Automatic recovery control method and device for communication interruption of laser radar of wind turbine generator

Abstract

The invention provides a wind turbine laser radar communication interruption automatic recovery control method and device, overcomes the defects in the prior art, provides a rapid control method of a laser radar communication automatic recovery technology, and effectively solves the problem of communication interruption caused by high frequency in a wind power plant. The method has the technical advantages of high reliability, low-cost maintenance and high compatibility, is one of key technologies for improving the intelligent level and economical efficiency of the wind turbine generator, and can improve the operation safety and reliability of the wind turbine generator under extreme wind conditions based on multi-information fusion sensor redundancy diagnosis and fault-tolerant control.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine communication technology, specifically relating to a method and device for automatic recovery control of communication interruption of wind turbine lidar. Background Technology

[0002] As a key sensor for wind turbines to achieve feedforward control, redundant control and protection, and load optimization, lidar wind measurement technology emits a laser beam in front of the wind turbine and receives reflected signals from atmospheric aerosols. It can proactively and non-contactly measure the original, undisturbed incoming wind conditions, including wind speed, wind direction, wind shear, turbulence intensity, and other information at a distance of tens to hundreds of meters ahead. By scanning the wind field data in front in real time, it provides key inputs for pitch angle adjustment, yaw control, and other functions.

[0003] However, when the turbine blades block the laser beam for extended periods, or during weather conditions such as fog, dust storms, or snow, the lidar may experience communication interruptions and signal loss. High-frequency noise from the turbine's electrical equipment can also cause signal loss. Lidar communication interruptions can cause the wind turbine to revert to a traditional "passive control mode," resulting in power generation losses and increased fatigue loads on critical components. Therefore, the main control system needs to integrate an automatic recovery algorithm to quickly restore the communication link and ensure the continuity of lidar data. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose an automatic recovery control method for communication interruption of lidar in wind turbine units.

[0006] The second objective of this invention is to provide an automatic recovery control device for communication interruption of lidar in wind turbine generators.

[0007] To achieve the above objectives, a first aspect of the present invention provides a method for automatic recovery control of communication interruption of a wind turbine lidar system, comprising: S1, acquire the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two; S2, determine whether the lidar signal is abnormal based on the change in the difference value; if the difference value is zero, start the abnormal duration counter. S3, detect whether the abnormal duration exceeds the preset threshold. If it does, set the communication abnormal status flag and trigger the main control system. S4, the main control system sends an activation signal to the lidar to trigger a hardware reset, and at the same time resets the communication abnormal status flag; S5. After communication is restored, the main control system initiates multi-sensor data fusion, which weights and fuses the lidar wind speed signal with the cabin anemometer data; based on the fused wind speed data, the yaw control parameters are dynamically adjusted to achieve smooth switching of control modes.

[0008] In one embodiment of the present invention, S1 includes: S11, the wind speed signal of the current sampling period is time-aligned with the wind speed signal of the previous sampling period through the timestamp synchronization module; S12 uses a ring buffer structure to store historical wind speed signals, ensuring the real-time performance and integrity of data storage during continuous sampling periods.

[0009] In one embodiment of the present invention, S2 includes: S21, when the absolute value of the difference is less than the preset fluctuation threshold and the duration exceeds the first sub-threshold, it is determined that the low frequency signal is abnormal and the counter is started; S22, the high-frequency noise component in the signal is detected by the electromagnetic noise feature recognition module. If the high-frequency noise intensity exceeds the second sub-threshold, the counter operation is paused.

[0010] In one embodiment of the present invention, S3 includes: S31, dynamically adjust the threshold based on real-time weather data, and increase the threshold by a preset ratio when fog, dust, or snow is detected; S32 combines the joint change characteristics of wind speed and wind direction signals to determine anomalies. If the wind speed signal is abnormal and the wind direction signal is also abnormal, the communication recovery process will be triggered first.

[0011] In one embodiment of the present invention, S4 includes: S41 adopts a phased reset strategy. First, a soft reset command is sent and a preset response time is waited. If no acknowledgment signal is received, a hard reset command is sent. S42 synchronously sends status detection requests to the backup lidar through redundant communication channels to ensure that the main control system maintains wind condition data input during recovery.

[0012] To achieve the above objectives, a second aspect of the present invention provides an automatic recovery control device for communication interruption of a wind turbine lidar system, comprising: The signal acquisition and difference calculation module is used to acquire the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two. The signal anomaly judgment and counting start module is used to judge whether the lidar signal is abnormal based on the change of the difference value. If the difference value is zero, the anomaly duration counter is started. The abnormal duration detection and triggering module is used to detect whether the abnormal duration exceeds a preset threshold. If it does, the communication abnormal status flag is set and the main control system is triggered. The hardware reset control and flag reset module is used by the main control system to send an activation signal to the lidar to trigger a hardware reset, and at the same time reset the communication abnormal status flag. The data fusion and dynamic adjustment module is used to perform weighted fusion of lidar wind speed signals and cabin anemometer data after communication is restored; and dynamically adjust yaw control parameters based on the fused wind speed data to achieve smooth switching of control modes.

[0013] The present invention discloses an automatic recovery control method and device for LiDAR communication interruption of wind turbine units, which effectively solves the problem of frequent communication interruption in wind farms. It can improve the operational safety and reliability of the unit under extreme wind conditions by using multi-information fusion-based sensor redundancy diagnosis and fault-tolerant control.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an automatic recovery control method for LiDAR communication interruption in a wind turbine according to an embodiment of the present invention; Figure 2 This is a flowchart of a lidar communication anomaly detection method according to an embodiment of the present invention; Figure 3 This is a flowchart of a lidar communication anomaly recovery control method according to an embodiment of the present invention; Figure 4 This is a flowchart of an alternative solution to the lidar communication anomaly detection method according to an embodiment of the present invention; Figure 5 This is a structural diagram of an automatic recovery control device for LiDAR communication interruption of a wind turbine according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] The following description, with reference to the accompanying drawings, describes an automatic recovery control method and apparatus for lidar communication interruption of a wind turbine according to an embodiment of the present invention.

[0019] Example 1 Figure 1 This is a flowchart of an automatic recovery control method for communication interruption of a wind turbine lidar according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: S1: Obtain the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two.

