Conduction detection system and method for lightning protection device of wind power generation blade

Abstract

The invention discloses a conduction detection system and method for a lightning protection device of a wind power generation blade, and belongs to the technical field of wind power lightning protection. The system comprises a digital twinborn model establishing module used for establishing a digital twinborn model comprising a wind power generation blade and a lightning protection device; the conduction state determination module is used for determining the actual conduction state of the lightning protection device; the conduction failure reason determination module is used for determining the conduction failure reason of the lightning protection device if the actual conduction state is conduction failure; and the conduction failure processing module is used for determining whether remote processing can be carried out or not, if so, determining a corresponding processing mechanism and a remote processing scheme, and carrying out conduction failure processing on the lightning protection device. According to the scheme, the problem can be immediately detected and the response can be made when the conduction failure occurs, so that the downtime and the maintenance cost are reduced. Faults can be identified and predicted more accurately, and the accuracy and reliability of decision making are improved. And through remote diagnosis and repair, the processing efficiency is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of wind power lightning protection, and specifically relates to a conduction detection system and method for a lightning protection device for a wind power blade. Background Art

[0002] In order to prevent lightning from damaging wind turbine blades, lightning protection devices such as lightning receivers and down conductors are usually installed on the blades. These devices can quickly conduct lightning current to the ground, thereby protecting the blades and the entire wind turbine system from lightning damage. If the lightning protection device loses its conductive performance, it will not be able to effectively conduct lightning current to the ground, and thus cannot achieve the expected lightning protection effect.

[0003] Nowadays, continuity testing is usually carried out using professional testing instruments and equipment, such as ground resistance testers and milliohm meters. During the testing process, it is necessary to conduct a comprehensive inspection of all parts of the lightning protection device, including lightning arresters, down conductors, and grounding systems. At the same time, it is also necessary to evaluate and judge the test results according to relevant technical specifications and standards to ensure that the conduction performance of the lightning protection device meets the requirements.

[0004] However, traditional continuity detection is usually performed at a specific time point or periodically, and cannot reflect the status changes of the lightning protection device in real time. In addition, traditional detection can only detect specific parts of the lightning protection device, such as the lightning rod and the down conductor, and it is difficult to fully evaluate the performance of the entire system. At the same time, traditional detection methods are also affected by the experience of the detection personnel, and insufficient experience may lead to missed detection or misjudgment. Summary of the invention

[0005] The present application belongs to the field of wind power lightning protection technology, and specifically relates to a conduction detection system and method for a lightning protection device for a wind power blade, which solves the problem that traditional conduction detection is usually performed at a specific time point or periodically, and cannot reflect the state change of the lightning protection device in real time. In addition, traditional detection can often only detect specific parts of the lightning protection device, and it is difficult to comprehensively evaluate the performance of the entire system. At the same time, traditional detection methods are also affected by the experience of the detection personnel, and insufficient experience may lead to missed detection or misjudgment.

[0006] In a first aspect, an embodiment of the present application provides a conduction detection system for a lightning protection device for a wind power generation blade, the system comprising: A digital twin model establishment module is used to obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and to establish a digital twin model including wind turbine blades and lightning protection devices according to the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; A conduction state determination module is used to obtain real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters, and input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the digital twin model to obtain the actual conduction state of the lightning protection device; A conduction failure cause determination module, for inputting the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters into a preset cause detection model to obtain the conduction failure cause of the lightning protection device if the actual conduction state is a conduction failure; The conduction failure processing module is used to determine whether remote processing can be performed based on the conduction failure cause and the preset fault processing plan. If remote processing is possible, the corresponding processing mechanism and remote processing plan are determined based on the conduction failure cause and the preset fault processing plan, and the conduction failure processing of the lightning protection device is performed according to the remote processing plan.

[0007] Furthermore, the conduction failure processing module is used to: If the conduction failure cause is in the remote processing type of the preset fault handling solution, it is determined that remote processing can be performed, and a processing mechanism corresponding to the conduction failure cause is determined; If the processing mechanism is an automatic recovery mechanism, a first target recovery device is determined according to the cause of the conduction failure, and a recovery method for the first target device is determined according to a preset fault processing solution; A remote processing scheme is determined according to the first target recovery device and the first target device recovery method, and a conduction failure process of the lightning protection device is performed according to the remote processing scheme.

[0008] Furthermore, the conduction failure processing module is also used to: If the processing mechanism is a manual remote processing mechanism, a second target recovery device is determined according to the cause of the conduction failure, and a second target device recovery method is determined according to a preset fault processing solution; A remote processing scheme is determined according to the second target recovery device and the second target device recovery method, and the remote processing scheme is sent to the control center for the staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

[0009] Furthermore, the conduction failure processing module is also used to: If remote processing is not possible, determining whether the conduction failure cause is within the on-site processing type of the preset fault handling solution; If the conduction failure cause is in the field processing type of the preset fault handling solution, the processing mechanism is determined to be a manual field processing mechanism, and a manual field processing method is matched in the preset fault handling solution according to the conduction failure cause; A remote processing scheme is determined according to the conduction failure cause and the manual on-site processing method, and the remote processing scheme is sent to a control center for staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

[0010] Furthermore, the system also includes a health assessment model construction module, and the health assessment model construction module is used to: Obtain historical health status information of lightning protection devices; Determine the correlation coefficients of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device and historical health state information of the lightning protection device respectively; Determining historical health prediction characteristics of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of lightning protection devices respectively according to the correlation coefficient and a preset correlation coefficient threshold; Calculate the weight value corresponding to each historical health prediction feature according to the preset weight calculation formula and the correlation coefficient corresponding to each historical health prediction feature; Calculate the health score of the lightning protection device according to the weight value and a preset health score calculation formula; Creating a first data set based on historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, each historical health prediction feature, corresponding weight values, and a corresponding health score of the lightning protection device; A health assessment model for the lightning protection device is constructed, and the health assessment model is trained according to the first data set until the health assessment model reaches a preset health assessment model training standard.

[0011] Furthermore, the system further includes a health prediction module, which is used to: If the actual conduction state is successful conduction, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into the health assessment model to obtain a real-time health score of the lightning protection device; If the real-time health score is lower than a preset health score threshold, maintenance information is sent to the control center.

[0012] Furthermore, the preset weight calculation formula is: .

