Perform fault early warning on the wind farm collector line based on the fault type and the fault location, and generate a fault early warning plan.

[0024] First, obtain a fault traveling wave signal within a detection area of a wind farm collector line. The traveling wave signal is a transient signal generated when a fault occurs in a power line. The fault traveling wave signal can be obtained by installing voltage transformers and current transformers at key positions of the wind farm collector line to capture the voltage and current signals on the line.

[0025] After obtaining the fault traveling wave signal, preprocess the fault traveling wave signal to obtain the target fault traveling wave signal after preprocessing. The noise in the signal can be removed by applying a filtering algorithm. In this embodiment, the Kalman filter is applied to remove the noise in the signal. The Kalman filter is an efficient recursive filter suitable for noise removal in dynamic systems and can dynamically adjust the filtering parameters according to the statistical characteristics of the signal. In addition, the wavelet transform method is used to denoise the fault traveling wave signal. The wavelet transform method is a signal processing means for denoising the fault traveling wave signal, which can provide a "time-frequency" window that changes with frequency and is suitable for time-frequency analysis of signals. In the denoising process, the wavelet transform gradually refines the signal at multiple scales through dilation and translation operations, and finally achieves the effect of fine time division at high frequencies and fine frequency division at low frequencies, so as to automatically adapt to the requirements of time-frequency signal analysis and realize the denoising process of the signal.

[0026] Next, perform waveform analysis on the target fault traveling wave signal to determine the characteristic information of the target fault traveling wave signal. Specifically, first, through wavelet transform, the target fault traveling wave signal is decomposed into components at multiple scales and frequencies. This process is similar to using different "sieves" to screen the signal to obtain the signal characteristics within the corresponding frequency range. Subsequently, key features such as the mutation points and amplitude changes of the traveling wave signal are extracted from the decomposed signal to determine the characteristic information of the target fault traveling wave signal.

[0027] Based on the characteristic information, determine the fault type in the detection area. In this embodiment, the characteristic information includes time-domain characteristics, frequency-domain characteristics, and transient characteristics. That is to say, by analyzing the time-domain characteristics, frequency-domain characteristics, and transient characteristics of the target fault traveling wave signal, the fault type in the detection area is determined. Machine learning or deep learning algorithms can be used to train a model to identify the fault type. The characteristic information is input into the fault type recognition model to obtain the fault type in the detection area. The fault type recognition model can be a convolutional neural network. A convolutional neural network is a feedforward neural network that contains convolutional calculations and has a deep structure and is one of the typical network structures in deep learning.

[0028] After determining the fault type in the detection area, obtain the topological structure of the detection area. Professional topology discovery tools can be used to automatically collect and analyze the device and connection information in the network and generate a topological structure diagram. Professional topology discovery tools can support multiple protocols (such as SNMP, LLDP, CDP, etc.) to discover devices and connections and provide a visual interface to display the topological structure. The topology discovery tool can be a network management system (NMS), network topology discovery software, etc.

[0029] Based on the topological structure and characteristic information, determine the fault location within the detection area. Specifically, first, by analyzing the topology diagram, understand the connection methods and paths between devices, and identify key nodes and paths. Key nodes and paths are high-incidence areas of faults. Second, collect real-time data related to the detection area from the monitoring system, and determine the characteristic information based on the real-time data. Utilize the connection relationships and path information in the topology diagram, combine the monitoring data and the signals of fault indicators, and gradually narrow down the fault range. Analyze the collected characteristic information, such as voltage fluctuations, current anomalies, temperature changes, etc., to determine the specific fault location. For example, if the temperature in a certain area rises abnormally and is accompanied by signs of current overload, the fault may be located at a certain device or connection point in this area. Subsequently, combine the location based on the topological structure and the location based on the characteristic information, and determine the most likely fault location by comprehensively analyzing the monitoring data, the signals of fault indicators, and historical data.

[0030] Based on the fault type and the fault location, conduct fault early warning for the wind farm collector line and generate a fault early warning plan. That is to say, according to the analysis of the fault type and location, generate the corresponding fault early warning plan. For example, by obtaining the location where the fault occurs and the fault type is lightning overvoltage, it is necessary to generate a lightning overvoltage early warning plan according to the location where the fault occurs.

[0031] By obtaining the fault traveling wave signal of the wind farm collector line and performing preprocessing, noise and interference can be removed, the signal-to-noise ratio of the signal can be improved, so as to more accurately reflect the fault characteristics and improve the accuracy of fault detection. Based on the topological structure of the detection area and the characteristic information of the fault traveling wave signal, the location where the fault occurs can be quickly determined, which helps to reduce the fault troubleshooting time and improve the maintenance efficiency. By analyzing the characteristic information of the fault traveling wave signal, the fault type can be accurately judged, and based on the judgment of the fault type and the fault location, a fault early warning plan can be generated, which can specifically determine the fault location, improve the accuracy of fault location, help the wind farm management personnel take measures in advance, discover and handle potential safety hazards in time, and enhance the overall operation safety of the wind farm.

