Communication self-inspection and emergency shutdown method for offshore wind turbine generator

Abstract

A communication self-inspection and emergency shutdown method for an offshore wind turbine generator comprises the following steps: acquiring heartbeat signal data between a fan main control unit and a looped network, analyzing frequency and intensity, and judging whether a signal is normal or not; if the signal intensity is low, the interference detection module is started to identify external interference or equipment faults, and a communication interruption timer is started accordingly. And once the communication interruption exceeds the preset duration, a fault is judged and a confirmation mechanism is triggered, and after the fault is confirmed, the main control unit sends an emergency shutdown instruction. And after shutdown, acquiring running state data of the fan to judge whether the fan is completely stopped, recording shutdown time and state change, generating a fault report covering fault types, interruption, shutdown time and fan state change, and uploading the fault report to a looped network monitoring system. According to the method, accurate self-inspection of the communication state of the offshore wind turbine generator is achieved, faults can be found in time, emergency shutdown measures can be taken, detailed data are provided for operation and maintenance personnel, and safe and stable operation of the wind turbine generator is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of safe operation of offshore wind turbines, and particularly relates to a method for communication self-check and emergency shutdown of offshore wind turbines. Background Art

[0002] In the operation and maintenance of offshore wind power, the communication stability between the wind turbine and the ring network is the core issue to ensure the safe operation of the wind turbine. Due to the complex and changeable offshore environment, communication interruptions occur frequently. Especially in extreme weather conditions, the failure of the communication link may cause the wind turbine to get out of control, leading to equipment damage or safety accidents. In the prior art, the detection of the communication status between the wind turbine and the ring network relies on manual intervention or regular inspections, and it is impossible to identify and take protective measures in time when the communication is interrupted. In addition, when the communication is interrupted, the wind turbine cannot automatically perform the shutdown operation and can only wait for manual intervention. This lag is particularly prominent in the high-risk offshore environment. Specifically, the main control unit of the wind turbine maintains communication with the ring network through a heartbeat signal. However, in some cases, such as signal interference, equipment failure, or bad weather, the communication link may be interrupted. The existing system lacks an automatic judgment mechanism and cannot actively confirm the fault status after the communication interruption reaches a certain time. Even when the communication is interrupted, the wind turbine will still continue to operate, which not only increases the equipment load but also may cause mechanical damage or abnormal power generation when the wind turbine is out of control. At the same time, the timeliness of manual inspections or dispatching ships for shutdown operations is poor. Especially in extreme conditions such as strong winds and big waves, it is impossible to reach the scene in time, further exacerbating the safety hazards.

[0003] Therefore, how to automatically judge the fault status and trigger the emergency shutdown operation when the communication between the wind turbine and the ring network is interrupted has become an urgent technical problem to be solved in the operation and maintenance of offshore wind power. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for communication self-check and emergency shutdown of offshore wind turbines. Through multi-dimensional analysis of the heartbeat signal data and a progressive detection mechanism for each link, the root cause of communication anomalies can be quickly and accurately located. Whether it is external interference or equipment failure, it can be accurately identified, providing a key basis for subsequent efficient maintenance, and reducing the troubleshooting time and cost.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: A method for communication self-check and emergency shutdown of offshore wind turbines, the steps are as follows: S1. Obtain the heartbeat signal data between the main control unit of the wind turbine and the ring network, analyze the frequency and intensity of signal transmission, and judge whether the signal is within the normal range; S1.1 Obtain the heartbeat data stream between the main control unit of the wind turbine and the ring network end, and extract the values of signal frequency and signal strength from it; S1.2 Perform a fast Fourier transform on the analog signal frequency and signal strength through analysis to obtain the spectral characteristics of the frequency values and strength values; S1.3 For the frequency values and strength values, perform numerical comparison within the pre-established normal threshold range respectively to determine the fluctuation conditions of the analog signal frequency and signal strength; S1.4 If the fluctuation amplitudes of the analog signal frequency and signal strength exceed the preset judgment criteria, it is determined that the current heartbeat signal is abnormal; S1.5 Use an adaptive filtering algorithm to perform noise reduction processing on the abnormal signal frequency and abnormal signal strength to obtain the corrected frequency values and strength values; S1.6 Perform a secondary comparison of the corrected frequency values and strength values with the normal threshold to determine whether the analog signal frequency and signal strength have returned to the normal range; S1.7 Generate a real-time monitoring report of the heartbeat signal between the fan main control unit and the ring network end based on the final judgment results of the analog signal frequency and signal strength.