[0013] Furthermore, the preset health score calculation formula is: ; in, is the weight value corresponding to the first historical health prediction feature; For the first historical health prediction feature; is the weight value corresponding to the second historical health prediction feature; For the second historical health prediction feature; is the weight value corresponding to the nth historical health prediction feature; is the nth historical health prediction feature.

[0014] Furthermore, the training process of the preset cause detection model includes: Acquire historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes, and create a second data set based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes; A cause detection model is constructed, and the cause detection model is trained according to the second data set until the cause detection model reaches a preset cause detection model training standard.

[0015] In a second aspect, an embodiment of the present application provides a conduction detection method for a lightning protection device for a wind power blade, the method comprising: Obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices according to the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; Acquire real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters, input the real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters into the digital twin model, and obtain the actual conduction state of the lightning protection device; If the actual conduction state is conduction failure, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into a preset cause detection model to obtain the conduction failure cause of the lightning protection device; Determine whether remote processing can be performed based on the cause of the conduction failure and the preset fault handling plan. If remote processing is possible, determine the corresponding processing mechanism and remote processing plan based on the cause of the conduction failure and the preset fault handling plan, and perform conduction failure processing of the lightning protection device according to the remote processing plan.

[0016] In an embodiment of the present application, a digital twin model establishment module is used to obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; a conduction state determination module is used to obtain real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters, and the real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters. The device operating parameters and real-time environmental parameters are input into the digital twin model to obtain the actual conduction state of the lightning protection device; the conduction failure cause determination module is used to input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the preset cause detection model if the actual conduction state is conduction failure, and obtain the conduction failure cause of the lightning protection device; the conduction failure processing module is used to determine whether remote processing can be performed according to the conduction failure cause and the preset fault processing scheme. If remote processing can be performed, the corresponding processing mechanism and remote processing scheme are determined according to the conduction failure cause and the preset fault processing scheme, and the conduction failure of the lightning protection device is processed according to the remote processing scheme. Through the above-mentioned conduction detection system for the lightning protection device of wind power blades, the operating parameters are obtained and analyzed in real time, which can ensure that the problem can be detected and responded immediately when the conduction failure occurs, reducing downtime and maintenance costs. Based on the analysis of historical data and real-time data, faults can be identified and predicted more accurately, and the accuracy and reliability of decision-making can be improved. In the case where the fault can be handled remotely, the processing efficiency is improved through remote diagnosis and repair. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a structural schematic diagram of a conduction detection system for a wind turbine blade lightning protection device provided in Embodiment 1 of the present application and Embodiment 3 of the present application; Figure 2 It is a structural schematic diagram of a conduction detection system for a wind turbine blade lightning protection device provided in Embodiment 2 of the present application; Figure 3 It is a structural schematic diagram of a conduction detection system for a wind turbine blade lightning protection device provided in Example 4 of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for the convenience of description, only part of the present application is shown in the accompanying drawings, but not all of the content. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow chart describes each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but it can also have additional steps not included in the accompanying drawings. The process can correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0020] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0021] In the following, in conjunction with the accompanying drawings, the conduction detection system for a wind turbine blade lightning protection device provided in the embodiment of the present application is described in detail through specific embodiments and application scenarios.

[0022] Embodiment 1: Figure 1 This is a schematic diagram of the structure of a conduction detection system for a wind turbine blade lightning protection device provided in Example 1 of the present application. The system includes: The digital twin model establishment module 101 is used to obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices according to the historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; The conduction state determination module 102 is used to obtain real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters, and input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the digital twin model to obtain the actual conduction state of the lightning protection device; The conduction failure cause determination module 103 is used to input the real-time blade operation parameters, the real-time lightning protection device operation parameters and the real-time environmental parameters into a preset cause detection model to obtain the conduction failure cause of the lightning protection device if the actual conduction state is conduction failure; The conduction failure processing module 104 is used to determine whether remote processing can be performed based on the conduction failure cause and the preset fault processing plan. If remote processing is possible, the corresponding processing mechanism and remote processing plan are determined based on the conduction failure cause and the preset fault processing plan, and the conduction failure processing of the lightning protection device is performed according to the remote processing plan.

[0023] First of all, the usage scenario of this solution can be to establish a digital twin model including wind turbine blades and lightning protection devices, and input the collected real-time data into the digital twin model to confirm whether the lightning protection device fails to conduct, and in the case of failure of the lightning protection device to determine the cause of the conduction failure through a preset cause detection model, and if the cause of the conduction failure can be handled remotely, determine the remote processing solution, and handle the scenario through the remote processing solution.

[0024] Based on the above usage scenarios, it can be understood that the executor of the present application can be the conduction detection system of the wind turbine blade lightning protection device, and no excessive limitations are made here.

[0025] In this solution, the historical blade parameters may include blade structural parameters and historical blade operating parameters. The blade structural parameters may include the length, material, shape, thickness, etc. of the blade.

[0026] Historical blade operating parameters may include blade vibration frequency, rotation speed, temperature, stress and strain, etc., which can be obtained through sensor data or maintenance records on the blade.

[0027] The historical lightning protection device parameters may include the lightning protection device structural parameters and the historical lightning protection device operation parameters. The lightning protection device structural parameters may include the design specifications, materials, and connection methods of the lightning protection device.

[0028] The historical lightning protection device operating parameters may include the current, voltage, and grounding resistance of the lightning protection device.

[0029] The historical environmental parameters may include environmental data such as historical wind speed, wind direction, temperature, and humidity.

[0030] The historical conduction status of the lightning protection device may refer to the operating status of the lightning protection device at different time points, and may include whether it is normally conducted (good connection), failed to conduct (bad connection or interruption), etc.

[0031] Wind turbine blades can be an important part of wind turbines, converting wind energy into mechanical energy through rotation. The structure and operating state of the blades directly affect the efficiency and stability of wind power generation.

[0032] Lightning protection devices can be part of a wind power generation system to protect wind turbines from damage caused by lightning strikes. They prevent equipment damage by conducting high voltage current to the ground.

[0033] A digital twin model can be a virtual model that reflects the operating status and performance of physical equipment. By collecting real-time data from physical equipment and inputting it into a digital model, the behavior of the equipment can be simulated and predicted to help with maintenance and optimization. The digital twin model of this solution can be a virtual model that integrates wind turbine blades and lightning protection devices. It presents physical objects in a virtual space in a digital way and simulates their behavioral characteristics in a real environment.