[0032] In one implementation manner of this embodiment, preprocess the fault traveling wave signal to obtain the target fault traveling wave signal after preprocessing, including: S210. Use the wavelet transform method to perform noise reduction processing on the fault traveling wave signal; S220. Use the Kalman filter method to perform filtering processing on the fault traveling wave signal after noise reduction processing to obtain the target fault traveling wave signal.

[0033] The wavelet transform method is used to denoise the fault traveling wave signal. The wavelet transform method is a signal processing means for denoising the fault traveling wave signal, which can provide a "time-frequency" window that changes with frequency and is suitable for time-frequency analysis of signals. Specifically, the fault traveling wave signal is decomposed by wavelet transform to obtain its low-frequency and high-frequency components. Threshold processing is performed on the high-frequency components. A threshold is set, and the high-frequency components less than the threshold are set to zero, thereby achieving noise removal. The selection of the threshold can be determined according to the characteristics of the signal and the noise level. Subsequently, the processed high-frequency components and low-frequency components are reconstructed to obtain the denoised fault traveling wave signal.

[0034] Next, the Kalman filter method is used to filter the denoised fault traveling wave signal to obtain the target fault traveling wave signal. The Kalman filter is an efficient recursive filter suitable for noise removal in dynamic systems and can dynamically adjust the filtering parameters according to the statistical characteristics of the signal. That is to say, the Kalman filter can be used to process the denoised fault traveling wave signal. By establishing a suitable state space model and using the Kalman filter for iterative processing, the target fault traveling wave signal can be extracted from the signal containing noise. By using the wavelet transform method to denoise the fault traveling wave signal and then using the Kalman filter method to filter the denoised fault traveling wave signal, the quality of the fault traveling wave signal can be significantly improved, the accuracy of fault detection and location can be enhanced, the stability and reliability of the system can be strengthened, and the subsequent processing flow can be optimized.

[0035] In one implementation manner of this embodiment, the characteristic information includes time-domain characteristics, frequency-domain characteristics, and transient characteristics. The waveform of the target fault traveling wave signal is analyzed to determine the characteristic information of the target fault traveling wave signal, including: S310. Perform Fourier transform on the target fault traveling wave signal to obtain the spectrogram of the target fault traveling wave signal; S320. Calculate the waveform variance of the target fault traveling wave signal using a preset variance formula; S330. Obtain the waveform maximum value and waveform minimum value of the target fault traveling wave signal; S340. Extract the spectral distribution and frequency components of the target fault traveling wave signal from the spectrogram; S350. Perform transient change analysis on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal; S360. Determine the frequency-domain characteristics of the target fault traveling wave signal according to the spectral distribution and frequency components; S370. Determine the time-domain characteristics of the target fault traveling wave signal according to the waveform variance, waveform maximum value, and waveform minimum value.

[0036] First, perform a Fourier transform on the target fault traveling wave signal to obtain the spectrogram of the target fault traveling wave signal. The Fourier transform is a mathematical tool used to convert a signal from the time domain to the frequency domain. The spectrogram can be obtained using MATLAB, which is a mathematical calculation software and can be used for matrix operations and numerical analysis. Specifically, use MATLAB to perform the Fourier transform on the signal and calculate the frequency resolution through MATLAB to plot the spectrogram of the signal.

[0037] Using the preset variance formula, calculate the waveform variance of the target fault traveling wave signal. In this embodiment, the preset variance formula may be set according to the characteristics and requirements of the signal. The preset variance formula can be: where N is the total number of data; is the i-th data point in the dataset; is the mean of the dataset; is the variance of the data; Calculate the waveform variance of the target fault traveling wave signal through the preset variance formula.

[0038] Subsequently, obtain the waveform maximum value and waveform minimum value of the target fault traveling wave signal. Mathematical tools such as Excel and MATLAB can be used to calculate them through the built-in maximum and minimum value functions. For example, in MATLAB, if there is a one-dimensional array named signal representing the fault traveling wave signal, the code can be used to calculate the maximum and minimum values.

[0039] Extract the spectral distribution and frequency components of the target fault traveling wave signal in the spectrogram. After obtaining the spectrogram of the target fault traveling wave signal, by analyzing the spectrogram, identify the main frequency components of the target fault traveling wave signal. Spectral analysis uses Fourier transform technology to determine the intensity of each frequency component in the signal. By performing spectral analysis on the signal, the main frequency characteristics of the signal can be understood. According to the peak positions in the spectrogram, extract the main frequency components of the target fault traveling wave signal. Analyze the energy distribution of different frequency components in the spectrogram to understand the spectral characteristics of the signal, and by comparing the spectral distribution of the target fault traveling wave signal with that of the normal signal, identify the abnormal components or changes in the spectrum.

