Fault detection method and device for overhead line of wind power plant and storage medium

Abstract

The invention discloses a fault detection method and device for an overhead line of a wind power plant and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: S1, intelligent data collection; s2, fault signal feature extraction and compression representation; s3, carrying out simulation modeling on a fault propagation path; s4, fault mode identification based on artificial intelligence; s5, a self-adaptive algorithm of fault positioning is carried out; s6, real-time detection of fault arc characteristics; s7, constructing a space-time dynamic fault early warning model; s8, constructing a multi-dimensional fault self-healing control strategy; and S9, fault cause analysis based on the knowledge graph. High-frequency signal features are quickly captured in the early stage of fault occurrence, and accurate positioning, prediction and real-time response to the fault are realized through multi-dimensional analysis and control strategies; meanwhile, the fuzzy logic and the expert system are combined, so that the system can dynamically adjust strategies, optimize a line operation mode, adjust load distribution and start a standby power supply when facing various fault types.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a fault detection method, a device and a storage medium for overhead lines in a wind farm. Background Art

[0002] As the scale of wind farms continues to expand, overhead lines have become an important infrastructure connecting wind farms and power networks. However, during operation, overhead lines in wind farms are prone to failure due to harsh environmental conditions (such as strong winds, lightning, rain and snow, etc.) and complex factors within the power system (such as voltage fluctuations, short circuits, etc.). Therefore, how to detect and locate these faults in a timely and accurate manner has become a key challenge to ensure the stable operation of wind farms.

[0003] Traditional fault detection technologies mostly rely on simple voltage and current monitoring and relay protection devices. These methods can usually only respond passively through current mutations or line impedance changes after a fault occurs, and it is difficult to quickly and accurately locate the fault. Especially when faced with complex multi-point faults and high-noise environments, traditional technologies appear to be slow to respond and inefficient. In addition, existing detection technologies lack effective intelligent support in fault type identification, fault propagation analysis, and fault self-healing control. The following are the shortcomings of traditional fault detection technologies in various key processes: Fault signal feature extraction: Existing technologies mostly use a single signal processing method, which is difficult to deal with the complexity and diversity of fault signals in wind farm overhead lines. Multi-scale signal analysis methods are rare in traditional applications, and even if they are used, their feature extraction effect is not ideal in a high noise background.

[0004] Fault propagation path simulation: Currently, fault propagation simulation is mostly based on linear models or simplified electromagnetic field calculations, ignoring the impact of environmental factors such as wind speed and temperature on the fault propagation path, resulting in low prediction accuracy. In addition, most models have poor adaptability to dynamic environments and it is difficult to make accurate fault path analysis under changing natural conditions.

[0005] Fault arc detection: Due to the limited frequency range, traditional detection methods mostly use low-frequency or medium-frequency signal processing technology, which makes it difficult to capture the ultra-high frequency (UHF) signal generated by arc discharge. This makes it difficult to identify arc signals in the early stages, thus delaying emergency response.

[0006] Multi-dimensional self-healing control strategy: In most existing technologies, the control strategy after a fault occurs mostly adopts a preset single response mode, which lacks flexibility and is difficult to dynamically adjust according to the severity of the fault and the system status. At the same time, the existing system lacks coordinated optimization of multi-dimensional control (such as load adjustment and backup power supply startup), resulting in slow grid self-healing speed and limited effect. Summary of the invention