[0034] In order to establish an accurate digital twin model to simulate the performance of wind turbine blades and lightning protection devices, historical parameters can be fully obtained. Specifically, they can include blade structural parameters obtained from design documents, technical specifications and field measurements, as well as blade operating parameters (such as wind speed, power generation and vibration) collected through the wind turbine monitoring system. At the same time, the structural parameters of the lightning protection device can be obtained from design drawings, installation records or on-site surveys, while the operating parameters (such as lightning monitoring data and performance test reports) are usually provided by professional lightning protection testing agencies. In addition, historical environmental parameters such as meteorological and geological data need to be collected, which can be obtained from meteorological departments, geological exploration departments or research institutions. The historical conduction status of the lightning protection device depends on its regular inspection, testing and maintenance records. After obtaining and organizing these historical parameters, a virtual model of the wind turbine blade and lightning protection device is constructed using 3D modeling and simulation software, integrated into the digital twin platform, and its behavioral characteristics in the real environment are simulated through simulation technology. The model should be able to simulate the structure and operating status of the blade and lightning protection device. The model is validated using some historical data to ensure that it accurately reflects the real status and behavior of the blades and lightning protection devices.

[0035] Real-time blade operating parameters can reflect the dynamic characteristics and status of wind turbine blades during operation, such as vibration frequency, rotation speed, temperature, stress and strain, etc.

[0036] Real-time lightning protection device operating parameters can reflect the dynamic characteristics and status of the lightning protection device during operation, such as conductive current, voltage, and grounding resistance.

[0037] Real-time environmental parameters can reflect the real-time conditions of the environment in which wind turbine blades and lightning protection devices are located, such as wind speed, temperature, humidity, and rainfall.

[0038] The actual conduction state can reflect whether the lightning protection device is normally conducted at a specific moment, for example, normal conduction, conduction failure, partial conduction, etc.

[0039] Data can be transmitted to the monitoring system in real time through wireless communication or wired connection through sensors installed on the blades, such as accelerometers, strain gauges and temperature sensors. Data can be transmitted to the monitoring system in real time through wireless communication or wired connection through current sensors, voltage sensors and ground resistance sensors installed on the lightning protection device. Data can be transmitted to the monitoring system in real time through wireless communication or wired connection through meteorological stations or environmental sensors installed around the wind turbine, such as anemometers, thermometers, hygrometers and rain gauges. The first data collected from each sensor, including real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters, are integrated into a centralized data processing system. The integrated real-time data is input into the digital twin model, and the model simulates the current operating status of the blade and lightning protection device. The digital twin model calculates, predicts and analyzes the actual conduction status of the lightning protection device based on the input data. The model outputs the actual conduction status of the lightning protection device and determines whether the lightning protection device is normally conducted based on the real-time data. The output can be "normal conduction", "conduction failure" or other status indications.

[0040] The preset cause detection model can be a specially designed and trained model for analyzing and diagnosing the cause of the lightning protection device conduction failure. The purpose of this model is to predict or explain the specific cause of the lightning protection device conduction failure by inputting relevant parameters.

[0041] The cause of the conduction failure may be a specific problem or fault that prevents the lightning protection device from conducting properly. For example, it may include blade problems: abnormal vibration, that is, the blade vibration amplitude is too large, resulting in loose or damaged connections. Structural damage, that is, cracks, breaks or other structural damage to the blades. Lightning protection device problems: too high grounding resistance, that is, the lightning protection device is poorly grounded and the resistance value exceeds the safety threshold. Abnormal current or voltage, that is, the internal circuit of the lightning protection device fails, resulting in abnormal current or voltage. Environmental problems: too strong lightning strikes, that is, the lightning strike current or voltage is too high, exceeding the tolerance range of the lightning protection device. The environmental humidity is too high, that is, high humidity causes the insulation performance of the lightning protection device to deteriorate.

[0042] In order to monitor and diagnose key components in wind power generation systems in real time, high-precision sensors can be used to obtain real-time blade operating parameters, including vibration frequency, rotation speed, temperature, and stress and strain, which directly reflect the health and performance of the blades. At the same time, the operating parameters of the real-time lightning protection device are also closely monitored, such as conduction current, voltage, and grounding resistance, to ensure that the lightning protection system can work effectively under any weather conditions. In addition, real-time environmental parameters such as wind speed, temperature, humidity, and rainfall are collected to fully understand the impact of the external environment on system performance. All of this real-time data is instantly input into the preset cause detection model, which uses advanced algorithms to analyze this data and accurately diagnose the possible causes of conduction failure of the lightning protection device. Finally, the model will output detailed diagnostic results, clearly indicating the specific reasons for the conduction failure of the lightning protection device.

[0043] A preset fault handling solution can be a set of predefined measures and steps to solve the problem of lightning protection device conduction failure. These solutions can be based on historical data, expert experience and industry standards, and can provide appropriate handling methods in different fault scenarios.

[0044] The handling mechanism may include an automatic recovery mechanism, where the system automatically attempts to repair the fault, usually applicable to software or electronic control problems, automatically restarting the equipment or reconfiguring parameters. Manual remote handling mechanism, maintenance personnel remotely access the system to diagnose and repair the equipment, adjust control parameters, remotely restart the equipment or perform remote diagnostic procedures. Manual on-site handling mechanism, maintenance personnel need to go to the site for physical inspection and repair, replace parts, repair structural damage or perform detailed physical diagnosis.

[0045] The remote processing solution may be a preset set of specific steps and measures to solve the conduction failure problem when remote processing is possible.

[0046] The system detects that the lightning protection device fails to conduct. The specific cause of the conduction failure can be determined through real-time data and the preset cause detection model. According to the detected cause of the conduction failure, the preset fault handling solution is searched to determine whether remote processing can be performed. If remote processing is possible, the appropriate processing mechanism is selected according to the cause of the conduction failure and the preset fault handling solution. According to the selected processing mechanism, the corresponding remote processing solution is executed. For example, a conduction failure is detected: real-time data is input into the digital twin model, and a conduction failure is detected. The cause of the conduction failure may be that the grounding resistance is too high. The preset fault handling solution is searched, and it is found that the grounding resistance is too high and can be solved through the remote processing mechanism. The system selects the manual remote processing mechanism. The remote operation and maintenance personnel access the lightning protection device through the remote system. Reconfigure the grounding system or adjust the control parameters. The system monitors whether the grounding resistance value returns to normal.