[0040] Subsequently, transient change analysis is performed on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal. In this embodiment, the transient characteristics include instantaneous amplitude and instantaneous frequency. That is to say, by extracting the instantaneous amplitude and instantaneous frequency, the transient characteristics of the target fault traveling wave signal are determined, and the transient characteristics are compared with known fault modes to identify the fault type. The instantaneous amplitude and instantaneous frequency can be extracted by the Hilbert transform method. The Hilbert transform is a linear transform that can convert a real signal into an analytic signal. The amplitude and frequency of the analytic signal are the instantaneous amplitude and instantaneous frequency of the original signal.

[0041] According to the spectrum distribution and frequency components, the frequency domain characteristics of the target fault traveling wave signal are determined. That is to say, after obtaining the spectrum distribution and frequency components of the target fault traveling wave signal, the frequency domain characteristics of the target fault traveling wave signal can be determined through the spectrum distribution and frequency components. Through the frequency components, the main frequency components and secondary frequency components of the signal can be understood. Through the frequency components, fault characteristics can be identified.

[0042] According to the waveform variance, waveform maximum value, and waveform minimum value, the time domain characteristics of the target fault traveling wave signal are determined. That is to say, through the waveform variance, waveform maximum value, and waveform minimum value of the target fault traveling wave signal, the time domain characteristics of the target fault traveling wave signal are determined. The waveform variance can reflect the fluctuation degree of the signal. Through the waveform maximum value and waveform minimum value, the extreme conditions of the signal can be reflected.

[0043] By performing waveform analysis on the target fault traveling wave signal to determine its characteristic information, the time domain characteristics, frequency domain characteristics, and transient characteristics of the target fault traveling wave signal can be comprehensively extracted, providing key information for subsequent fault identification, diagnosis, or prediction.

[0044] In one implementation manner of this embodiment, transient change analysis is performed on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal, including: S410. Use a preset window to traverse the spectrogram of the target fault traveling wave signal to obtain the spectral characteristics of the target fault traveling wave signal within the window. The spectral characteristics include spectral amplitude, spectral frequency, and spectral phase; S420. Identify the mutation points of the target fault traveling wave signal through the spectral amplitude, spectral frequency, and spectral phase; S430. At the position of the mutation point, use the Hilbert transform method to extract the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal; S440. Combine the instantaneous amplitude and instantaneous frequency to determine the transient characteristics of the target fault traveling wave signal.

[0045] First, traverse the spectrogram of the target fault traveling wave signal using a preset window to obtain the spectral characteristics of the target fault traveling wave signal within the window. In this embodiment, the spectral characteristics include spectral amplitude, spectral frequency, and spectral phase. The preset window can be set according to the signal characteristics. Specifically, apply the preset window to the spectrogram and traverse the entire spectrogram. At each window position, extract the spectral characteristics within the window. Within the window, determine the amplitudes of the various frequency components in the spectrogram, which can be achieved by reading the intensity values at the corresponding frequency points in the spectrogram. Next, determine the frequency range corresponding to the spectral characteristics within the window, which can be achieved by reading the abscissa of the spectrogram. Finally, within the window, calculate the phases of the various frequency components in the spectrogram. The phase information can be extracted through signal processing algorithms (such as the inverse Fourier transform or phase recovery algorithms).

[0046] Secondly, through the spectral amplitude, spectral frequency, and spectral phase, identify the mutation points of the target fault traveling wave signal. A sliding window or a fixed window can be used to traverse the spectrogram to capture the local changes in spectral amplitude and frequency. Detect the mutation points in the spectrogram, such as wave fronts, reflection points, etc. These mutation points are usually directly related to the fault location, type, etc. In the spectrogram, observe the changes in spectral phase over time or frequency. The mutation points may be manifested as sudden changes in spectral phase or significant increases in phase differences. Identify the mutation points of the target fault traveling wave signal through the sudden changes in spectral phase and frequency.

[0047] After determining the positions of the mutation points, use the Hilbert transform method to extract the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal. The Hilbert transform is a linear integral transform that can generate a corresponding analytic signal for a real-valued signal. Specifically, use the Hilbert transform function to process the target fault traveling wave signal to obtain the analytic signal. The amplitude of the analytic signal is the instantaneous amplitude. In MATLAB, the abs function can be used to calculate the modulus of the analytic signal to obtain the instantaneous amplitude. The instantaneous frequency can be obtained by differentiating the instantaneous phase. First, use the angle function to calculate the phase of the analytic signal, and then use the unwrap function to unwrap the phase to eliminate phase jumps. Finally, perform a difference operation on the unwrapped phase and divide by 2π multiplied by the sampling rate to obtain the instantaneous frequency.