[0020] Specifically, in the automatic recovery control method for LiDAR communication anomalies in wind turbine units, the step of "acquiring the LiDAR wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculating the difference between the two" is the core link in realizing communication status monitoring. This step identifies whether there is data freezing or communication interruption in the LiDAR by comparing time series data, thereby triggering the subsequent automatic recovery mechanism.

[0021] In some implementations, the lidar wind speed signal V(k) is acquired by a forward-looking scanning lidar within the current sampling period and uploaded to the signal processing unit of the main control system. The signal processing unit typically performs data acquisition and processing at a fixed sampling frequency (e.g., 10Hz or 20Hz) to ensure that the time resolution of the wind speed signal meets control requirements. Simultaneously, the system caches the wind speed signal V(k-1) from the previous sampling period in memory for difference calculation with the current signal. The difference ΔV = |V(k) - V(k-1)|, where the absolute value operation is used to eliminate the influence of the wind speed fluctuation direction, focusing only on the magnitude of the wind speed change.

[0022] In practical applications, this step is typically deployed in the main control PLC or DCS system of wind turbines as part of the communication status monitoring module. In scenarios with frequent wind changes or significant environmental interference (such as sandstorms, strong wind shearing, etc.), this method can effectively identify abnormal states of the lidar communication link, avoid misjudgments, and improve system robustness.

[0023] This step, by introducing time-series difference analysis, enables dynamic monitoring of lidar wind speed signals, providing a reliable basis for subsequent automatic recovery control logic. Its innovation lies in combining the temporal characteristics of wind speed signals with a threshold judgment mechanism, thereby improving the operational continuity and control accuracy of wind turbines in complex environments, demonstrating significant engineering practical value.

[0024] Furthermore, S1 includes: S11 uses the timestamp synchronization module to perform time alignment processing on the wind speed signals of the current sampling period and the previous sampling period.

[0025] Specifically, in some implementations, aligning the wind speed signals of the current sampling period with those of the previous sampling period using a timestamp synchronization module is one of the key technical steps in this invention for realizing the detection and recovery control of lidar communication anomalies. This step aims to eliminate the time misalignment of wind speed signals caused by communication delays, data packet loss, or inconsistent sampling frequencies, thereby ensuring that the main control system has an accurate time reference and data consistency when comparing and judging wind speed signals.

[0026] The timestamp synchronization module is typically integrated into the signal processing unit. Its core function is to add precise timestamp information to the wind speed data for each sampling period. This timestamp can be obtained based on the high-precision real-time clock (RTC) of the main control system or through an external time synchronization protocol (such as the IEEE 1588 Precision Time Protocol or NTP Network Time Protocol) to ensure consistency with the time base of other control systems of the wind turbine. In the data acquisition unit, the lidar collects wind speed data at a fixed sampling frequency (such as 10Hz or 20Hz) and uploads the data packets along with the timestamps to the main control system. After receiving the wind speed data for the current period, the main control system first extracts its timestamp and compares it with the timestamp of the data from the previous period, calculating the time difference Δt between the two. If Δt exceeds the set synchronization tolerance range (such as ±50ms), a time alignment algorithm is triggered to interpolate or compensate for the delay in the current period's data to achieve alignment with the data from the previous period in the time dimension.

[0027] Key parameters of the timestamp synchronization module include sampling frequency, timestamp accuracy, synchronization tolerance threshold, and data dwell time. The sampling frequency is typically set between 10Hz and 50Hz to balance real-time performance and data stability. Timestamp accuracy should be controlled within ±10ms to meet the timing requirements of the wind turbine control system. The synchronization tolerance threshold can be dynamically adjusted based on the stability of the actual communication link, and is generally set between 50ms and 100ms. The data dwell time is used to buffer misaligned data to prevent misjudgments due to brief communication delays, and is typically set between 200ms and 500ms.

[0028] This step, by introducing a timestamp mechanism, significantly improves the accuracy and robustness of wind speed signal comparison, effectively avoiding misjudgments caused by time misalignment. In lidar communication anomaly detection, time alignment is a prerequisite for determining whether the wind speed signal is "constantly unchanged," thereby improving the sensitivity and reliability of communication interruption identification. Furthermore, this technique enhances the adaptive control capability of wind turbines in extreme environments, providing a solid data foundation for subsequent automatic recovery control logic and improving the overall system's operational efficiency and safety.

[0029] S12 uses a ring buffer structure to store historical wind speed signals, ensuring the real-time performance and integrity of data storage during continuous sampling periods.

[0030] Specifically, in this invention, the use of a circular buffer structure to store historical wind speed signals is a key data management mechanism in the method for detecting and automatically recovering communication anomalies in lidar. This step involves constructing a fixed-size circular buffer in the signal processing unit to continuously and efficiently store wind speed data collected by the lidar over multiple sampling periods, thereby ensuring that the system can still perform anomaly detection and recovery operations based on historical data when communication interruptions occur.

[0031] In some implementations, the capacity of the ring buffer can be configured based on the sampling frequency of the wind turbine and the length of historical data required for anomaly detection. For example, if the lidar sampling frequency is 10Hz and the historical data window required for anomaly detection is 10 seconds, then the buffer should store wind speed data for at least 100 sampling points. Each storage unit in the buffer can use a 32-bit floating-point format to ensure the accuracy of the wind speed signal, typically with a wind speed range of 0~30 m / s, corresponding to a numerical range of 0.0~30.0, meeting the IEEE 754 standard.

[0032] Furthermore, at each sampling period k, the system writes the current wind speed signal to the next available location in the buffer and updates the read pointer after writing, so that it can be used by subsequent anomaly detection algorithms. When the buffer is full, new data will overwrite the oldest data, thus achieving real-time data updates and historical data retention. This structure avoids the problem of frequent memory allocation and release in traditional linear buffers, improving system efficiency and stability.

[0033] This step operates on the embedded platform of the wind turbine's main control system, such as a control system based on a PLC or industrial-grade ARM processor. The sampling period is typically set to 100ms to 200ms to balance real-time performance and data integrity. Through efficient data management using a circular buffer, the system can quickly identify data stagnation when communication interruptions occur and trigger the Re_Innit activation signal to automatically restart the lidar and restore data, thereby ensuring the continuous operation and control accuracy of the wind turbine in complex environments.