[0007] In view of the deficiencies of the prior art, the present invention provides a method, device and storage medium for detecting faults of overhead lines in a wind farm, so as to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a fault detection method for an overhead line of a wind farm, comprising the following steps: S1, intelligent data collection; Conduct all-round monitoring and data collection of wind farm overhead lines based on sensors, and integrate multi-source data collected; S2, fault signal feature extraction and compression representation; Using wavelet transform and Fourier transform signal processing technology, fault feature signals are extracted from multi-source data, and multi-scale analysis is performed on the collected time series data to separate features in different frequency bands. High-dimensional features are compressed into low-dimensional space through principal component analysis (PCA) or sparse coding methods to retain the most important fault information. S3, simulation modeling of fault propagation path; The fault propagation model of the wind farm overhead line is constructed through electromagnetic field simulation technology to simulate the propagation path of the fault signal in the power line. Based on the actual line parameters such as resistance, inductance, capacitance and environmental conditions such as wind speed and temperature, the finite element analysis method is used to calculate the transmission characteristics of the fault signal in different line sections. S4, Fault pattern recognition based on artificial intelligence; Use deep learning models to automatically identify fault modes, and use large-scale training data sets for supervised learning to automatically identify common fault types, such as short circuits, broken wires, and insulation damage; S5, adaptive algorithm for fault location; The impedance method combined with the particle swarm optimization algorithm (PSO) is used to accurately locate the fault point. The impedance method is used to calculate the preliminary fault location, and the PSO algorithm is used to perform optimization search to find the coordinates of the fault point with the smallest error; S6. Real-time detection of fault arc characteristics; During the detection process, ultra-high frequency (UHF) signals are used to detect the characteristics of the fault arc in real time, and the key features of the arc signal are further extracted and amplified through intelligent filters and signal enhancement algorithms; S7, constructing a spatiotemporal dynamic fault warning model; Based on the results of pattern recognition, a spatiotemporal dynamic fault warning model is constructed. Geographic Information System (GIS) technology is used to map fault information to the physical geographical location of the wind farm in real time. Through spatiotemporal data modeling and analysis, combined with historical data and current real-time monitoring data, the possible development trend and impact range of the fault are predicted; S8. Build a multi-dimensional fault self-healing control strategy; After the fault is detected and initially located, the multi-dimensional fault self-healing control strategy is automatically activated, including switching line operation mode, adjusting load distribution, and starting backup power supply operations. Fuzzy logic control and expert system technology are used to make the best decision in an instant to minimize the impact of the fault on the entire power grid. S9. Fault cause analysis based on knowledge graph; By establishing the relationship between the data nodes of wind farm equipment, lines, and environmental factors, and using graph algorithms to mine the root causes that may lead to failures, the knowledge graph helps identify the complex causal relationships hidden in the data, allowing the system to make a more accurate root cause analysis of the failure.

[0009] Further optimizing the technical solution, in step S1, the sensor includes current, voltage, temperature, vibration, sound wave and other sensor types, and multiple physical quantities are collected simultaneously based on multi-source sensors to obtain all data of the wind farm overhead lines; Through high-precision time synchronization technology, all data are integrated into a unified time base, and data fusion algorithms such as Kalman filtering and multi-scale clustering are used to perform preliminary processing on different sensor data to enhance the accuracy and reliability of fault signals.

[0010] To further optimize the technical solution, in step S2, when performing multi-scale analysis on the collected time series data, a multi-scale signal decomposition model is constructed, and the fault signal is deeply analyzed and decomposed by combining the nonlinear characteristics and spectral information of the time series signal. The multi-scale signal decomposition model includes a signal decomposition formula, a kernel function, a multi-scale spectral decomposition formula, and an adaptive scale optimization mechanism.

[0011] To further optimize the technical solution, in the multi-scale signal decomposition model: Signal decomposition formula, assuming that the original signal It is a nonlinear time series signal containing multiple frequency components. In order to decompose the signal, a nonlinear kernel function is introduced. The formula is as follows: ; in, Indicated in scale The signal components after decomposition; It is a scale-dependent kernel function, which is used to perform convolution operations on signals at a specific scale; Represents a multi-scale parameter, which is used to control the width of the kernel function. As it increases, the kernel function will capture the low-frequency characteristics of the signal; Kernel function, select the nonlinear Gauss-Lorentz composite kernel function to enhance the nonlinear feature extraction capability of the signal. The kernel function is as follows: ; in, Control the decay speed of the Gaussian part to capture the high-frequency part of the signal; Control the peak shape of the Lorentz part to extract low-frequency features; By combining the advantages of Gaussian and Lorentz distributions, the high-frequency and low-frequency characteristics of the signal can be effectively captured at different scales, and the nonlinear changes of the fault signal can be better adapted.

[0012] To further optimize the technical solution, in the multi-scale signal decomposition model: Multi-scale spectral decomposition formula, for the original signal Perform multi-scale transformation to obtain a series of decomposition signals of different scales , the signal is analyzed at different scales, each scale The signal is represented as: ; In this process, as the scale As increases, the kernel function gradually becomes wider, so that the low-frequency components are gradually separated; Adaptive scale optimization mechanism, based on which the scale selection problem under different fault scenarios is solved and the multi-scale energy ratio of the signal is defined for: ; By maximizing , the system can automatically select the optimal scale To extract fault features, the optimal scale The following conditions are met: ; At the selected scale The signal energy is most concentrated and the fault characteristics are most obvious.

[0013] To further optimize the technical solution, the multi-scale signal decomposition model includes the following specific processes when used: Initial signal analysis: First, the original fault signal collected Conduct preliminary analysis to determine the overall frequency distribution of the signal, and select the appropriate multi-scale parameter range by observing the signal characteristics for subsequent nonlinear multi-scale spectral decomposition; Step-by-step execution of multi-scale decomposition: Use the model to decompose the signal step by step, by The signal components Through analysis, high-frequency features reflecting transient disturbances of the line and low-frequency features representing the impact of fault persistence are extracted respectively; Adaptively select the optimal scale: During the decomposition process, the multi-scale energy ratio at each scale is calculated in real time , and automatically adjust the scale according to the formula to make the decomposition at the optimal scale This ensures that no matter how the fault type changes, the model can extract fault features at an appropriate scale; Compressed representation and feature retention: Once the signal is decomposed at the optimal scale, the decomposed data is compressed and represented through principal component analysis (PCA). PCA reduces the high-dimensional multi-scale signal data to a low dimension while retaining its main fault feature information, which is used to reduce the data dimension and improve the computational efficiency of subsequent fault mode recognition and diagnosis.