[0048] Combine the instantaneous amplitude and instantaneous frequency to determine the transient characteristics of the target fault traveling wave signal. That is to say, by obtaining the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal, the transient characteristics of the target fault traveling wave signal can be determined. Extract key characteristic points, such as peaks, valleys, frequency mutation points, etc., from the instantaneous amplitude and instantaneous frequency curves, which can be used as the basis for fault identification, diagnosis, and location.

[0049] By determining the change characteristics of the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal, the transient characteristics of the target fault traveling wave signal can be effectively determined, providing key information for subsequent fault identification, diagnosis, and location, and improving the accuracy of fault location.

[0050] In one implementation manner of this embodiment, based on the characteristic information, determining the fault type of the detection area includes: S510. Obtain the historical data set of the detection area; S520. Train the fault type recognition model with the training set to obtain the trained fault type recognition model, where the training set is obtained based on the historical data set; S530. Input the characteristic information into the trained fault type recognition model to obtain the fault type of the detection area.

[0051] To obtain the historical data set of the detection area, a preset database can be used to query the historical data set of the detection area in the preset database. In this embodiment, the historical data set is a data set of fault signals.

[0052] Train the fault type recognition model with the training set to obtain the trained fault type recognition model, where the training set is obtained based on the historical data set. Specifically, a part of the data in the historical data set can be selected as the training set, or the historical data set can be used as the training set. This application does not limit this. In this embodiment, the fault type recognition model can be a convolutional neural network. A convolutional neural network is a feedforward neural network that contains convolutional calculations and has a deep structure, and is one of the typical network structures of deep learning. That is, using the training set data to train the convolutional neural network can obtain the trained fault type recognition model.

[0053] Subsequently, input the characteristic information into the trained fault type recognition model to obtain the fault type of the detection area. Specifically, the extracted characteristic information is used as input data and passed to the trained fault type recognition model. After the model receives the input data, internal calculations are performed to predict the fault type. Subsequently, the model outputs the predicted fault type, and based on the result output by the model, the fault type of the detection area is obtained.

[0054] Determining the fault type of the detection area through the characteristic information can improve the accuracy of fault prediction and diagnosis, and enhance the reliability and safety of the system.

[0055] In one implementation manner of this embodiment, based on the topological structure and characteristic information, determining the fault location in the detection area includes: S601. Determine the fault point location of the target fault traveling wave signal through the characteristic information; S602. Determine the traveling wave head transmission path of the target fault traveling wave signal based on the fault point location; S603. Construct an inherent distance difference matrix based on the topological structure; S604. Through the traveling wave head transmission path, use the variational mode decomposition method and the Teager energy operator method to mark the traveling wave head of the target fault traveling wave signal, and determine the initial fault traveling wave head within the detection area; S605. Determine the positions of the two end nodes of all line sections within the detection area through the topological structure; S606. For any line section, based on the positions of the two end nodes, use the double - end detection method to respectively determine the first time point when the initial fault traveling wave head enters the line section and the second time point when the initial fault traveling wave head leaves the line section; S607. Subtract the first time point from the second time point to determine the time difference of the line section; S608. Obtain the traveling wave propagation speed of the initial fault traveling wave head; S609. Multiply the time difference of each line section by the traveling wave propagation speed to obtain the fault distance of each line section; S610. Construct a fault distance difference matrix from the fault distances of all line sections; S611. Subtract each matrix element in the fault distance difference matrix from the corresponding matrix element in the inherent distance difference matrix to obtain a fault branch determination matrix; S612. Determine the fault location within the detection area based on the fault branch determination matrix and the topological structure of the detection area.

[0056] Based on the topological structure and characteristic information, determine the fault location within the detection area. Specifically, through the characteristic information, determine the fault point location of the target fault traveling wave signal. In this embodiment, the characteristic information includes time - domain characteristics, frequency - domain characteristics, and transient characteristics. That is, by analyzing the time - domain characteristics, frequency - domain characteristics, and transient characteristics of the target fault traveling wave signal, determine the fault point where there is a sudden change in the spectral phase or a significant increase in the phase difference of the target fault traveling wave signal, thereby determining the fault point location of the target fault traveling wave signal.

[0057] After determining the fault point location of the target fault traveling wave signal, based on the fault point location, determine the traveling wave head transmission path of the target fault traveling wave signal. Specifically, regard the fault point location as the signal source, and the traveling wave propagates in all directions from the fault point location. Subsequently, according to the topological structure of the transmission line and the traveling wave propagation characteristics, draw the possible propagation paths of the traveling wave head. The propagation paths can include the paths directly propagating from the fault point to each measurement point, as well as the paths propagating to the measurement point after refraction and reflection, and determine the traveling wave head transmission path of the target fault traveling wave signal.