[0034] S2, determine whether the lidar signal is abnormal based on the change in the difference value. If the difference value is zero, start the abnormal duration counter.

[0035] Specifically, in some implementations, the step of "judging whether the lidar signal is abnormal based on the numerical change of ΔV, and starting an abnormal duration counter if ΔV is zero" in the method of this invention is the core logic link in the lidar communication anomaly detection mechanism. This step calculates the difference ΔV between the lidar wind speed signal of the current sampling period and the previous sampling period, i.e., ΔV = V(k) - V(k-1), where V(k) is the wind speed value at the current moment and V(k-1) is the wind speed value at the previous moment, thereby determining whether the signal has undergone a valid change. Under normal operating conditions, wind conditions have dynamic characteristics, and the lidar wind speed signal should fluctuate continuously over time. If ΔV is zero, it indicates that the current wind speed value is completely consistent with the previous moment, which may mean that the lidar has not received a valid reflected signal, or that the communication link has been interrupted and is in a data frozen state.

[0036] Optionally, the calculation period for ΔV in this step can be set between 100ms and 500ms, depending on the sampling frequency of the wind turbine's main control system and the data update rate of the lidar. In practical applications, the threshold for ΔV can be set to 0.1m / s to 0.5m / s to exclude minor fluctuations caused by stable wind conditions or sensor accuracy limitations. When ΔV is continuously zero for a duration exceeding a preset abnormal duration threshold (e.g., set to 5 to 15 seconds), the lidar communication is determined to be abnormal, and the status flag is set.

[0037] Furthermore, this step is implemented through embedded logic in the wind turbine's main control system, typically integrated into the PLC or DCS control program, and employs a periodic scanning mechanism for real-time monitoring. Its technical value lies in effectively identifying communication interruptions caused by obstruction, noise interference, or harsh environments through dynamic signal change detection, avoiding misjudgments and improving the accuracy and timeliness of the system response. This mechanism provides a reliable prerequisite for subsequent automatic recovery of control logic and is a key technical support for realizing intelligent control and redundancy fault tolerance capabilities of wind turbines.

[0038] Furthermore, S2 includes: S21, when the absolute value of ΔV is less than the preset fluctuation threshold and the duration exceeds the first sub-threshold, it is determined that the low frequency signal is abnormal and the counter is started.

[0039] Specifically, when the absolute value of ΔV (i.e., the difference between the current period and the previous period's lidar wind speed signal) is less than a preset fluctuation threshold and the duration exceeds a first sub-threshold, the system determines that the low-frequency signal is abnormal and starts a counter. This step is one of the core logics of the lidar communication anomaly detection method in this invention, aiming to identify the abnormal state of wind speed signal remaining unchanged for a long time due to lidar communication interruption or signal obstruction.

[0040] In some implementations, ΔV is calculated as: ΔV = |V(k) - V(k-1)|, where V(k) is the wind speed signal in the current sampling period, and V(k-1) is the wind speed signal in the previous sampling period. The preset fluctuation threshold is typically set between 0.2 m / s and 0.5 m / s, and the specific value can be calibrated according to the typical wind speed variation range of the wind farm where the wind turbine is located. If ΔV is continuously less than this threshold, it indicates that the wind speed signal has not undergone the expected dynamic change, and may be in a state of communication interruption or data freeze.

[0041] Furthermore, the system introduces a first sub-threshold as a time-based criterion to measure the duration of signal anomalies. This sub-threshold is typically set between 30 and 60 seconds, and can be adjusted based on the lidar sampling frequency (e.g., 10 Hz or 20 Hz) and the minimum detectable time window for wind condition changes. When the cumulative time that ΔV is continuously less than the fluctuation threshold exceeds the first sub-threshold, the system determines it as a low-frequency signal anomaly and triggers a counter to start recording the duration of the anomaly, providing a basis for subsequent communication recovery logic.

[0042] In practical applications, this step is typically deployed in the signal processing unit of the wind turbine's main control system, running in the real-time control program of the PLC or DCS controller, and conforming to the IEC 61400-25 standard's requirements for monitoring the communication status of wind turbines. Its technical value lies in effectively distinguishing between normal low wind speed conditions and abnormal signal freeze states through statistical analysis of dynamic signal changes, thereby avoiding misjudgments and improving the accuracy and response speed of communication anomaly detection.

[0043] By implementing this step, the system can promptly identify LiDAR communication anomalies in a non-shutdown state, providing a reliable basis for automatically sending Re_Init activation signals and restarting the LiDAR communication link, thereby ensuring the stable operation and data continuity of wind turbine units in complex environments and improving the fault tolerance and intelligence level of the overall control system.

[0044] S22, the high-frequency noise component in the signal is detected by the electromagnetic noise feature recognition module. If the high-frequency noise intensity exceeds the second sub-threshold, the counter operation is paused.

[0045] Specifically, in some implementations, detecting high-frequency noise components in the signal through an electromagnetic noise feature identification module and pausing the counter operation when the high-frequency noise intensity exceeds a second sub-threshold is one of the key control steps in this invention for improving the stability of lidar communication. This module, based on digital signal processing (DSP) technology, performs real-time spectrum analysis of electromagnetic interference (EMI) in the lidar communication link to identify high-frequency noise components that may affect data transmission quality. Specifically, this module uses a Fast Fourier Transform (FFT) algorithm to perform frequency domain conversion on the acquired communication signal, setting the analysis frequency range to 100 kHz to 10 MHz, focusing on the noise energy distribution in the frequency band above 2 MHz.

[0046] Furthermore, the system determines the presence of abnormal electromagnetic interference by setting a second sub-threshold (e.g., a noise intensity threshold between -40 dBm and -20 dBm, the specific value of which can be adaptively adjusted according to the radar communication protocol and the on-site electromagnetic environment). When the high-frequency noise energy exceeds this threshold within a certain sampling period, the system triggers a counter pause mechanism to prevent misjudgments or abnormal data accumulation caused by noise interference. This pause operation sends a control command to the signal processing module through the main control unit, causing the counter to enter a "frozen" state until the noise intensity falls back to a safe range.