[0047] In an embodiment of the present application, a digital twin model establishment module is used to obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; a conduction state determination module is used to obtain real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters, and the real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters. The device operating parameters and real-time environmental parameters are input into the digital twin model to obtain the actual conduction state of the lightning protection device; the conduction failure cause determination module is used to input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the preset cause detection model if the actual conduction state is conduction failure, and obtain the conduction failure cause of the lightning protection device; the conduction failure processing module is used to determine whether remote processing can be performed according to the conduction failure cause and the preset fault processing scheme. If remote processing can be performed, the corresponding processing mechanism and remote processing scheme are determined according to the conduction failure cause and the preset fault processing scheme, and the conduction failure of the lightning protection device is processed according to the remote processing scheme. Through the above-mentioned conduction detection system for the lightning protection device of wind power blades, the operating parameters are obtained and analyzed in real time, which can ensure that the problem can be detected and responded immediately when the conduction failure occurs, reducing downtime and maintenance costs. Based on the analysis of historical data and real-time data, faults can be identified and predicted more accurately, and the accuracy and reliability of decision-making can be improved. In the case where the fault can be handled remotely, the processing efficiency is improved through remote diagnosis and repair.

[0048] On the basis of the above technical solution, optionally, the conduction failure processing module is used to: If the conduction failure cause is in the remote processing type of the preset fault handling solution, it is determined that remote processing can be performed, and a processing mechanism corresponding to the conduction failure cause is determined; If the processing mechanism is an automatic recovery mechanism, a first target recovery device is determined according to the cause of the conduction failure, and a recovery method for the first target device is determined according to a preset fault processing solution; A remote processing scheme is determined according to the first target recovery device and the first target device recovery method, and a conduction failure process of the lightning protection device is performed according to the remote processing scheme.

[0049] In this solution, the preset fault handling scheme may include remote handling type and on-site handling type. The remote handling type may be to handle the conduction failure of the lightning protection device by automatic machine recovery or manual remote operation. The on-site handling type may be to handle the conduction failure of the lightning protection device by going to the location of the lightning protection device manually.

[0050] An automatic recovery mechanism can mean that after a system detects a specific type of fault, it automatically performs a preset repair operation and attempts to restore normal operation without human intervention. This mechanism can include fault identification, which is to determine the type and specific location of the fault. Fault isolation, which is to isolate the fault area to prevent further impact on the system. Automatic repair, which is to perform preset repair operations, such as restarting the device, reconfiguring the software, switching to a backup system, etc. Verification and recovery, which is to confirm that the repair operation is successful and restore the normal operation of the system.

[0051] The first target recovery device can refer to the device that the system needs to prioritize and recover after detecting a specific fault, that is, the fault corresponding to the automatic recovery mechanism. These devices are key points for fault handling and can include control systems responsible for the control and monitoring of wind turbine blades and lightning protection devices. Sensors are used to monitor the operating status of blades, lightning protection device status and environmental parameters. Communication equipment ensures real-time data transmission and remote control functions. Electrical components ensure the normal operation of power supply and electrical connections.

[0052] The first target device recovery method may refer to specific repair operations performed on the target recovery device, which are adjusted according to different fault types. It may include: if the cause of the conduction failure is a software failure, the device may be restarted, that is, the control system or related software module may be automatically restarted. Reconfiguration, that is, restoring the software to the default configuration or reloading the configuration file. Patch update, that is, automatically installing a software patch or update. If the cause of the conduction failure is a sensor failure, the sensor may be calibrated and recalibrated through an automatic calibration procedure. Sensor switching, switching to a backup sensor. If the cause of the conduction failure is a communication failure, the communication module may be restarted, that is, the communication device or module may be automatically restarted. Reconnection, that is, reestablishing the communication link. If the cause of the conduction failure is an electrical failure, a power supply check may be performed, that is, checking and resetting the power supply. Circuit restart, that is, automatically restarting the electrical component or switching to a backup circuit. If the cause of the conduction failure is a data failure, data retransmission may be performed, that is, retransmitting the lost or erroneous data. Data verification, that is, performing data integrity verification and correcting erroneous data.

[0053] If the cause of the conduction failure is one or more of the software failure, sensor failure, communication failure, electrical failure, and data failure in the preset fault handling scheme, the handling mechanism is determined to be an automatic recovery mechanism. Subsequently, the equipment that needs to be processed first, such as control systems, sensors, communication equipment, or electrical components, is identified to accurately locate the repair point. Next, based on the type of fault and the target device, the most appropriate recovery method is selected, such as restarting the device, reconfiguring the software, calibrating the sensor, checking and reconnecting the communication line, etc. Once the recovery method is determined, the corresponding repair operation is automatically performed to quickly respond to and solve the problem. Finally, the success of the repair operation is confirmed through the verification step to ensure that the system is restored to a normal operating state, thereby fully restoring the conduction function of the device.

[0054] In this solution, a preset repair operation can be automatically executed when a conduction failure is detected to ensure normal conduction of the lightning protection device and improve the reliability and maintenance efficiency of the system.

[0055] On the basis of the above technical solution, optionally, the conduction failure processing module is further used to: If the processing mechanism is a manual remote processing mechanism, a second target recovery device is determined according to the cause of the conduction failure, and a second target device recovery method is determined according to a preset fault processing solution; A remote processing scheme is determined according to the second target recovery device and the second target device recovery method, and the remote processing scheme is sent to the control center for the staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

[0056] In this solution, the manual remote processing mechanism can mean that when the system detects a specific type of fault (such as software or firmware fault, sensor calibration, parameter optimization, and network communication problem), the system automatically generates a processing plan and sends the plan to the control center through remote communication, and the staff performs manual intervention and repair operations according to the plan. This mechanism may include: fault identification, that is, the system automatically detects and identifies the fault type. Solution generation, that is, generating detailed processing steps and instructions based on the preset fault handling plan. Solution transmission, that is, sending the processing plan to the control center through remote communication. Manual intervention, that is, the staff of the control center performs specific repair operations according to the received plan. Verification and confirmation, that is, the staff verifies the repair effect to ensure that the problem is solved.

[0057] The second target recovery device can refer to the devices that need to be prioritized and recovered by the staff in the manual remote processing mechanism. These devices are key points in remote intervention and can include: Software and firmware components, that is, software modules and firmware that need to be updated, restarted or reconfigured. Sensors, that is, sensor devices that need to be calibrated or adjusted. Communication equipment, that is, network communication equipment that needs to be checked and optimized. Control systems, that is, control systems that need to optimize parameters or troubleshoot.