[0058] Based on the topological structure, an inherent distance difference matrix is constructed. The inherent distance difference matrix is a data matrix that contains the distance difference information between each data point in the dataset. Specifically, first, through the topological structure of the wind farm collector line, each node in the collector line and their connection relationships are determined. In this embodiment, the branch nodes are located at the connection of the branch line of the wind farm collector line and the main line of the wind farm collector line, and the terminal nodes are located at the connection of the branch line of the wind farm collector line and the load device. After obtaining each node and their connection relationships, a blank matrix is created, where both rows and columns represent the nodes in the network and are used to store the distance information between the nodes. According to the topological structure and the distance definition between the nodes, each element in the matrix is filled. If two nodes are directly connected, the distance between them is known. If two nodes are not directly connected, the distance between them may be calculated through other nodes or lines. Subsequently, when selecting one or more reference points, key nodes or nodes with special significance in the wind farm collector line (such as fault points, power supply points, etc.) can be used as reference points. In this embodiment, the reference points are the first node and the second node. The first node is each branch node, and the second node is each terminal node. For any distribution line of the wind farm collector line, the distance between the first node and the second node is calculated respectively to obtain the first distance; the distance between the second node and another adjacent first node is calculated respectively to obtain the second distance; next, the first distance and the second distance are added together to obtain the distance between any two terminal nodes; a matrix is constructed with the distance between any two terminal nodes to obtain the inherent distance difference matrix. For example, as Figure 2 shown, it is a schematic structural diagram for calculating the distance between any two terminal nodes provided by this application. As shown in the figure, among them, the first nodes are K1, K2, K3, K4, K5, K6 respectively, and the second nodes are M1, M2, M3, M4, M5, M6, M7, M8 respectively; the first distance is the distance between M1 and K1, and the second distance is the distance between K1 and M2. The distance between M1 and K1 and the distance between K1 and M2 are added together to obtain the distance between any two terminal nodes.

[0059] Subsequently, through the traveling wave front transmission path, using the variational mode decomposition method and the Teager energy operator method, the traveling wave fronts of the target fault traveling wave signal are marked to determine the initial traveling wave fronts of the fault within the detection area. The variational mode decomposition method is an adaptive, data-driven technique for non-stationary signal decomposition. The Teager energy operator is a non-linear operator used in signal processing that can capture transient features and estimate the instantaneous frequency and amplitude of the signal. According to the traveling wave front transmission path, the position of the traveling wave front is determined in the transmission path, and the traveling wave front is calibrated based on the variational mode decomposition method - TEO. Specifically, first, the preprocessed traveling wave signal is decomposed into multiple intrinsic mode functions using the variational mode decomposition method. Subsequently, the Teager energy operator can detect the mutation points in the signal, and the arrival time of the mutation points corresponds to the arrival time of the traveling wave front. By comparing the arrival times of the wave fronts on different paths, the position of the initial traveling wave front of the fault can be further confirmed. In the instantaneous energy spectrum, the energy mutation points are found and marked as traveling wave fronts.

[0060] Through the topological structure, the positions of the two end nodes of all line sections within the detection area are determined. That is to say, in the topological graph, the nodes of all wind farm collector lines are identified. Next, all line sections are marked. For each line section, the two nodes it connects can be determined by observing the starting point and the ending point of the line section. In the topological graph, these points usually have clear markings or connection indications.

[0061] After determining the positions of the two end nodes, for any line section, based on the positions of the two end nodes, using the double-end detection method, the first time point when the initial traveling wave front of the fault enters the line section and the second time point when the initial traveling wave front of the fault leaves the line section are determined respectively. Specifically, the double-end detection method is a fault location technique that uses the detection devices installed at both ends of the line to simultaneously measure the traveling wave signals generated by the fault. By comparing the time difference of the traveling wave signals arriving at both ends, the position of the fault point can be calculated. Specifically, when the positions of the two end nodes of the line section are determined, the time point when the wave front propagates from the fault point to one end of the line section is denoted as t1. Subsequently, the wave front continues to propagate, and the time point when it reaches the other end of the line section is denoted as t2, and t2 is the second time point when the initial traveling wave front of the fault leaves the line section.

[0062] Subtract the second time point from the first time point to determine the time difference of the line section. That is, subtract t2 from t1 to obtain the time difference of the line section. Next, obtain the traveling wave propagation speed of the initial wavefront of the fault. This can be done through the direct measurement method. The direct measurement method involves injecting a known signal (such as a pulse signal) into the actual power system and measuring the time it takes for the signal to propagate along the line, thereby calculating the traveling wave propagation speed. This method usually requires installing sensors at both ends or multiple points of the power system to capture the arrival time of the signal. By calculating the propagation time and distance of the signal between two points, the traveling wave propagation speed can be obtained.