[0014] To further optimize the technical solution, in step S3, line parameters, environmental factors and nonlinear losses are introduced into the fault propagation model; Line parameters, considering the distributed parameters of overhead lines, such as resistance ,inductance ,capacitance and admittance Produces different responses at different frequencies; Environmental factors, such as wind speed ,temperature ,humidity Dynamic effects on signal propagation; Nonlinear loss: the amplitude and frequency characteristics of the fault signal are affected by both the electromagnetic characteristics of the line and the environmental conditions; The fault propagation model is as follows: ; in, is in position and time Voltage distribution at is the propagation speed, which is the result of the interaction of medium parameters, electromagnetic constants and environmental factors; is the nonlinear loss term introduced, which refers to the attenuation of the signal under different environmental conditions; function Expressing the loss and voltage , Current And environmental parameters , such as the complex relationship between wind speed and temperature.

[0015] To further optimize the technical solution, in step S8, the multi-dimensional fault self-healing control strategy includes the following specific processes when being constructed: 1. Construction of fuzzy logic controller; Based on fuzzy logic theory, it processes uncertainty and fuzzy information in the input signal, including the following control dimensions: Line operation mode switching control: after a fault is detected, the line operation mode is switched, such as switching from parallel operation to series operation, to ensure system stability; Load distribution adjustment control, adjust the load distribution on each line to avoid overload problems in the fault section to balance the overall system load; Backup power supply start-up control: Automatically start the backup power supply when necessary to maintain the continuity of power supply and reduce the impact of power outage areas; Second, the fuzzy reasoning process is carried out using the Mamdani fuzzy reasoning model; 3. Integration and optimization of expert systems, using pre-set knowledge bases and reasoning engines to provide optimized self-healing strategies in complex fault scenarios; 4. Multi-dimensional collaborative control strategy; Multi-dimensional fault self-healing operations will be performed simultaneously to achieve all-round grid restoration; 5. Real-time dynamic adjustment and feedback mechanism; State monitoring and feedback: monitor system state changes in real time, and feed back monitoring data to the fuzzy controller and expert system to update reasoning parameters; Adaptive control optimization: Automatically optimize fuzzy rules and expert system strategies based on feedback data to improve the accuracy and response speed of self-healing control.

[0016] A fault detection device for an overhead line of a wind farm, the fault detection device comprising a memory, a processor and a fault detection program stored in the memory and executable on the processor, wherein the fault detection program implements the steps of the above-mentioned method for detecting a fault of an overhead line of a wind farm when executed by the processor.

[0017] A computer-readable storage medium stores a fault detection program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting faults in overhead lines of a wind farm.

[0018] Compared with the prior art, the present invention provides a method, device and storage medium for detecting faults of overhead lines in a wind farm, which have the following beneficial effects: The wind farm overhead line fault detection method, device and storage medium can quickly capture high-frequency signal characteristics in the early stage of a fault, and achieve accurate positioning, prediction and real-time response to the fault through multi-dimensional analysis and control strategies; At the same time, the combination of fuzzy logic and expert systems enables the system to dynamically adjust strategies when facing various types of faults, optimize line operation modes, adjust load distribution, and start backup power supplies. This not only significantly improves fault detection and response speed, but also improves the intelligence of fault self-healing and the reliability of power grid operation, and can effectively reduce the impact of faults on the stability of the entire power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a fault detection method for overhead lines in a wind farm proposed by the present invention; Figure 2 A schematic diagram of the composition and flow of a multi-scale signal decomposition model in a fault detection method for overhead lines in a wind farm proposed by the present invention; Figure 3 A schematic diagram of a formula for the influence of environmental factors on propagation speed in a fault propagation model in a fault detection method for overhead lines in a wind farm proposed by the present invention; Figure 4 A schematic diagram of a nonlinear loss model in a fault propagation model in a fault detection method for overhead lines in a wind farm proposed by the present invention; Figure 5 A schematic flow chart of a fault propagation model of a fault detection method for overhead lines in a wind farm proposed by the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1

[0022] See also Figure 1 , a fault detection method for an overhead line of a wind farm comprises the following steps: S1. Intelligent data collection Based on sensors, the wind farm overhead lines are fully monitored and collected, and the collected data are integrated with multi-source data.