[0058] The second target device recovery method may refer to a specific repair operation performed by the staff on the target recovery device in the manual remote processing mechanism. These methods are adjusted according to different fault types. If the cause of the conduction failure is a software or firmware failure, remote update can be performed, that is, updating the software or firmware through remote operation. Remote restart, that is, remote restarting the affected software module or firmware. Reconfiguration, that is, remotely reconfiguring the software settings or parameters according to instructions. If the cause of the conduction failure is sensor calibration, remote calibration can be performed, that is, remote calibration of the sensor, and calibration instructions are sent through the control center. Adjust parameters, that is, remotely adjust the parameter settings of the sensor according to real-time data. If the cause of the conduction failure is parameter optimization, parameter tuning can be performed, that is, remote optimization of the operating parameters of the control system or device according to the cause of the failure. Configuration update, that is, remote updating of the configuration file to optimize system performance. If the cause of the conduction failure is a network communication problem, network check can be performed, that is, remote checking of the network connection status and communication link. Reconnection, that is, remotely performing a reconnection operation to ensure that the communication link is normal.

[0059] The control center can refer to the central facility for managing and monitoring the wind power generation system and its lightning protection device. The control center is responsible for receiving fault information, handling fault solutions, performing remote operations, and coordinating maintenance work. Its main responsibilities include monitoring and management, and real-time monitoring of the operating status of the wind power generation system and its lightning protection device. Fault response, that is, receiving fault information and handling solutions sent by the system, and quickly responding to and handling faults. Remote operation, that is, executing remote operation instructions, remotely repairing or adjusting the affected equipment. Maintenance coordination, that is, coordinating on-site and remote maintenance work to ensure the reliability and stability of system operation.

[0060] When the system detects that the conduction failure is caused by software or firmware failure, sensor calibration problems, parameter optimization needs or network communication obstacles, detailed processing steps and operation instructions can be generated according to the preset fault handling solutions. Subsequently, the system clearly identifies the equipment that needs to be handled first, such as software and firmware components, sensors, communication equipment or control systems. Based on the type of fault and the target device, the system intelligently selects the applicable recovery method, such as remote software update, remote sensor calibration, system parameter optimization or network status check. These treatment solutions are quickly transmitted to the control center through remote communication channels, and professional staff perform the necessary repair operations according to the detailed solutions received. Finally, the staff strictly verifies the repair effect to ensure that all problems are effectively resolved and confirm that the system has fully resumed normal operation.

[0061] In this scheme, a detailed treatment plan can be generated when a specific fault is detected, and repairs can be effectively performed through a manual remote processing mechanism to ensure the normal conduction of the lightning protection device.

[0062] On the basis of the above technical solution, optionally, the conduction failure processing module is further used to: If remote processing is not possible, determining whether the conduction failure cause is within the on-site processing type of the preset fault handling solution; If the conduction failure cause is in the field processing type of the preset fault handling solution, the processing mechanism is determined to be a manual field processing mechanism, and a manual field processing method is matched in the preset fault handling solution according to the conduction failure cause; A remote processing scheme is determined according to the conduction failure cause and the manual on-site processing method, and the remote processing scheme is sent to a control center for staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

[0063] In this solution, the manual on-site processing mechanism may refer to the need to dispatch maintenance personnel to the site to handle when the system detects that certain faults cannot be solved by remote means. It may include fault identification, where the system automatically detects and identifies the cause of the conduction failure. Solution generation, where detailed on-site processing steps and instructions are generated according to the preset fault handling solution. Notification and dispatch, where the processing solution is sent to the control center via remote communication and the on-site maintenance team is notified. On-site operation, where on-site maintenance personnel perform specific repair operations according to the received solution. Verification and reporting, where maintenance personnel verify the repair effect and report the results to the control center.

[0064] Manual on-site processing methods may refer to specific repair operations performed by maintenance personnel on target devices in the preset fault handling scheme in the on-site processing mechanism. These methods are adjusted according to different fault types and may include hardware repair, specifically, replacement of parts, i.e. replacement of faulty hardware components or parts, such as sensors, lines, electrical components, etc. Repairing connections, i.e. repairing or replacing damaged connection cables, connectors, etc. Equipment calibration. This may include manual calibration, i.e. manual calibration of sensors or devices on-site to ensure their accuracy. Adjusting settings, i.e. adjusting the settings or parameters of the device as needed. Mechanical maintenance, which may include lubrication and maintenance, i.e. lubricating, cleaning and maintaining mechanical parts to ensure their normal operation. Inspection and repair, i.e. inspecting the wear of mechanical parts and making necessary repairs or replacements. Environmental adjustment, which may include environmental control, i.e. adjusting the environmental conditions around the device, such as temperature and humidity, to ensure that the device operates in a suitable environment.

[0065] If remote processing is not possible, determine whether the cause of the conduction failure is in the on-site processing type of the preset fault handling solution. If so, match the corresponding manual on-site processing method in the preset fault handling solution according to the cause of the conduction failure, such as replacing damaged parts, implementing manual calibration, performing necessary mechanical maintenance, or adjusting the equipment operating environment. The on-site maintenance personnel then efficiently perform the repair operation according to the detailed plan received. After the repair is completed, the maintenance personnel strictly verify the repair effect to ensure that the problem is properly resolved, and report the verification results to the control center in a timely manner for subsequent tracking and recording. For example, the cause of the conduction failure is a sensor failure, which cannot be repaired by remote means. The solution is to replace the faulty sensor. Then notify the on-site maintenance team and dispatch maintenance personnel. The maintenance personnel can replace the faulty sensor, verify that the sensor is working properly, and report the results.

[0066] This solution can generate a detailed on-site processing plan when a fault that cannot be handled remotely is detected, and effectively repair it through a manual on-site processing mechanism to ensure the normal conduction of the lightning protection device.