[0063] After determining the traveling wave propagation speed, multiply the time difference of each line section by the traveling wave propagation speed to obtain the fault distance of each line section. That is, between the obtained traveling wave propagation speed and the time difference, use the calculation formula to calculate the fault distance. The calculation formula is as follows: Fault distance = Time difference × Traveling wave propagation speed Next, construct a fault distance difference matrix for all line sections. That is, based on the fault distances of each line section calculated in the previous steps, construct a two-dimensional matrix where the rows and columns represent different line sections, and each element in the matrix represents the difference in fault distances between the corresponding two line sections.

[0064] Subtract each element in the fault distance difference matrix from the corresponding element in the inherent distance difference matrix to obtain the fault branch determination matrix. Specifically, based on the previously obtained inherent distance difference matrix and the fault distance difference matrix, subtract each element in the fault distance difference matrix from the element in the corresponding position in the inherent distance difference matrix. The result forms a fault branch determination matrix, where each element represents the difference between the fault distance difference and the inherent distance difference.

[0065] Finally, based on the fault branch determination matrix and the topological structure of the detection area, determine the fault location within the detection area. That is, analyze the fault branch determination matrix to find the element or combination of elements with the largest difference. These elements usually indicate the branch where the fault occurred. Combine with the topological structure of the detection area, especially the connection method of the lines and the branch points, to further narrow down the fault range. Ultimately, determine the specific location of the fault within the detection area and trigger the corresponding alarm or take other countermeasures.

[0066] By determining the fault location within the detection area based on the topological structure and characteristic information, not only can the accuracy and efficiency of fault location be improved, but also the stability and reliability of the system can be enhanced, the operation and maintenance cost can be reduced, and the development of intelligent operation and maintenance can be supported.

[0067] In one implementation manner of this embodiment, based on the topological structure, an inherent distance difference matrix is constructed, including: S701. Through the topological structure, all terminal nodes and all branch nodes within the detection area are obtained, where the branch nodes are located at the connection of the branch line of the wind farm collector line and the main line of the wind farm collector line, and the terminal nodes are located at the connection of the branch line of the wind farm collector line and the load device; S702. Each branch node is used as the first node, and each terminal node is used as the second node; S703. For any distribution line of the wind farm collector line, the distance between the first node and the second node is calculated respectively to obtain the first distance; S704. The distance between the second node and another adjacent first node is calculated respectively to obtain the second distance; S705. The first distance and the second distance are added together to obtain the distance between any two terminal nodes; S706. The distances between any two terminal nodes are used to construct a matrix to obtain the inherent distance difference matrix.

[0068] Through the topological structure, all terminal nodes and all branch nodes within the detection area are obtained. In this embodiment, the branch nodes are located at the connection of the branch line of the wind farm collector line and the main line of the wind farm collector line, and the terminal nodes are located at the connection of the branch line of the wind farm collector line and the load device. Specifically, first, the network topology diagram of the detection area is obtained as needed to show the connection relationships between all devices. On the topology diagram, the terminal nodes and branch nodes are identified according to the connection conditions of the nodes. Terminal nodes are usually only connected to one node, while branch nodes are connected to multiple nodes. Finally, the identified terminal nodes and branch nodes are listed separately.

[0069] Subsequently, each branch node is used as the first node, and each terminal node is used as the second node. That is to say, after all branch nodes and terminal nodes are identified, they are respectively designated as the first node and the second node.

[0070] For any distribution line of the wind farm collector line, the distance between the first node and the second node is calculated respectively to obtain the first distance. In this embodiment, the first distance refers to the calculated distance from the branch node to the terminal node. That is to say, it is necessary to calculate the distance between the branch node (the first node) and the terminal node (the second node).

[0071] Next, calculate the distance between the second node and another adjacent first node respectively to obtain the second distance. In this embodiment, the second distance refers to, for each terminal node, the distance that needs to be calculated between it and its adjacent branch node. That is to say, it is necessary to calculate the distance between the terminal node (the second node) and another adjacent branch node (the first node).

[0072] For example, as Figure 2 shown, it is a schematic structural diagram for calculating the distance between any two terminal nodes provided by this application. As shown in the figure, among them, the first nodes are K1, K2, K3, K4, K5, K6 respectively, and the second nodes are M1, M2, M3, M4, M5, M6, M7, M8 respectively; calculate the first distance as the distance between M1 and K1, and the second distance as the distance between K1 and M2, and add the distance between M1 and K1 and the distance between K1 and M2 to obtain the distance between any two terminal nodes After obtaining the first distance and the second distance, add the first distance and the second distance to obtain the distance between any two terminal nodes. That is to say, through the obtained first distance and second distance, calculate the total distance between any two terminal nodes. For any two terminal nodes, it is necessary to find the distances of all adjacent nodes (including branch nodes and terminal nodes) between them and add these distances together.