[0023] In this embodiment, in step S1, the sensors include current, voltage, temperature, vibration, and sound wave sensors, and multiple physical quantities are collected simultaneously based on multi-source sensors to obtain all data of the wind farm overhead lines; Through high-precision time synchronization technology, all data are integrated into a unified time base, and data fusion algorithms such as Kalman filtering and multi-scale clustering are used to perform preliminary processing on different sensor data to enhance the accuracy and reliability of fault signals.

[0024] S2. Fault signal feature extraction and compression representation By using the signal processing technology of wavelet transform and Fourier transform, fault characteristic signals are extracted from multi-source data, and multi-scale analysis is performed on the collected time series data to separate the features in different frequency bands. The high-dimensional features are compressed into low-dimensional space through principal component analysis (PCA) or sparse coding methods to retain the most important fault information.

[0025] like Figure 2 As shown, in this embodiment, in the step S2, when performing multi-scale analysis on the collected time series data, a multi-scale signal decomposition model is constructed, and the fault signal is deeply analyzed and decomposed by combining the nonlinear characteristics and spectral information of the time series signal. The multi-scale signal decomposition model includes a signal decomposition formula, a kernel function, a multi-scale spectral decomposition formula and an adaptive scale optimization mechanism.

[0026] Furthermore, in the multi-scale signal decomposition model: Signal decomposition formula, assuming that the original signal It is a nonlinear time series signal containing multiple frequency components. In order to decompose the signal, a nonlinear kernel function is introduced. The formula is as follows: ; in, Indicated in scale The signal components after decomposition; It is a scale-dependent kernel function, which is used to perform convolution operations on signals at a specific scale; Represents a multi-scale parameter, which is used to control the width of the kernel function. As it increases, the kernel function will capture the low-frequency characteristics of the signal.

[0027] Kernel function, select the nonlinear Gauss-Lorentz composite kernel function to enhance the nonlinear feature extraction capability of the signal. The kernel function is as follows: ; in, Control the decay speed of the Gaussian part to capture the high-frequency part of the signal; Control the peak shape of the Lorentz part to extract low-frequency features; By combining the advantages of Gaussian and Lorentz distributions, the high-frequency and low-frequency characteristics of the signal can be effectively captured at different scales, and the nonlinear changes of the fault signal can be better adapted.

[0028] Multi-scale spectral decomposition formula, for the original signal Perform multi-scale transformation to obtain a series of decomposition signals of different scales , the signal is analyzed at different scales, each scale The signal is represented as: ; In this process, as the scale As increases, the kernel function gradually becomes wider, so that the low-frequency components are gradually separated.

[0029] Adaptive scale optimization mechanism, based on which the scale selection problem under different fault scenarios is solved and the multi-scale energy ratio of the signal is defined for: ; By maximizing , the system can automatically select the optimal scale To extract fault features, the optimal scale The following conditions are met: ; At the selected scale The signal energy is most concentrated and the fault characteristics are most obvious.

[0030] When the multi-scale signal decomposition model is used, it includes the following specific processes: Initial signal analysis: First, the original fault signal collected Conduct preliminary analysis to determine the overall frequency distribution of the signal, and select the appropriate multi-scale parameter range by observing the signal characteristics for subsequent nonlinear multi-scale spectral decomposition; Step-by-step execution of multi-scale decomposition: Use the model to decompose the signal step by step, by The signal components Through analysis, high-frequency features reflecting transient disturbances of the line and low-frequency features representing the impact of fault persistence are extracted respectively; Adaptively select the optimal scale: During the decomposition process, the multi-scale energy ratio at each scale is calculated in real time , and automatically adjust the scale according to the formula to make the decomposition at the optimal scale This ensures that no matter how the fault type changes, the model can extract fault features at an appropriate scale; Compressed representation and feature retention: Once the signal is decomposed at the optimal scale, the decomposed data is compressed and represented through principal component analysis (PCA). PCA reduces the high-dimensional multi-scale signal data to a low dimension while retaining its main fault feature information, which is used to reduce the data dimension and improve the computational efficiency of subsequent fault mode recognition and diagnosis.

[0031] By introducing adaptive kernel functions and energy ratio optimization mechanisms, the feature extraction capability of wind farm overhead line fault signals is effectively improved. The model can not only accurately decompose complex fault signals, but also automatically adjust the analysis scale according to different fault scenarios, so that the fault characteristics can be fully reflected in the multi-scale space.

[0032] S3. Simulation modeling of fault propagation paths The fault propagation model of the wind farm overhead line is constructed through electromagnetic field simulation technology to simulate the propagation path of the fault signal in the power line. Based on the actual line parameters such as resistance, inductance, capacitance and environmental conditions such as wind speed and temperature, the finite element analysis method is used to calculate the transmission characteristics of the fault signal in different line sections.