[0047] Optionally, this module can also incorporate a sliding average (SMA) algorithm to smooth the noise intensity across multiple consecutive sampling periods, thereby improving the robustness of the detection. For example, an average calculation can be performed using a sliding window of 5 sampling periods, and the counter can be paused only when the noise intensity continuously exceeds the threshold for 3 periods, thus avoiding malfunctions caused by instantaneous noise interference.

[0048] In practical applications, this step is particularly suitable for wind turbines operating in complex electromagnetic environments, such as areas near high-noise sources like frequency converters and transformers, or under severe weather conditions like thunderstorms and sandstorms. By monitoring and responding to electromagnetic noise in real time, the system can effectively isolate interference signals, improve the reliability of lidar data, and provide a stable data foundation for subsequent communication anomaly detection and automatic control recovery, thereby enhancing the operational safety and control accuracy of wind turbines under extreme conditions.

[0049] S3 detects whether the duration of the abnormality exceeds a preset threshold. If it does, the communication abnormality status flag is set and the main control system is triggered.

[0050] Specifically, in some implementations, the step of "detecting whether the abnormal duration exceeds a preset threshold ΔT, and if so, setting a communication abnormality flag and triggering the main control system" in this invention is the core judgment mechanism in the automatic recovery control method for lidar communication interruption. Its technical implementation is based on continuous analysis of time-series data and state machine logic control. Specifically, within each sampling period k, the main control system acquires the lidar wind speed signal at the current moment and compares it with the wind speed signal of the previous sampling period k-1. If the current wind speed signal is consistent with the signal of the previous period (i.e., ΔV = V(k) - V(k-1) = 0), it is considered that the lidar data has not changed, and there may be a communication interruption or signal loss. At this time, the system starts an abnormal duration counter. This counter accumulates using the sampling period Ts of the main control system (usually 10ms to 100ms) as the time unit, continuously monitoring the duration of the abnormal state.

[0051] Optionally, the setting of ΔT needs to be optimized based on the actual operating environment of the wind turbine and the sampling characteristics of the lidar. In typical applications, ΔT can be set to 3 to 10 seconds to ensure no misjudgment under brief obstruction or signal interference, while also responding promptly to genuine communication interruption events. Furthermore, when the cumulative duration of the abnormality exceeds ΔT, the system will set the "LiDAR communication abnormality flag" and trigger the main control system to send an activation signal to the lidar to restart the communication link and restore the data acquisition function.

[0052] In practical applications, this step is typically deployed in the main control PLC or DCS system of the wind turbine, running in a real-time operating system environment to ensure that the response speed meets the IEC 61400-25 standard requirements for the communication reliability of wind turbines. Its technical value lies in effectively identifying abnormal signal states through data consistency detection within a time window, avoiding malfunctions caused by momentary interference, and ensuring that the system can quickly respond and initiate a recovery mechanism when communication interruptions occur. This maintains the effectiveness of the wind turbine's feedforward control system, improving turbine operating efficiency and equipment lifespan.

[0053] Furthermore, S3 includes: S31, dynamically adjusts the ΔT threshold based on real-time weather data, and increases the ΔT threshold by a preset ratio when fog, dust, or snow is detected.

[0054] Specifically, in some implementations, this invention proposes a control strategy that dynamically adjusts the ΔT threshold based on real-time weather data to improve the adaptability and accuracy of lidar communication anomaly detection. The core technical principle of this step lies in introducing meteorological environmental variables to adaptively adjust the stability judgment threshold of the lidar wind speed signal, thereby avoiding misjudgment of communication interruption under severe weather conditions and ensuring the stable operation of the wind turbine control system.

[0055] The specific operation is as follows: A meteorological data interface is integrated into the main control system to acquire real-time weather information from the wind farm environment, including but not limited to PM2.5 concentration, visibility, snowfall intensity, wind speed, and humidity. When the system detects haze, dust storms, or snow, a preset logic judgment module proportionally increases the ΔT threshold (the threshold for the duration for which the lidar wind speed signal remains unchanged). For example, under normal weather conditions, the ΔT threshold can be set to 30 seconds (i.e., if the lidar wind speed signal does not change within 30 consecutive seconds, it is considered abnormal). However, under haze conditions, due to the increased concentration of aerosols in the atmosphere and severe attenuation of the laser reflection signal, radar data may experience short-term stagnation. In this case, the ΔT threshold can be increased by 10%-30%, i.e., adjusted to 33-45 seconds, to avoid falsely triggering the communication interruption flag.

[0056] Furthermore, this adjustment logic can be calibrated based on the requirements for communication reliability of wind turbine units in the IEC 61400-25 standard, combined with the equipment performance parameters (such as signal response time, maximum detection distance, environmental adaptability, etc.) provided by the lidar manufacturer. In practical applications, this step is typically deployed between the wind farm SCADA system and the wind turbine unit's main control PLC, using industrial communication protocols such as OPC UA or Modbus TCP to achieve real-time access to weather data and dynamic updates of the ΔT threshold.

[0057] This step plays a crucial role in the automatic recovery control method for LiDAR communication interruptions in wind turbine generators. By dynamically adjusting the ΔT threshold, the system can more accurately distinguish between signal delays caused by weather interference and genuine communication failures, thereby improving the robustness of communication anomaly detection. Under conditions of high pollution or extreme weather, this technology effectively reduces the false alarm rate and improves the operating efficiency and control accuracy of wind turbine generators in complex environments, demonstrating significant engineering practical value and innovation.

[0058] S32 combines the joint change characteristics of wind speed and wind direction signals to determine anomalies. If the wind speed signal is abnormal and the wind direction signal is also abnormal, the communication recovery process will be triggered first.

[0059] Specifically, in some implementations, the method proposed in this invention improves the accuracy and robustness of lidar communication anomaly detection by combining the joint change characteristics of wind speed and wind direction signals for anomaly determination. The core technical principle of this step is based on dynamic consistency analysis of wind data; that is, under normal operating conditions, wind speed and wind direction signals should exhibit a certain correlation and trend. If both are abnormal simultaneously, for example, if the wind speed signal remains constant for a long time and the wind direction signal does not change, this usually means that the lidar's scanning beam is blocked or affected by environmental interference (such as fog, dust, snow, etc.), preventing it from acquiring accurate wind data and thus causing communication interruption.