[0067] Embodiment 2: Figure 2 Schematic diagram of the structure of the conduction detection system for the wind turbine blade lightning protection device provided in the second embodiment of the present application. Figure 2 As shown, specifically including the following: The system further includes a health assessment model building module 105, wherein the health assessment model building module 105 is used to: Obtain historical health status information of lightning protection devices; Determine the correlation coefficients of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device and historical health state information of the lightning protection device respectively; Determining historical health prediction characteristics of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of lightning protection devices respectively according to the correlation coefficient and a preset correlation coefficient threshold; Calculate the weight value corresponding to each historical health prediction feature according to the preset weight calculation formula and the correlation coefficient corresponding to each historical health prediction feature; Calculate the health score of the lightning protection device according to the weight value and a preset health score calculation formula; Creating a first data set based on historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, each historical health prediction feature, corresponding weight values, and a corresponding health score of the lightning protection device; A health assessment model for the lightning protection device is constructed, and the health assessment model is trained according to the first data set until the health assessment model reaches a preset health assessment model training standard.

[0068] In this embodiment, the historical health status information may refer to the health status record of the lightning protection device during past operation, which generally includes data such as fault records, maintenance records, and performance indicators.

[0069] The correlation coefficient can be a numerical value in statistics that is used to describe the strength and direction of the relationship between two variables. It can include the Pearson correlation coefficient and the Spearman rank correlation coefficient. The correlation coefficient ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0070] The preset correlation coefficient threshold can be a set value used to determine which features have a significant correlation with the health status. For example, the threshold is set to 0.5, indicating that only features with an absolute value of the correlation coefficient greater than 0.5 are considered important features.

[0071] The historical health prediction features may be important variables extracted from historical data and related to the health status of the lightning protection device. The historical health prediction features of historical blade parameters may include historical health prediction features of blade structural parameters and historical health prediction features of historical blade operating parameters, wherein the historical health prediction features of blade structural parameters may include blade length, blade weight, and material composition. The historical health prediction features of historical blade operating parameters may include blade vibration frequency and amplitude, blade rotation speed, blade stress and strain data, historical operating time, and operating power. The historical health prediction features of historical lightning protection device parameters may include historical health prediction features of lightning protection device structural parameters and historical health prediction features of historical lightning protection device operating parameters, wherein the historical health prediction features of lightning protection device structural parameters may include wire diameter, wire material, and grounding resistance. The historical health prediction features of historical lightning protection device operating parameters may include current intensity (conduction current), voltage, grounding resistance value changes, lightning strike times and intensity, and historical maintenance and inspection records of lightning protection devices. The historical health prediction features of historical environmental parameters may include meteorological conditions: wind speed and direction, temperature, humidity, air depressant, and rainfall. Environmental conditions, air quality index, salt spray concentration (seaside environment), and seismic activity (if applicable). The historical health prediction features of the historical conduction status of the lightning protection device may include the conduction status record: conduction success / failure record, conduction time, electrical parameters (current, voltage) during conduction, historical conduction failure reasons, and the recovery time and method of the conduction device.

[0072] The preset weight calculation formula may be a mathematical formula for calculating the weight of each feature according to its correlation coefficient.

[0073] The weight value can be calculated according to a preset weight calculation formula and is used to measure the contribution of each historical health prediction feature to the health score.

[0074] The preset health score calculation formula may be a mathematical formula for combining the weights and feature values ​​of each feature to calculate a comprehensive health score of the lightning protection device.

[0075] The health score can be a comprehensive value calculated based on the characteristic value and the weight to evaluate the current health status of the lightning protection device. The health score can range from 0 to 100, and the higher the score, the better the health status.

[0076] The first data set may be a historical first data set for training and testing a health assessment model, which may include historical blade parameters, historical lightning protection device parameters, historical environmental parameters, historical conduction states, health prediction features, weight values, and corresponding health scores.

[0077] The health assessment model may be a machine learning model trained using the first data set and used to predict the health status of the lightning protection device, and may include linear regression, decision tree, random forest, and the like.

[0078] The preset health assessment model training standard may refer to the performance indicators that the model needs to achieve during the training process, such as accuracy, recall rate, and F1 score, etc. These standards are used to evaluate whether the model meets the expected prediction accuracy and effect.

[0079] In order to accurately evaluate the health status of the lightning protection device, the health status records of the lightning protection device in the past can be systematically collected, including the historical parameters of the lightning protection device, the blade parameters related to it, the environmental parameters, and the historical conduction status of the lightning protection device. Subsequently, the correlation coefficient between these parameters and the historical health status of the lightning protection device is calculated by statistical methods to quantify the strength of the association between them. Next, based on the calculated correlation coefficient and the preset significance threshold, the features that have a significant impact on the health status are screened out. In order to further improve the accuracy of the prediction, according to the preset weight calculation formula, combined with these correlation coefficients, a corresponding weight value is assigned to each screened feature. After determining the key features of health prediction and their weights, a health scoring mechanism is designed, which combines the specific values ​​of the features with their respective weights, and generates a quantitative health score for the lightning protection device through the preset health score calculation formula. This score intuitively reflects the health status of the lightning protection device. Finally, a comprehensive first data set is constructed using the above-mentioned processed historical data, the screened health prediction features, the calculated weight values, and the health score. Based on this first data set, a machine learning model was trained and continuously optimized until the model reached the preset training standard, thus realizing intelligent assessment of the health status of the lightning protection device.

[0080] In this embodiment, by real-time monitoring and analysis of health prediction features, potential failures or health problems can be identified in advance, reducing equipment failures and downtime. Based on the prediction results of the health assessment model, predictive maintenance can be implemented to ensure that the equipment is handled before serious problems occur, thereby avoiding regular maintenance based on fixed time intervals.

[0081] On the basis of the above technical solution, optionally, the system further includes a health prediction module, and the health prediction module is used to: If the actual conduction state is successful conduction, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into the health assessment model to obtain a real-time health score of the lightning protection device; If the real-time health score is lower than a preset health score threshold, maintenance information is sent to the control center.

[0082] In this solution, the real-time health score can be a value obtained by evaluating the current state of the lightning protection device through the health assessment model, which is used to indicate the health status of the lightning protection device. The real-time health score can reflect the health status of the lightning protection device under the current environment and operating conditions. This score helps to determine whether further measures are needed, such as inspection or maintenance.

[0083] The preset health score threshold can be a predetermined health score value. When the real-time health score is lower than this threshold, it indicates that there is a potential problem with the health status of the device. This threshold can be used to distinguish between normal status and status that requires attention. It helps identify whether the lightning protection device is in a high-risk state and requires further inspection or maintenance.

[0084] Maintenance information can refer to detailed information about the current health status of the lightning protection device sent by the system to the control center, as well as recommended follow-up operations (such as inspection, repair or maintenance). Specifically, it can include health score, the current calculated real-time health score. Abnormal description, explaining why the health score is lower than the threshold, which may include abnormal parameters, abnormal operating status, etc. Recommended measures, based on the situation where the health score is lower than the threshold, recommend specific maintenance operations or maintenance steps. Urgency, can indicate the urgency of the problem (for example, whether it needs to be handled immediately).