[0033] In this embodiment, in step S3, line parameters, environmental factors and nonlinear losses are introduced into the fault propagation model; Line parameters, considering the distributed parameters of overhead lines, such as resistance ,inductance ,capacitance and admittance Produces different responses at different frequencies; Environmental factors, such as wind speed ,temperature ,humidity Dynamic effects on signal propagation; Nonlinear loss: the amplitude and frequency characteristics of the fault signal are affected by both the electromagnetic characteristics of the line and the environmental conditions; The fault propagation model is as follows: ; in, is in position and time Voltage distribution at is the propagation speed, which is the result of the interaction of medium parameters, electromagnetic constants and environmental factors; is the nonlinear loss term introduced, which refers to the attenuation of the signal under different environmental conditions; function Expressing the loss and voltage , Current And environmental parameters , such as the complex relationship between wind speed and temperature.

[0034] Propagation speed Significantly affected by external environmental conditions, defined as wind speed ,temperature and humidity The relationship is as Figure 3 As shown in Figure 2, the formula describes how the signal propagation speed is dynamically adjusted as environmental conditions change. For example, higher wind speeds It will accelerate the cooling effect, thus affecting the conductivity of the medium; while the temperature An increase in may increase conductivity and slow down signal propagation.

[0035] In order to more accurately describe the loss of fault signals during propagation, a nonlinear loss model is constructed as Figure 4 As shown in Figure 2, the model shows that the loss intensity of the fault signal depends not only on the amplitude and frequency characteristics of the signal itself, but is also modulated by external environmental factors. For example, higher humidity will reduce the ionization rate and reduce losses, while increased temperature may accelerate the increase in line resistance and increase losses.

[0036] like Figure 5 As shown, the model, when used: Initialize model parameters: In the process of using the model, the line parameters (resistance ,inductance ,capacitance and admittance ) and environmental factors (such as wind speed ,temperature ,humidity These parameters will be adjusted dynamically in subsequent simulations.

[0037] Fault signal propagation simulation: By solving the fault signal propagation equation, the dynamic propagation process of the fault signal in the overhead line is simulated. The equation is discretized using finite element analysis (FEA) technology to obtain the voltage at the discrete point. and current distribution , thereby simulating the propagation characteristics and speed of fault signals in different line sections.

[0038] Impact of environmental conditions on propagation paths: During the simulation, the effects of environmental factors (such as wind speed, temperature, and humidity) on the propagation speed are updated in real time. and nonlinear loss Through this dynamic adjustment, the propagation path of the fault signal in different environments and its changes can be simulated, so as to more accurately predict the location of the fault point.

[0039] Accurate positioning of fault location: The voltage and current distribution characteristics in the simulation results can be further combined with the particle swarm optimization algorithm (PSO) to optimize the location of the fault point. By adjusting the model parameters, the error between the simulation results and the actual fault signal measurement results is minimized, and the exact location of the fault point is finally determined.

[0040] S4. Fault pattern recognition based on artificial intelligence Use deep learning models to automatically identify fault modes, and use large-scale training data sets for supervised learning to automatically identify common fault types such as short circuits, broken wires, and insulation damage.

[0041] In this embodiment, the prediction results of multiple neural network models are integrated through an integrated learning method, such as the Adaboost algorithm, to improve the accuracy of fault classification. This can not only improve the robustness of detection, but also effectively reduce the misjudgment rate caused by environmental interference.

[0042] S5. Adaptive algorithm for fault location The impedance method combined with the particle swarm optimization algorithm (PSO) is used to accurately locate the fault point. The preliminary fault location is calculated by the impedance method, and the PSO algorithm is used for optimization search to find the coordinates of the fault point with the smallest error.

[0043] In this embodiment, the advantage of the particle swarm algorithm is that it can quickly converge to the global optimal solution and maintain high accuracy even under non-ideal conditions (such as noise interference, line nonlinearity, etc.). Combining the classical algorithm with the optimization technology significantly improves the speed and accuracy of fault location.

[0044] S6. Real-time detection of fault arc characteristics During the detection process, ultra-high frequency (UHF) signals are used to detect the characteristics of the fault arc in real time, and the key features of the arc signal are further extracted and amplified through intelligent filters and signal enhancement algorithms.

[0045] In this embodiment, an adaptive intelligent filter is designed to dynamically adjust the signal enhancement strategy: Adaptive filter structure: The filter is designed using an adaptive algorithm (such as the LMS algorithm or the RLS algorithm), which can dynamically adjust the filter parameters according to the characteristics of the input signal to suppress noise of different frequencies.