[0060] In the specific implementation, the signal processing unit simultaneously acquires the wind speed signal from the lidar within each sampling period k. With wind direction signal and with the previous period k-1 and Compare them. If and If the wind speed and direction signals remain unchanged, the anomaly timer begins to accumulate. When the duration of the anomaly exceeds a preset threshold (e.g., 30 seconds to 2 minutes, depending on the wind field environment and radar sampling frequency), a communication anomaly flag is triggered, and the communication recovery process begins.

[0061] Furthermore, the parameters involved in this step include the sampling period. (Typically 0.1 to 1 second), anomaly duration threshold, wind speed change threshold (e.g., 0.2 m / s), and wind direction change threshold (e.g., 2°). These parameters need to be configured appropriately according to the requirements for wind turbine communication reliability in the IEC 61400-25 standard to ensure effective identification of anomalies under different wind conditions and environmental conditions.

[0062] This step is applicable to wind turbines operating in complex weather conditions or with frequent blade shading, especially under low wind speed, high turbulence, or extreme weather conditions. It effectively avoids misjudgments and improves the timeliness and accuracy of communication recovery. By synchronously analyzing the changing characteristics of wind speed and direction signals, this method enhances the ability to identify lidar communication interruptions, thereby prioritizing the triggering of the communication recovery process when an anomaly occurs. This reduces the time it takes for the unit to enter passive control mode, improves power generation efficiency, and reduces fatigue loads on key components.

[0063] The technical effect of this step is that, through multivariate joint analysis, it significantly improves the sensitivity and specificity of communication anomaly detection, avoids false triggering caused by a single signal anomaly, enhances the system's fault tolerance and operational stability, and is an important technical support for realizing intelligent control of wind turbine units.

[0064] S4, the main control system sends an activation signal to the lidar to trigger a hardware reset, and at the same time resets the communication abnormality status flag.

[0065] Specifically, in some implementations, when the main control system detects that the lidar communication anomaly status flag is set, it sends a Re_Innit activation signal to the lidar to trigger its hardware reset, and simultaneously resets the communication anomaly status flag, thereby achieving automatic recovery of the communication link. This step is one of the core components of the lidar communication anomaly recovery control method in this invention. Its technical implementation is based on standardized communication protocols between the main control system and the lidar, such as Modbus TCP / IP or CANopen industrial communication protocols, ensuring the reliability and real-time performance of signal transmission.

[0066] The main control system receives wind speed signals from the lidar via periodic polling or interruption, and compares the wind speed value of the current sampling period (denoted as V(k)) with the wind speed value of the previous sampling period (denoted as V(k-1)). If V(k) ≠ V(k-1), the signal is considered normal; if V(k) = V(k-1), an abnormality timer is started. When the abnormal duration exceeds a set threshold time T (e.g., T = 10 seconds), the main control system sets a communication abnormality flag and triggers the transmission of the Re_Innit signal. The Re_Innit signal is a predefined hardware reset instruction, sent to the lidar control unit via a communication interface (such as RS485 or Ethernet), causing it to execute an internal hardware reset process, including restarting the laser emission module, initializing the signal receiving channel, and resetting the communication protocol stack.

[0067] The abnormal duration threshold T can be dynamically configured based on the wind turbine operating environment and the lidar sampling frequency. For example, when the lidar sampling frequency is 1Hz, T can be set to 10 sampling cycles, i.e., 10 seconds; if the sampling frequency is increased to 5Hz, T can be adjusted to 5 seconds accordingly. Furthermore, the transmission interval of the Re_Innit signal should be set to a reasonable value (e.g., 30 seconds to 5 minutes) to avoid frequent resets causing hardware damage, while ensuring timely communication recovery.

[0068] This step applies to situations where the lidar data freezes or communication is interrupted under complex weather conditions (such as sandstorms, fog, and snow) or mechanical obstruction (such as blades blocking the laser path). The main control system automatically executes this recovery logic in non-shutdown mode without manual intervention, thereby ensuring data continuity and control accuracy of the wind turbine in key functions such as feedforward control and yaw optimization.

[0069] This step, through a closed-loop control mechanism between the main control system and the lidar, enables rapid identification and recovery of communication interruptions, effectively improving the operational stability and data reliability of wind turbines in extreme environments. Its innovation lies in using dynamic changes in wind speed signals as the basis for judging communication status, combined with hardware reset commands, to construct a low-cost, highly compatible automatic recovery control strategy, providing key technical support for the intelligent operation of wind turbines.

[0070] Furthermore, S4 includes: S41 employs a phased reset strategy, first sending a soft reset command and waiting for a preset response time; if no acknowledgment signal is received, then sending a hard reset command.

[0071] Specifically, this invention employs a phased reset strategy to address lidar communication interruption issues. The underlying principle is based on the dynamic monitoring and response mechanism of the main control system to lidar data signals. The specific operation is as follows: when the main control system detects that the lidar wind speed signal has not changed within multiple consecutive sampling periods (i.e., the wind speed value in the current period k is the same as the wind speed value in the previous period k-1), it starts an anomaly timer to record the duration of the signal anomaly. If this duration exceeds a preset response time threshold (e.g., set to 30 seconds to 2 minutes, which can be adjusted according to the wind turbine operating environment and lidar sampling frequency), the main control system determines it as a communication anomaly and first sends a soft reset command (e.g., a Re_Init activation signal) to the lidar to attempt to restore the communication link. The soft reset command is typically issued via a standard serial communication protocol (e.g., Modbus RTU or CANopen), featuring non-destructive and low-risk characteristics, allowing the lidar to reinitialize the communication module without disconnecting the power supply.

[0072] If the main control system does not receive an acknowledgment (ACK) signal from the lidar within the set response window (e.g., 5 to 10 seconds) after the soft reset command is issued, a hard reset command will be executed. This will force a restart of the device by disconnecting and re-energizing the lidar's communication interface or controlling its internal reset pin. Hard reset operations typically involve physical control of the lidar's power module or communication module, and it is essential to ensure that the timing of the reset signal meets the device startup requirements (e.g., reset pulse width ≥ 100ms, power-on delay ≥ 500ms).