[0073] Construct a matrix for the distance between any two terminal nodes to obtain an inherent distance difference matrix. The inherent distance difference matrix refers to a data matrix that contains the distance difference information between each data point in the dataset. Specifically, first, create a two-dimensional matrix, which means that the rows and columns respectively represent the terminal nodes in the distribution line. Fill the calculated distance between any two terminal nodes into the corresponding positions of the matrix, where the rows and columns respectively represent different line sections, and each element in the matrix represents the difference in the fault distances of the corresponding two line sections.

[0074] By constructing the inherent distance difference matrix based on the topological structure, the topological structure of the entire collector line becomes clearer, potential problems in the line can be discovered in a timely manner, the operation and maintenance strategy can be optimized, and the stability and reliability of the line can be improved.

[0075] In one implementation manner of this embodiment, according to the fault branch determination matrix and the topological structure of the detection area, determine the fault location in the detection area, including: S801. Traverse each determination element in the fault branch determination matrix and determine whether the determination element exceeds a preset threshold; S802. If the determination element exceeds the preset threshold, determine the node range of the target fault traveling wave signal; S803. Obtain each line number in the topological structure of the detection area; S804. Compare each line number with the node range. If the power distribution line formed by the node range is the same line as the line number, the fault location within the detection area is obtained.

[0076] According to the fault branch determination matrix and the topological structure of the detection area, determine the fault location within the detection area. Specifically, first, traverse each determination element in the fault branch determination matrix to determine whether the determination element exceeds a preset threshold. In this embodiment, the preset threshold can be determined according to the actual situation. That is to say, traverse the matrix, compare each determination element, and determine whether it exceeds the preset threshold.

[0077] If the determination element exceeds the preset threshold, determine the node range of the target fault traveling wave signal. That is to say, when identifying the determination elements that exceed the threshold, it is necessary to determine the node ranges associated with these elements, and these node ranges may contain the location where the fault occurs. According to the structure of the determination matrix and the positions of the elements that exceed the threshold, the node range related to the target fault traveling wave signal can be inferred.

[0078] Obtain each line number in the topological structure of the detection area. The line number is a unique identifier that enables each line to be clearly identified and located in the topological structure. The line number information of each line can be obtained by referring to the system documentation, topological diagram, or using automated tools. Through each line number, the fault location can be accurately found.

[0079] Next, compare each line number with the node range. If the power distribution line formed by the node range is the same line as the line number, the fault location within the detection area is obtained. That is to say, match the previously determined node range with the actual line number to accurately locate the fault location. By comparing the power distribution line formed by the node range with the known line number, the specific line and possible location where the fault occurs can be determined, thereby obtaining the fault location within the detection area.

[0080] By traversing the fault branch determination matrix and comparing the node range with the line number, the fault location can be quickly determined, reducing the fault troubleshooting time. Quick and accurate fault location helps to repair the fault in a timely manner, reduce the system downtime, and improve the reliability and stability of the wind farm collector line.

[0081] This application provides a traveling wave fault early warning and location system for a wind farm collector line, including: A memory configured to store instructions; and A processor configured to call instructions from the memory and capable of implementing the above-mentioned traveling wave fault early warning and location method for the wind farm collector line when executing the instructions.

[0082] The present application also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps for the traveling wave fault warning and location method of the wind farm collector line are implemented.

[0083] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0085] These computer program instructions can 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 generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0087] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0088] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0089] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory 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, such as modulated data signals and carrier waves.

[0090] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0091] The above are only 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 modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

A 1.35kV wind farm collector line traveling wave fault early warning and positioning method is characterized in that: include: Acquire a fault traveling wave signal within a detection area of ​​a wind farm collector line; Preprocessing the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal; Performing waveform analysis on the target fault traveling wave signal to determine characteristic information of the target fault traveling wave signal; Based on the characteristic information, determining the fault type of the detection area; Acquire the topological structure of the detection area; Based on the topological structure and the characteristic information, determining a fault location within the detection area; Based on the fault type and the fault location, a fault warning is performed on the wind farm collector line, and a fault warning plan is generated.

2. The method according to claim 1, characterized in that The preprocessing of the fault traveling wave signal to obtain a preprocessed target fault traveling wave signal includes: Using wavelet transform method to perform noise reduction processing on the fault traveling wave signal; The fault traveling wave signal after noise reduction is filtered using a Kalman filtering method to obtain a target fault traveling wave signal.