[0046] Nonlinear signal enhancement: Through nonlinear processing techniques (such as amplitude modulation or envelope detection), the key features of the arc signal are further amplified to make the signal more prominent in the background noise.

[0047] S7. Constructing a spatiotemporal dynamic fault warning model Based on the results of pattern recognition, a spatiotemporal dynamic fault warning model is constructed. Geographic Information System (GIS) technology is used to map fault information to the physical geographical location of the wind farm in real time. Through spatiotemporal data modeling and analysis, combined with historical data and current real-time monitoring data, the possible development trend and impact range of the fault are predicted.

[0048] S8. Build a multi-dimensional fault self-healing control strategy After the fault is detected and initially located, the multi-dimensional fault self-healing control strategy is automatically activated, including switching line operation mode, adjusting load distribution, and starting backup power supply. Fuzzy logic control and expert system technology are used to make the best decision in an instant to minimize the impact of the fault on the entire power grid.

[0049] In this embodiment, in step S8, the multi-dimensional fault self-healing control strategy includes the following specific processes when being constructed: 1. Construction of fuzzy logic controller; Based on fuzzy logic theory, it processes uncertainty and fuzzy information in the input signal, including the following control dimensions: Line operation mode switching control: after a fault is detected, the line operation mode is switched, such as switching from parallel operation to series operation, to ensure system stability; Load distribution adjustment control, adjust the load distribution on each line to avoid overload problems in the fault section to balance the overall system load; Backup power supply start-up control: Automatically start the backup power supply when necessary to maintain the continuity of power supply and reduce the impact of power outage areas.

[0050] Construct a fuzzy rule table based on input parameters such as fault severity, line voltage fluctuation, and load conditions. An example is as follows: Input variables: fault severity (minor, moderate, severe), voltage fluctuation amplitude (small, moderate, large), load level (low, moderate, high).

[0051] Output control: line switching (hold, partial switching, full switching), load regulation (no regulation, partial regulation, full regulation), backup power state (off, standby, start).

[0052] Second, the fuzzy reasoning process is carried out using the Mamdani fuzzy reasoning model; 3. Integration and optimization of expert systems, using pre-set knowledge bases and reasoning engines to provide optimized self-healing strategies in complex fault scenarios, including: Knowledge base construction: Based on historical fault data, expert experience and wind farm operation specifications, a detailed knowledge base is established, including various fault types, possible response measures and their priorities.

[0053] Reasoning engine: When a fault occurs, the reasoning engine will deduce the optimal self-healing strategy based on the current fault information and the rules in the knowledge base. The reasoning engine uses a combination of forward reasoning and reverse reasoning to reach a conclusion as quickly as possible.

[0054] 4. Multi-dimensional collaborative control strategy; Multi-dimensional fault self-healing operations will be performed simultaneously to achieve all-round grid restoration; 5. Real-time dynamic adjustment and feedback mechanism; State monitoring and feedback: monitor system state changes in real time, and feed back monitoring data to the fuzzy controller and expert system to update reasoning parameters; Adaptive control optimization: Automatically optimize fuzzy rules and expert system strategies based on feedback data to improve the accuracy and response speed of self-healing control.

[0055] S9. Fault Cause Analysis Based on Knowledge Graph By establishing the relationship between the data nodes of wind farm equipment, lines, and environmental factors, and using graph algorithms to mine the root causes that may lead to failures, the knowledge graph helps identify the complex causal relationships hidden in the data, allowing the system to make a more accurate root cause analysis of the failure.

[0056] In this embodiment, a fault detection device for an overhead line of a wind farm is provided, the fault detection device comprising a memory, a processor, and a fault detection program stored in the memory and executable on the processor, the fault detection program, when executed by the processor, implements the steps of the above-mentioned method for detecting the fault of an overhead line of a wind farm.

[0057] In this embodiment, a computer-readable storage medium is provided, on which a fault detection program is stored. When the fault detection program is executed by a processor, the steps of the above-mentioned method for detecting a fault of an overhead line in a wind farm are implemented.

[0058] The beneficial effects of the present invention are: The wind farm overhead line fault detection method, device and storage medium can quickly capture high-frequency signal characteristics in the early stage of a fault, and achieve accurate positioning, prediction and real-time response to the fault through multi-dimensional analysis and control strategies; At the same time, the combination of fuzzy logic and expert systems enables the system to dynamically adjust strategies when facing various types of faults, optimize line operation modes, adjust load distribution, and start backup power supplies. This not only significantly improves fault detection and response speed, but also improves the intelligence of fault self-healing and the reliability of power grid operation, and can effectively reduce the impact of faults on the stability of the entire power grid.