[0073] In practical applications, this phased reset strategy is suitable for scenarios where lidar communication is interrupted due to complex weather conditions (such as sandstorms, haze, and snow) or mechanical obstruction (such as blades blocking the laser path) in wind turbines. By prioritizing soft reset and using hard reset as a backup, the false trigger rate can be effectively reduced, and system stability and recovery efficiency can be improved. Its technical value lies in significantly enhancing the self-recovery capability of wind turbines in the event of communication anomalies, reducing the frequency of manual intervention, thereby improving the continuity of turbine operation and power generation efficiency. It is a crucial technical support for realizing intelligent control of wind turbines.

[0074] S42 synchronously sends status detection requests to the backup lidar through redundant communication channels to ensure that the main control system maintains wind condition data input during recovery.

[0075] Specifically, in some implementations, synchronously sending status detection requests to the backup lidar via redundant communication channels is a key control step in this invention to ensure that the wind turbine can maintain wind condition data input even when the main lidar communication is interrupted. This step is based on a dual-channel redundant communication architecture design and adopts a mechanism of parallel operation of the main and backup lidars to ensure the continuity of wind condition data and the stability of system control when the main control system performs communication recovery operations.

[0076] In some implementations, this step involves the main control system immediately sending a status detection request to the backup lidar via a pre-defined redundant communication link (such as a CAN bus, Ethernet redundant channel, or RS485 backup interface) after detecting a communication anomaly with the main lidar. The request signal contains a specific protocol frame format, such as a status query command in Modbus RTU or TCP / IP protocols, used to obtain the current operating status of the backup lidar, its data update frequency, and the validity of the wind speed signal. Simultaneously, the main control system starts a status response timer to ensure that valid feedback is received within a set time.

[0077] The sending cycle for status detection requests is typically set between 100ms and 500ms. The response timeout threshold is set at two consecutive cycles without receiving valid data, indicating a communication failure of the backup radar. The criteria for judging the validity of wind speed signals include: a data update frequency of no less than 10Hz, a wind speed change rate fluctuating within ±0.5m / s, and no consecutive zero or fixed values. Furthermore, the transmission delay of the redundant communication channel should be controlled within 5ms to meet real-time control requirements.

[0078] This procedure applies to scenarios where the main lidar communication of wind turbines is interrupted due to complex weather conditions (such as sandstorms, fog, snow) or mechanical obstruction (such as blades sweeping across the laser path). During the execution of the recovery logic by the main control system, the backup radar can serve as a temporary data source, providing continuous wind condition input for critical control functions such as yaw control and pitch adjustment, thereby preventing the unit from entering a "passive control mode".

[0079] This step, through redundant communication mechanisms and status detection logic, significantly improves the system robustness and data availability of wind turbines during lidar communication interruptions, effectively reducing power generation losses and equipment fatigue loads caused by communication failures. This demonstrates the innovation and practicality of this invention in the intelligent control of wind turbines.

[0080] S5. After communication is restored, the main control system initiates multi-sensor data fusion, which weights and fuses the lidar wind speed signal with the cabin anemometer data; based on the fused wind speed data, the yaw control parameters are dynamically adjusted to achieve smooth switching of control modes.

[0081] The present invention discloses a control method and device for automatic recovery of communication anomalies of lidar in wind turbine units, which effectively solves the problem of frequent communication interruptions in wind farms. It can improve the operational safety and reliability of the unit under extreme wind conditions by using multi-information fusion-based sensor redundancy diagnosis and fault-tolerant control.

[0082] Specifically, once communication is restored, the main control system activates the multi-sensor data fusion module to perform weighted fusion of the lidar wind speed signal and the nacelle anemometer data. This is a key step in this invention to enable the wind turbine to quickly and accurately acquire wind condition information after the lidar communication anomaly is restored. This step is based on the principles of sensor redundancy and fault-tolerant control. By fusing wind speed data from different sources, it improves the robustness and reliability of wind speed estimation, thereby ensuring the continuity and stability of the unit's control system.

[0083] In some implementations, this data fusion module employs Kalman filtering or weighted averaging algorithms to dynamically fuse lidar wind speed signals and nacelle anemometer data. LiDAR provides non-contact wind speed measurement of the forward wind field, offering high spatial resolution and foresight, while the nacelle anemometer measures wind speed from behind the rotor, providing high time response speed and stability. During communication interruptions, the main control system may have switched to the nacelle anemometer as a substitute data source. Once lidar communication is restored, a smooth transition between the two wind speed data streams is necessary using a fusion algorithm to prevent control command instability due to sudden data changes.

[0084] The specific operation involves the following steps: First, the main control system reads the current wind speed values ​​from both the lidar and the nacelle anemometer. Second, based on preset weighting coefficients (e.g., lidar weight is 0.7, nacelle anemometer weight is 0.3), the two wind speed signals are weighted and calculated to obtain a fused wind speed estimate. The weighting coefficients can be dynamically adjusted based on sensor accuracy, environmental interference levels, and consistency with historical data to adapt to measurement requirements under different operating conditions.

[0085] The sampling frequency of the fusion algorithm is typically set to 10Hz to 20Hz to match the real-time requirements of the wind turbine control system. The update delay of the lidar wind speed signal is generally no more than 50ms, while the response time of the nacelle anemometer is approximately 100ms to 200ms. The fusion module also needs to set a data consistency threshold (e.g., the wind speed difference does not exceed 2m / s) to determine whether the two data streams meet the fusion conditions and prevent abnormal data from interfering with the control logic.

[0086] Furthermore, in some implementations, dynamically adjusting the yaw control parameters based on the fused wind speed data to achieve smooth switching of control modes is one of the key control strategies in this invention for improving the operational stability and power generation efficiency of wind turbines. This step is based on the fusion of multi-source data from lidar and traditional wind speed sensors (such as wind vanes and ultrasonic anemometers). By monitoring the changing trend of wind speed signals in real time, it determines whether lidar communication is abnormal and dynamically adjusts the yaw control parameters accordingly, thereby achieving a seamless switch from feedforward control mode to traditional feedback control mode.