3. The method according to claim 1, characterized in that The characteristic information includes time domain characteristics, frequency domain characteristics and transient characteristics. The waveform analysis of the target fault traveling wave signal to determine the characteristic information of the target fault traveling wave signal includes: Performing Fourier transform on the target fault traveling wave signal to obtain a frequency spectrum of the target fault traveling wave signal; Calculating the waveform variance of the target fault traveling wave signal using a preset variance formula; Obtaining a waveform maximum value and a waveform minimum value of the target fault traveling wave signal; Extracting the frequency spectrum distribution and frequency components of the target fault traveling wave signal in the frequency spectrum diagram; Performing transient change analysis on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal; Determining frequency domain characteristics of the target fault traveling wave signal according to the frequency spectrum distribution and the frequency components; The time domain characteristics of the target fault traveling wave signal are determined according to the waveform variance, the waveform maximum value and the waveform minimum value.

4. The method according to claim 3, characterized in that The performing transient change analysis on the target fault traveling wave signal to determine the transient characteristics of the target fault traveling wave signal includes: Using a preset window to traverse the frequency spectrum of the target fault traveling wave signal, obtain the frequency spectrum characteristics of the target fault traveling wave signal in the window, wherein the frequency spectrum characteristics include frequency spectrum amplitude, frequency spectrum frequency and frequency spectrum phase; Identify the mutation point of the target fault traveling wave signal through the spectrum amplitude, the spectrum frequency and the spectrum phase; At the location of the mutation point, using the Hilbert transform method, extracting the instantaneous amplitude and instantaneous frequency of the target fault traveling wave signal; The transient characteristics of the target fault traveling wave signal are determined in combination with the instantaneous amplitude and the instantaneous frequency.

5. The method according to claim 1, characterized in that The determining the fault type of the detection area based on the characteristic information includes: Acquire a historical data set of the detection area; Training the fault type identification model through a training set to obtain the trained fault type identification model, wherein the training set is obtained based on the historical data set; The characteristic information is input into the trained fault type recognition model to obtain the fault type of the detection area.

6. The method according to claim 1, characterized in that The determining the fault location within the detection area based on the topological structure and the characteristic information includes: Determining the fault point position of the target fault traveling wave signal through the characteristic information; Based on the fault point location, determining a traveling wave head transmission path of the target fault traveling wave signal; Based on the topological structure, construct an intrinsic distance difference matrix; By using the traveling wave head transmission path, the traveling wave head of the target fault traveling wave signal is marked by using a variational mode decomposition method and a Teager energy operator method to determine the initial traveling wave head of the fault in the detection area; Determine the locations of the two end nodes of all line sections within the detection area through the topological structure; For any of the line sections, based on the positions of the two end nodes, using a double-end detection method, respectively determine a first time point when the head of the initial fault traveling wave enters the line section and a second time point when the head of the initial fault traveling wave leaves the line section; Subtracting the second time point from the first time point to determine a time difference between the route sections; Obtaining the traveling wave propagation speed of the initial traveling wave head of the fault; Multiplying the time difference of each line section by the traveling wave propagation speed to obtain the fault distance of each line section; Constructing a fault distance difference matrix from the fault distances of all the line sections; Subtracting each matrix element in the fault distance difference matrix from each matrix element in the inherent distance difference matrix to obtain a fault branch decision matrix; The fault location within the detection area is determined according to the fault branch decision matrix and the topological structure of the detection area.

7. The method according to claim 6, characterized in that The step of constructing an intrinsic distance difference matrix based on the topological structure includes: Obtain all terminal nodes and all branch nodes in the detection area through the topological structure, wherein the branch node is located at the connection point between the branch line of the wind farm collector line and the main line of the wind farm collector line, and the terminal node is located at the connection point between the branch line of the wind farm collector line and the load device; Taking each of the branch nodes as a first node and taking each of the terminal nodes as a second node; For any distribution line of the wind farm collector line, respectively calculate the distance between the first node and the second node to obtain a first distance; respectively calculating the distance between the second node and another adjacent first node to obtain a second distance; Adding the first distance and the second distance to obtain the distance between any two of the terminal nodes; The distances between any two of the terminal nodes are constructed into a matrix to obtain an inherent distance difference matrix.

8. The method according to claim 1, characterized in that: The determining the fault location within the detection area according to the fault branch determination matrix and the topological structure of the detection area includes: Traversing each decision element in the fault branch decision matrix to determine whether the decision element exceeds a preset threshold; If the determination element exceeds a preset threshold, determining a node range of the target fault traveling wave signal; Obtaining each line number in the topological structure of the detection area; Each of the line numbers is compared with the node range. If the distribution line formed by the node range has the same line number as the line, the fault location in the detection area is obtained.

9. A 35kV wind farm collector line traveling wave fault early warning and positioning system, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instruction from the memory and to implement the traveling wave fault early warning and positioning method for a 35kV wind farm collector line according to any one of claims 1 to 8 when executing the instruction.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for early warning and locating a traveling wave fault of a 35kV wind farm collector line according to any one of claims 1 to 8 are implemented.

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