[0059] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting faults in overhead lines of a wind farm, characterized in that: The following steps are involved: S1, intelligent data collection; Comprehensive monitoring and collection of data from wind farm overhead lines based on sensors, and fusion of multi-source data; S2, fault signal feature extraction and compression representation; Using wavelet transform and Fourier transform signal processing technology, fault feature signals are extracted from multi-source data, and multi-scale analysis is performed on the collected time series data to separate features in different frequency bands. High-dimensional features are compressed into low-dimensional space through principal component analysis (PCA) or sparse coding methods to retain the most important fault information. S3, simulation modeling of fault propagation path; The fault propagation model of the wind farm overhead line is constructed through electromagnetic field simulation technology to simulate the propagation path of the fault signal in the power line. Based on the actual line parameters such as resistance, inductance, capacitance and environmental conditions such as wind speed and temperature, the finite element analysis method is used to calculate the transmission characteristics of the fault signal in different line sections. S4, Fault pattern recognition based on artificial intelligence; Use deep learning models to automatically identify fault modes, and use large-scale training data sets for supervised learning to automatically identify common fault types, such as short circuits, broken wires, and insulation damage; S5, adaptive algorithm for fault location; The impedance method combined with the particle swarm optimization algorithm (PSO) is used to accurately locate the fault point. The impedance method is used to calculate the preliminary fault location, and the PSO algorithm is used to perform optimization search to find the coordinates of the fault point with the smallest error; S6. Real-time detection of fault arc characteristics; During the detection process, ultra-high frequency (UHF) signals are used to detect the characteristics of the fault arc in real time, and the key features of the arc signal are further extracted and amplified through intelligent filters and signal enhancement algorithms; S7, constructing a spatiotemporal dynamic fault warning model; Based on the results of pattern recognition, a spatiotemporal dynamic fault warning model is constructed. Geographic Information System (GIS) technology is used to map fault information to the physical geographical location of the wind farm in real time. Through spatiotemporal data modeling and analysis, combined with historical data and current real-time monitoring data, the possible development trend and impact range of the fault are predicted; S8. Build a multi-dimensional fault self-healing control strategy; After the fault is detected and initially located, the multi-dimensional fault self-healing control strategy is automatically activated, including switching line operation mode, adjusting load distribution, and starting backup power supply operations. Fuzzy logic control and expert system technology are used to make the best decision in an instant to minimize the impact of the fault on the entire power grid. S9. Fault cause analysis based on knowledge graph; By establishing the relationship between the data nodes of wind farm equipment, lines, and environmental factors, and using graph algorithms to mine the root causes that may lead to failures, the knowledge graph helps identify the complex causal relationships hidden in the data, allowing the system to make a more accurate root cause analysis of the failure.

2. A method for detecting faults in overhead lines of a wind farm according to claim 1, characterized in that: In step S1, the sensors include current, voltage, temperature, vibration, and sound wave sensors, and multiple physical quantities are collected simultaneously based on multi-source sensors to obtain all data of the wind farm overhead lines; Through high-precision time synchronization technology, all data are integrated into a unified time base, and data fusion algorithms such as Kalman filtering and multi-scale clustering are used to perform preliminary processing on different sensor data to enhance the accuracy and reliability of fault signals.

3. A method for detecting faults in overhead lines of a wind farm according to claim 1, characterized in that: In step S2, when performing multi-scale analysis on the collected time series data, a multi-scale signal decomposition model is constructed, and the fault signal is deeply analyzed and decomposed by combining the nonlinear characteristics and spectrum information of the time series signal. The multi-scale signal decomposition model includes a signal decomposition formula, a kernel function, a multi-scale spectral decomposition formula and an adaptive scale optimization mechanism.

4. A method for detecting faults in overhead lines of a wind farm according to claim 3, characterized in that: In the multi-scale signal decomposition model: Signal decomposition formula, assuming that the original signal It is a nonlinear time series signal containing multiple frequency components. In order to decompose the signal, a nonlinear kernel function is introduced. The formula is as follows: ； in, Indicated in scale The signal components after decomposition; It is a scale-dependent kernel function, which is used to perform convolution operations on signals at a specific scale; Represents a multi-scale parameter, which is used to control the width of the kernel function. As it increases, the kernel function will capture the low-frequency characteristics of the signal; Kernel function, select the nonlinear Gauss-Lorentz composite kernel function to enhance the nonlinear feature extraction capability of the signal. The kernel function is as follows: ； in, Control the decay speed of the Gaussian part to capture the high-frequency part of the signal; Control the peak shape of the Lorentz part to extract low-frequency features; By combining the advantages of Gaussian and Lorentz distributions, the high-frequency and low-frequency characteristics of the signal can be effectively captured at different scales, and the nonlinear changes of the fault signal can be better adapted.