[0087] Specifically, this step first involves setting up a data fusion module in the main control system to perform weighted fusion processing on the lidar wind speed signal and traditional wind speed sensor data, thereby improving the robustness of wind speed estimation. In each sampling period k, the system acquires the current lidar wind speed signal. and the previous cycle Compare them. If the difference is... If the wind speed is less than a set threshold (e.g., 0.1 m / s), it is assumed that the wind speed signal has not changed, which may be due to a communication interruption. The system will then use an abnormal duration counter. Increasing, when If the preset time threshold (e.g., 10 seconds) is exceeded, the lidar communication is determined to be abnormal, triggering the control mode switching logic.

[0088] An embodiment of the present invention provides an automatic recovery control method for communication interruption of a wind turbine lidar system. Based on the automatic recovery of communication interruption, this method further improves the accuracy of wind speed measurement and the adaptability of yaw response through multi-sensor data fusion and dynamic yaw parameter adjustment, optimizes the load distribution of the unit, and improves the wind energy capture efficiency.

[0089] Example 2 The following description, in conjunction with the accompanying drawings, details an embodiment of the present invention regarding an automatic recovery control method and apparatus for wind turbine lidar communication interruption.

[0090] This invention proposes an automatic recovery control method for lidar communication interruptions in wind turbine generators, overcoming the shortcomings of existing technologies and providing a rapid control method for automatic lidar communication recovery, effectively solving the frequent communication interruption problem in wind farms. Its technical advantages are reflected in three main aspects: high reliability, low-cost maintenance, and strong compatibility. It is one of the key technologies for improving the intelligence level and economy of wind turbine generators, and can enhance the operational safety and reliability of the generator under extreme wind conditions through multi-information fusion-based sensor redundancy diagnosis and fault-tolerant control.

[0091] An improved wind turbine control system mainly includes: Data acquisition unit: including forward-looking scanning lidar.

[0092] Signal processing unit: Used to receive and process raw data from all data acquisition units. Specifically, this includes converting the communication data uploaded by the lidar into wind speed signals that match the distance and height of the turbine's rotor. Simultaneously, it stores the lidar wind speed signal from the previous sampling period. .

[0093] Control and Protection Unit: Based on the wind speed signal obtained from the signal processing unit, it processes the radar wind speed signal for the current period in the program. LiDAR wind speed signal compared to the previous sampling period The comparison test checks whether there is a continuous change. If the data returned by the lidar remains unchanged for a period of time, it is determined that the lidar communication is abnormal, and the main control sends an activation signal to the lidar, and the lidar restarts. If the lidar wind speed signal changes in real time, it is determined that the lidar communication is normal.

[0094] LiDAR communication interruption handling method: When the unit blades block the laser beam for an extended period, or during weather conditions such as fog, dust storms, or snow, the LiDAR may experience communication interruption and signal loss. To ensure normal LiDAR data transmission, control logic for restoring LiDAR communication needs to be added to the main controller. This involves adding detection and comparison variables. and duration If the duration exceeds the set threshold If the data returned by the lidar remains unchanged, it is determined that the lidar communication is abnormal. The main control sends a Re_Innit activation signal to the lidar, and at the same time... Reset, the radar restarts.

[0095] The specific plan is as follows: 1. A method for monitoring communication anomalies in wind turbine lidar is proposed, such as... Figure 2 As shown.

[0096] Add the wind speed from the previous moment of the lidar to the main control program. As a comparison variable, we detect whether the radar wind speed signal is abnormal, and we also increase the duration of abnormal lidar wind speed signal. If the wind speed at the previous moment was... Current lidar wind speed If the values ​​are different, the lidar signal is considered normal; if the wind speed at the previous moment is different... Current lidar wind speed If the signals are the same, the lidar signal is considered abnormal, and the duration of the abnormal lidar wind speed signal is determined. Start counting.

[0097]

[0098] Where k is the current period value of the sampled signal, and k-1 is the previous period value of the sampled signal.

[0099] If the duration exceeds the set threshold If the data returned by the lidar remains unchanged, it indicates a lidar communication error, and the lidar communication status flag will be displayed. Set; otherwise, it is assumed that the lidar is functioning normally and is working properly. LiDAR communication status flag. Reset.

[0100]

[0101] 2. A control method for restoring abnormal communication of lidar in wind turbine units is proposed, such as... Figure 3 As shown.

[0102] When the unit is not shut down, if the main control system detects an abnormal communication status of the lidar, it will automatically resend an activation and restart command signal to the lidar, and simultaneously update the program. After resetting, the lidar restarted, and the communication data signal returned to normal.

[0103] Alternative solutions, such as Figure 4 As shown: The control method can be adapted to the actual application and operating environment of the unit by canceling the lidar fault flag and triggering a set threshold when the lidar data remains unchanged for an extended period of time. At that time, the unit malfunctioned and shut down.

[0104] Example 3 To achieve the above embodiments, such as Figure 5 As shown, this embodiment also provides an automatic recovery control device 10 for communication interruption of wind turbine lidar. The device 10 includes a signal acquisition and difference calculation module 100, a signal anomaly judgment and counting start module 200, an anomaly duration detection and triggering module 300, a hardware reset control and flag reset module 400, and a data fusion and dynamic adjustment module 500.

[0105] The signal acquisition and difference calculation module 100 is used to acquire the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two. The signal anomaly judgment and counting start module 200 is used to judge whether the lidar signal is abnormal based on the numerical change of the difference. If the difference is zero, the anomaly duration counter is started. The abnormal duration detection and triggering module 300 is used to detect whether the abnormal duration exceeds a preset threshold. If it does, the communication abnormal status flag is set and the main control system is triggered. The hardware reset control and flag reset module 400 is used by the main control system to send an activation signal to the lidar to trigger a hardware reset, and at the same time reset the communication abnormal status flag. The data fusion and dynamic adjustment module 500 is used to perform weighted fusion of lidar wind speed signals and cabin anemometer data after communication is restored; and dynamically adjust yaw control parameters based on the fused wind speed data to achieve smooth switching of control modes.

[0106] Furthermore, the signal acquisition and difference calculation module 100 described above is also used for: The wind speed signal of the current sampling period is time-aligned with that of the previous sampling period using the timestamp synchronization module. A ring buffer structure is used to store historical wind speed signals to ensure the real-time performance and integrity of data storage within continuous sampling periods.