[0085] Real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters can be input into the health assessment model. The health assessment model calculates the real-time health score of the lightning protection device based on these parameters. The calculated real-time health score is compared with the preset health score threshold. If the real-time health score is lower than the threshold, the system will generate and send maintenance information to the control center. If the real-time health score is higher than the threshold, the equipment status is considered normal and no further action is required.

[0086] In this solution, preventive maintenance of lightning protection devices, rather than waiting until a failure occurs to repair them, can reduce equipment downtime and improve the overall operating efficiency of wind turbines. Through real-time health scoring, potential equipment failures can be detected in time before the problem becomes serious. Early detection helps prevent small problems from developing into major failures, thereby reducing maintenance costs and downtime.

[0087] Based on the above technical solution, the optional preset weight calculation formula is: .

[0088] On the basis of the above technical solution, optionally, the preset health score calculation formula is: ; in, is the weight value corresponding to the first historical health prediction feature; For the first historical health prediction feature; is the weight value corresponding to the second historical health prediction feature; For the second historical health prediction feature; is the weight value corresponding to the nth historical health prediction feature; is the nth historical health prediction feature.

[0089] Embodiment three: Figure 1 Schematic diagram of the structure of the conduction detection system for the wind turbine blade lightning protection device provided in the third embodiment of the present application. Figure 1 As shown, the training process of the preset cause detection model includes: Acquire historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes, and create a second data set based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes; A cause detection model is constructed, and the cause detection model is trained according to the second data set until the cause detection model reaches a preset cause detection model training standard.

[0090] In this embodiment, the historical conduction failure cause may refer to the specific cause of the conduction failure event of the lightning protection device that occurred in the past, for example, it may include software failure, bug or configuration error of the system software leading to conduction failure. Sensor failure, inaccurate sensor data or sensor failure itself. Communication failure, network problem or communication interface failure in data transmission. Electrical failure, electrical component failure, such as wiring problem, short circuit and overload. Data failure, wrong data input or processing leading to conduction failure. Environmental factors, extreme weather conditions or other environmental influences leading to equipment failure.

[0091] The second data set may be a data set used to train a cause detection model, and includes historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes.

[0092] The preset cause detection model training standard may refer to the standard that needs to be met when training the cause detection model to ensure the effectiveness and reliability of the model. Specifically, it may include training accuracy, where the accuracy or recall rate of the model on the training data reaches the preset threshold. Verification accuracy, where the performance of the model on the verification data reaches the preset standard, such as accuracy, recall rate, F1 score, etc. Overfitting check, where the performance gap between the model on the training data and the verification data is small to avoid overfitting. Training time, where the model completes training within a reasonable time and meets the requirements of actual applications. Model stability, where the performance of the model on different data sets is stable and the results are consistent. Model complexity, where the complexity of the model matches the actual application requirements, where it can fully express the features without being too complex.

[0093] The historical blade parameters such as blade length, width, material type, operating speed and angle adjustment records can be obtained through the sensors and control systems of wind power generation equipment; the historical lightning protection device parameters such as lightning protection device model, installation location, maintenance records, conduction current and voltage are extracted from the sensors and monitoring systems of lightning protection devices; at the same time, historical environmental parameters such as temperature, humidity, wind speed, wind direction and rainfall are collected from environmental monitoring stations or meteorological data providers; in addition, the specific causes, occurrence times, treatment methods and results of historical conduction failures are summarized from maintenance records, equipment logs and fault reports. Next, data integration was carried out to ensure that the timestamps of all relevant data were aligned for accurate analysis. Subsequently, a strict data cleaning process was implemented to handle missing values, outliers and duplicate data, and to unify the data format. In the data annotation stage, the cause of conduction failure was set as the target variable of the model, and all other parameters were used as feature variables. After that, the integrated and cleaned data set was split into training and test sets for model training and verification. In the model selection phase, the applicability of various machine learning models such as decision trees, random forests, support vector machines, and neural networks was evaluated, and the model that best suits the current data set and needs was selected. The model was trained using the training set, and the hyperparameters were continuously adjusted during the process to optimize the model performance. Finally, the model was verified using the test set, and key indicators such as accuracy, precision, recall, and F1 score were evaluated. The model was optimized based on the verification results, effectively avoiding overfitting or underfitting problems, thereby building a reliable conduction failure detection model.

[0094] In this embodiment, the cause detection model is used to automatically identify and diagnose the cause of the fault, reduce manual troubleshooting time, and improve the work efficiency of maintenance personnel.

[0095] Embodiment 4: Figure 3 1 is a flow chart of a conduction detection method for a lightning protection device for wind turbine blades provided in the fourth embodiment of the present application. Figure 3 As shown, specifically including the following: S301, obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters.

[0096] S302, obtain real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters, input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the digital twin model, and obtain the actual conduction state of the lightning protection device.

[0097] S303, if the actual conduction state is conduction failure, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into a preset cause detection model to obtain the conduction failure cause of the lightning protection device.

[0098] S304, determining whether remote processing is possible based on the cause of the conduction failure and a preset fault handling solution; if remote processing is possible, determining a corresponding processing mechanism and a remote processing solution based on the cause of the conduction failure and a preset fault handling solution, and performing conduction failure processing of the lightning protection device based on the remote processing solution.

[0099] In the embodiment of the present application, historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device are obtained, and a digital twin model including wind turbine blades and lightning protection devices is established based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters are obtained, and the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters are converted into the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time lightning protection device operating parameters. The operating parameters and real-time environmental parameters are input into the digital twin model to obtain the actual conduction state of the lightning protection device; if the actual conduction state is conduction failure, the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters are input into the preset cause detection model to obtain the conduction failure cause of the lightning protection device; according to the conduction failure cause and the preset fault handling scheme, it is determined whether remote processing can be performed. If remote processing can be performed, the corresponding processing mechanism and remote processing scheme are determined according to the conduction failure cause and the preset fault handling scheme, and the conduction failure of the lightning protection device is processed according to the remote processing scheme. Through the above-mentioned conduction detection method for the lightning protection device of wind power blades, the operating parameters are obtained and analyzed in real time, which can ensure that the problem can be detected and responded immediately when the conduction failure occurs, reducing downtime and maintenance costs. Based on the analysis of historical data and real-time data, faults can be identified and predicted more accurately, and the accuracy and reliability of decision-making can be improved. In the case where the fault can be handled remotely, the processing efficiency is improved through remote diagnosis and repair.