5. A method for detecting faults in overhead lines of a wind farm according to claim 3, characterized in that: In the multi-scale signal decomposition model: Multi-scale spectral decomposition formula, for the original signal Perform multi-scale transformation to obtain a series of decomposition signals of different scales , the signal is analyzed at different scales, each scale The signal is represented as: ； In this process, as the scale As increases, the kernel function gradually becomes wider, so that the low-frequency components are gradually separated; Adaptive scale optimization mechanism, based on which the scale selection problem under different fault scenarios is solved and the multi-scale energy ratio of the signal is defined for: ； By maximizing , the system can automatically select the optimal scale To extract fault features, the optimal scale The following conditions are met: ； At the selected scale The signal energy is most concentrated and the fault characteristics are most obvious.

6. A method for detecting faults in overhead lines of a wind farm according to claim 3, characterized in that: When the multi-scale signal decomposition model is used, it includes the following specific processes: Initial signal analysis: First, the original fault signal collected Conduct preliminary analysis to determine the overall frequency distribution of the signal, and select the appropriate multi-scale parameter range by observing the signal characteristics for subsequent nonlinear multi-scale spectral decomposition; Step-by-step execution of multi-scale decomposition: Use the model to decompose the signal step by step, by The signal components Through analysis, high-frequency features reflecting transient disturbances of the line and low-frequency features representing the impact of fault persistence are extracted respectively; Adaptively select the optimal scale: During the decomposition process, the multi-scale energy ratio at each scale is calculated in real time , and automatically adjust the scale according to the formula to make the decomposition at the optimal scale This ensures that no matter how the fault type changes, the model can extract fault features at an appropriate scale; Compressed representation and feature retention: Once the signal is decomposed at the optimal scale, the decomposed data is compressed and represented through principal component analysis (PCA). PCA reduces the high-dimensional multi-scale signal data to a low dimension while retaining its main fault feature information, which is used to reduce the data dimension and improve the computational efficiency of subsequent fault mode recognition and diagnosis.

7. A method for detecting faults in overhead lines of a wind farm according to claim 1, characterized in that: In step S3, line parameters, environmental factors and nonlinear losses are introduced into the fault propagation model; Line parameters, considering the distributed parameters of overhead lines, such as resistance ,inductance ,capacitance and admittance Produces different responses at different frequencies; Environmental factors, such as wind speed ,temperature ,humidity Dynamic effects on signal propagation; Nonlinear loss: the amplitude and frequency characteristics of the fault signal are affected by both the electromagnetic characteristics of the line and the environmental conditions; The fault propagation model is as follows: ; in, is in position and time Voltage distribution at is the propagation speed, which is the result of the interaction of medium parameters, electromagnetic constants and environmental factors; is the nonlinear loss term introduced, which refers to the attenuation of the signal under different environmental conditions; function Expressing the loss and voltage , Current And environmental parameters , such as the complex relationship between wind speed and temperature.

8. A method for detecting faults in overhead lines of a wind farm according to claim 1, characterized in that: In step S8, when constructing the multi-dimensional fault self-healing control strategy, the following specific processes are included:

1. Construction of fuzzy logic controller; Based on fuzzy logic theory, it processes uncertainty and fuzzy information in the input signal, including the following control dimensions: Line operation mode switching control: after a fault is detected, the line operation mode is switched, such as switching from parallel operation to series operation, to ensure system stability; Load distribution adjustment control, adjust the load distribution on each line to avoid overload problems in the fault section to balance the overall system load; Backup power supply start-up control: Automatically start the backup power supply when necessary to maintain the continuity of power supply and reduce the impact of power outage areas; Second, the fuzzy reasoning process is carried out using the Mamdani fuzzy reasoning model; 3. Integration and optimization of expert systems, using pre-set knowledge bases and reasoning engines to provide optimized self-healing strategies in complex fault scenarios; 4. Multi-dimensional collaborative control strategy; Multi-dimensional fault self-healing operations will be performed simultaneously to achieve all-round grid restoration; 5. Real-time dynamic adjustment and feedback mechanism; State monitoring and feedback: monitor system state changes in real time, and feed back monitoring data to the fuzzy controller and expert system to update reasoning parameters; Adaptive control optimization: Automatically optimize fuzzy rules and expert system strategies based on feedback data to improve the accuracy and response speed of self-healing control.

9. A fault detection device for overhead lines in a wind farm, characterized in that: The fault detection device includes a memory, a processor, and a fault detection program stored in the memory and executable on the processor. When the fault detection program is executed by the processor, the steps of the wind farm overhead line fault detection method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a fault detection program, and when the fault detection program is executed by the processor, the steps of the method for detecting a fault of an overhead line in a wind farm are implemented as described in any one of claims 1 to 8.

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