[0107] Furthermore, the aforementioned signal anomaly judgment and counting start module 200 is also used for: When the absolute value of the difference is less than the preset fluctuation threshold and the duration exceeds the first sub-threshold, it is determined to be an abnormal low-frequency signal and the counter is started. The high-frequency noise component in the signal is detected by the electromagnetic noise feature recognition module. If the intensity of the high-frequency noise exceeds the second sub-threshold, the counter operation is paused.

[0108] Furthermore, the aforementioned abnormal duration detection and triggering module 300 is also used for: The threshold is dynamically adjusted based on real-time weather data. When smog, sandstorm, or snow is detected, the threshold is increased by a preset percentage. Anomaly detection is performed by combining the combined changes in wind speed and wind direction signals. If both the wind speed and wind direction signals are abnormal, the communication recovery process is triggered first.

[0109] Furthermore, the aforementioned hardware reset control and flag reset module 400 is also used for: A phased reset strategy is adopted. First, a soft reset command is sent and a preset response time is waited. If no acknowledgment signal is received, a hard reset command is sent. The system synchronously sends status detection requests to the backup lidar via redundant communication channels to ensure that the main control system maintains wind condition data input during recovery.

[0110] This invention discloses an automatic recovery control device for LiDAR communication interruption in wind turbine units, which effectively solves the problem of frequent communication interruptions in wind farms. It can improve the operational safety and reliability of the unit under extreme wind conditions by using multi-information fusion-based sensor redundancy diagnosis and fault-tolerant control.

[0111] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0112] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for automatic recovery control of communication interruption of lidar in wind turbine generators, characterized in that, include: S1, acquire the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two; S2, determine whether the lidar signal is abnormal based on the change in the difference value; if the difference value is zero, start the abnormal duration counter. S3, detect whether the abnormal duration exceeds the preset threshold. If it does, set the communication abnormal status flag and trigger the main control system. S4, the main control system sends an activation signal to the lidar to trigger a hardware reset, and at the same time resets the communication abnormal status flag; S5. After communication is restored, the main control system initiates multi-sensor data fusion, which weights and fuses the lidar wind speed signal with the cabin anemometer data; based on the fused wind speed data, the yaw control parameters are dynamically adjusted to achieve smooth switching of control modes.

2. The method as described in claim 1, characterized in that, S1 includes: S11, the wind speed signal of the current sampling period is time-aligned with the wind speed signal of the previous sampling period through the timestamp synchronization module; S12 uses a ring buffer structure to store historical wind speed signals, ensuring the real-time performance and integrity of data storage during continuous sampling periods.

3. The method as described in claim 1, characterized in that, S2 includes: S21, when the absolute value of the difference is less than the preset fluctuation threshold and the duration exceeds the first sub-threshold, it is determined that the low frequency signal is abnormal and the counter is started; S22, the high-frequency noise component in the signal is detected by the electromagnetic noise feature recognition module. If the high-frequency noise intensity exceeds the second sub-threshold, the counter operation is paused.

4. The method as described in claim 1, characterized in that, The S3 further includes: S31, dynamically adjust the threshold based on real-time weather data, and increase the threshold by a preset ratio when fog, dust, or snow is detected; S32 combines the joint change characteristics of wind speed and wind direction signals to determine anomalies. If the wind speed signal is abnormal and the wind direction signal is also abnormal, the communication recovery process will be triggered first.

5. The method as described in claim 1, characterized in that, The S4 includes: S41 adopts a phased reset strategy. First, a soft reset command is sent and a preset response time is waited. If no acknowledgment signal is received, a hard reset command is sent. S42 synchronously sends status detection requests to the backup lidar through redundant communication channels to ensure that the main control system maintains wind condition data input during recovery.

6. An automatic recovery control device for communication interruption of a wind turbine lidar system, characterized in that, include: The signal acquisition and difference calculation module is used to acquire the lidar wind speed signal of the current sampling period and the wind speed signal of the previous sampling period, and calculate the difference between the two. The signal anomaly judgment and counting start module is used to judge whether the lidar signal is abnormal based on the change of the difference value. If the difference value is zero, the anomaly duration counter is started. The abnormal duration detection and triggering module is used to detect whether the abnormal duration exceeds a preset threshold. If it does, the communication abnormal status flag is set and the main control system is triggered. The hardware reset control and flag reset module is used by the main control system to send an activation signal to the lidar to trigger a hardware reset, and at the same time reset the communication abnormal status flag. The data fusion and dynamic adjustment module is used to perform weighted fusion of lidar wind speed signals and cabin anemometer data after communication is restored; and dynamically adjust yaw control parameters based on the fused wind speed data to achieve smooth switching of control modes.

7. The apparatus as claimed in claim 6, characterized in that, The signal acquisition and difference calculation module is also used for: The wind speed signal of the current sampling period is time-aligned with that of the previous sampling period using the timestamp synchronization module. A ring buffer structure is used to store historical wind speed signals to ensure the real-time performance and integrity of data storage during continuous sampling periods.

8. The apparatus as claimed in claim 6, characterized in that, The signal anomaly detection and counting start module is also used for: When the absolute value of the difference is less than the preset fluctuation threshold and the duration exceeds the first sub-threshold, it is determined to be an abnormal low-frequency signal and the counter is started. The high-frequency noise component in the signal is detected by the electromagnetic noise feature recognition module. If the intensity of the high-frequency noise exceeds the second sub-threshold, the counter operation is paused.

9. The apparatus as claimed in claim 6, characterized in that, The abnormal duration detection and triggering module is also used for: The threshold is dynamically adjusted based on real-time weather data. When smog, sandstorm, or snow is detected, the threshold is increased by a preset percentage. Anomaly detection is performed by combining the combined changes in wind speed and wind direction signals. If both the wind speed and wind direction signals are abnormal, the communication recovery process is triggered first.

10. The apparatus as claimed in claim 6, characterized in that, The hardware reset control and flag reset module is also used for: A phased reset strategy is adopted. First, a soft reset command is sent and a preset response time is waited. If no acknowledgment signal is received, a hard reset command is sent. The system synchronously sends status detection requests to the backup lidar via redundant communication channels to ensure that the main control system maintains wind condition data input during recovery.

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