[0100] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0101] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0102] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0103] The above are only preferred embodiments of the present application and the technical principles used. The present application is not limited to the specific embodiments described herein, and various obvious changes, readjustments and substitutions that can be made by those skilled in the art will not deviate from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the claims.

Claims

1. A conduction detection system for a wind turbine blade lightning protection device, characterized in that: The system comprises: A digital twin model establishment module is used to obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and to establish a digital twin model including wind turbine blades and lightning protection devices according to the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; A conduction state determination module is used to obtain real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters, and input the real-time blade operating parameters, real-time lightning protection device operating parameters and real-time environmental parameters into the digital twin model to obtain the actual conduction state of the lightning protection device; A conduction failure cause determination module, for inputting the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters into a preset cause detection model to obtain the conduction failure cause of the lightning protection device if the actual conduction state is a conduction failure; The conduction failure processing module is used to determine whether remote processing can be performed based on the conduction failure cause and the preset fault processing plan. If remote processing is possible, the corresponding processing mechanism and remote processing plan are determined based on the conduction failure cause and the preset fault processing plan, and the conduction failure processing of the lightning protection device is performed according to the remote processing plan.

2. The conduction detection system for a wind turbine blade lightning protection device according to claim 1, characterized in that: The conduction failure processing module is used to: If the conduction failure cause is in the remote processing type of the preset fault handling solution, it is determined that remote processing can be performed, and a processing mechanism corresponding to the conduction failure cause is determined; If the processing mechanism is an automatic recovery mechanism, a first target recovery device is determined according to the cause of the conduction failure, and a recovery method for the first target device is determined according to a preset fault processing solution; A remote processing scheme is determined according to the first target recovery device and the first target device recovery method, and a conduction failure process of the lightning protection device is performed according to the remote processing scheme.

3. The conduction detection system for a wind turbine blade lightning protection device according to claim 2, characterized in that: The conduction failure processing module is further used for: If the processing mechanism is a manual remote processing mechanism, a second target recovery device is determined according to the cause of the conduction failure, and a second target device recovery method is determined according to a preset fault processing solution; A remote processing scheme is determined according to the second target recovery device and the second target device recovery method, and the remote processing scheme is sent to the control center for the staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

4. The conduction detection system for a wind turbine blade lightning protection device according to claim 1, characterized in that: The conduction failure processing module is further used for: If remote processing is not possible, determining whether the conduction failure cause is within the on-site processing type of the preset fault handling solution; If the conduction failure cause is in the field processing type of the preset fault handling solution, the processing mechanism is determined to be a manual field processing mechanism, and a manual field processing method is matched in the preset fault handling solution according to the conduction failure cause; A remote processing scheme is determined according to the conduction failure cause and the manual on-site processing method, and the remote processing scheme is sent to a control center for staff to perform conduction failure processing of the lightning protection device according to the remote processing scheme.

5. The conduction detection system for a wind turbine blade lightning protection device according to claim 1, characterized in that: The system further comprises a health assessment model construction module, wherein the health assessment model construction module is used to: Obtain historical health status information of lightning protection devices; Determine the correlation coefficients of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device and historical health state information of the lightning protection device respectively; Determining historical health prediction characteristics of historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of lightning protection devices respectively according to the correlation coefficient and a preset correlation coefficient threshold; Calculate the weight value corresponding to each historical health prediction feature according to the preset weight calculation formula and the correlation coefficient corresponding to each historical health prediction feature; Calculate the health score of the lightning protection device according to the weight value and a preset health score calculation formula; Creating a first data set based on historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, each historical health prediction feature, corresponding weight values, and a corresponding health score of the lightning protection device; A health assessment model for the lightning protection device is constructed, and the health assessment model is trained according to the first data set until the health assessment model reaches a preset health assessment model training standard.

6. The conduction detection system for a wind turbine blade lightning protection device according to claim 5, characterized in that: The system further comprises a health prediction module, wherein the health prediction module is used to: If the actual conduction state is successful conduction, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into the health assessment model to obtain a real-time health score of the lightning protection device; If the real-time health score is lower than a preset health score threshold, maintenance information is sent to the control center.

7. The conduction detection system for a wind turbine blade lightning protection device according to claim 5, characterized in that: The preset weight calculation formula is: 。 8. The conduction detection system for a wind turbine blade lightning protection device according to claim 1, characterized in that: The preset health score calculation formula is: ； in, is the weight value corresponding to the first historical health prediction feature; For the first historical health prediction feature; is the weight value corresponding to the second historical health prediction feature; For the second historical health prediction feature; is the weight value corresponding to the nth historical health prediction feature; is the nth historical health prediction feature.

9. The conduction detection system for a wind turbine blade lightning protection device according to claim 1, characterized in that: The training process of the preset cause detection model includes: Acquire historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes, and create a second data set based on the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction failure causes; A cause detection model is constructed, and the cause detection model is trained according to the second data set until the cause detection model reaches a preset cause detection model training standard.

10. A conduction detection method for a wind turbine blade lightning protection device, characterized in that: The method comprises: Obtain historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device, and establish a digital twin model including wind turbine blades and lightning protection devices according to the historical blade parameters, historical lightning protection device parameters, historical environmental parameters, and historical conduction states of the lightning protection device; wherein the historical blade parameters include blade structural parameters and historical blade operating parameters, and the historical lightning protection device parameters include lightning protection device structural parameters and historical lightning protection device operating parameters; Acquire real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters, input the real-time blade operating parameters, real-time lightning protection device operating parameters, and real-time environmental parameters into the digital twin model, and obtain the actual conduction state of the lightning protection device; If the actual conduction state is conduction failure, the real-time blade operating parameters, the real-time lightning protection device operating parameters and the real-time environmental parameters are input into a preset cause detection model to obtain the conduction failure cause of the lightning protection device; Determine whether remote processing can be performed based on the cause of the conduction failure and the preset fault handling plan. If remote processing is possible, determine the corresponding processing mechanism and remote processing plan based on the cause of the conduction failure and the preset fault handling plan, and perform conduction failure processing of the lightning protection device according to the remote processing plan.

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