[0006] S2. If the heartbeat signal strength is lower than the preset threshold, start the signal interference detection module, analyze the signal spectral characteristics, and determine whether there is external interference or equipment failure; S2.1 Obtain the heartbeat signal strength and compare it with the preset threshold; S2.2 If the signal strength is lower than the preset value, start the signal interference detection module; S2.3 Obtain the signal spectral characteristics through a signal processing algorithm; S2.4 Use a spectral analysis method to analyze the signal characteristics; S2.5 Determine whether there is external interference according to the analysis results; S2.6 If there is external interference, determine the interference type and source; S2.7 If there is no external interference, determine whether there is equipment failure.

[0007] S3. According to the signal interference detection results, if external interference or equipment failure is detected, start the communication interruption timer and record the interruption duration; S3.1 Use a preset detection algorithm to obtain the signal characteristics and analyze whether there is interference or failure for the signal characteristics; S3.2 If the detection result is external interference or equipment failure, trigger the communication interruption timer and record the interruption time length; S3.3 Determine the interruption type according to the interruption time length and generate an interruption event record; S3.4 Use a time series analysis method to cluster the interruption events and identify the interruption patterns; S3.5 Compare the interruption patterns with the preset fault library through a pattern matching algorithm to determine the fault category; S3.6 Adjust the detection algorithm parameters according to the fault category to optimize the interference recognition accuracy.

[0008] S4. If the communication interruption duration exceeds the preset time threshold, it is determined as a fault state, and the fault state confirmation mechanism is triggered; S4.1 Obtain the communication status information and analyze whether the current communication is in an interrupted state; S4.2 If the communication interruption state persists, calculate the interruption time length; S4.3 Compare the calculated interruption time length with the preset threshold; S4.4 If the interruption time length exceeds the preset threshold, it is determined as a fault state; S4.5 Trigger the fault state confirmation mechanism according to the fault state determination result; S4.6 Analyze the fault state-related information using a machine learning algorithm to determine the fault type; S4.7 Generate fault handling suggestions according to the fault type and the result of the confirmation mechanism.

[0009] S5. According to the fault state confirmation result, if the communication interruption is confirmed as a fault state, send an emergency stop instruction through the fan main control unit to stop the fan operation; S5.1 For the fan control system, obtain the operation status data of the communication module and judge whether the communication is interrupted; S5.2 If the communication is interrupted, it is identified as a fault state and the fault handling mechanism is triggered; S5.3 Generate an emergency stop instruction by the fan main control unit according to the fault state; S5.4 Send the emergency stop instruction to the fan execution unit through the main control unit; S5.5 The fan execution unit receives the stop instruction and executes the stop operation; S5.6 Monitor the fan operation status and confirm that the fan has stopped running; S5.7 Record the shutdown event and fault information and store it in the system log.

[0010] S6. After the emergency stop instruction is executed, obtain the fan operation status data, judge whether the fan has completely stopped, and record the shutdown time and the change of operation status; S6.1 Obtain the real-time status data corresponding to the fan number and extract the rotation speed value, power value, voltage value, current value, temperature value and vibration value; S6.2 Compare the status data with the preset stop threshold. If the rotation speed value is less than the preset stop rotation speed threshold, it is determined that the fan has stopped; S6.3 Generate change values based on the status data, including the rotational speed change amount, power change amount, voltage change amount, current change amount, temperature change amount, and vibration change amount; S6.4 Extract the instruction execution time point, and combine it with the status data change values to generate time series data for the fan shutdown process; S6.5 Use the time series data to calculate the change rates of each status parameter during the fan shutdown process, and generate a shutdown process feature vector; S6.6 Based on the feature vector, use a classification algorithm to determine whether the fan shutdown process is normal. If the feature vector is within the preset normal range, it is determined to be a normal shutdown; S6.7 Store the shutdown time, shutdown status, shutdown process feature vector, and shutdown determination result in the fan operation status database.

[0011] S7. Generate a fault report based on the shutdown time and operation status data, including the fault type, interruption duration, shutdown time, and fan status change information; S7.1 By obtaining the fan operation status data, extract the fan state, status value, operation value, and time point information; S7.2 For the extracted status value, if it is lower than the preset operation value, it is determined to be an abnormal value, and the time point is recorded; S7.3 Based on the abnormal value and the corresponding time point, calculate the interruption time and shutdown time to determine the interruption duration; S7.4 By analyzing the characteristics of the abnormal value and combining the fan state change value, classify the fault category; S7.5 Use a clustering algorithm to aggregate the fault categories to distinguish different fault types; S7.6 Based on the interruption time, shutdown time, and fault category information, generate the report content; S7.7 Based on the report content, output a fault report including the fault type, interruption duration, shutdown time, and fan status change information.

[0012] S8. Upload the fault report to the ring network monitoring system for maintenance personnel to remotely view and analyze, providing data support for subsequent fault handling; S8.1 Obtain the fault data, match the fault data with the preset fault model. If the match is successful, determine the fault type; S8.2 Based on the fault type, extract the associated monitoring data; S8.3 Integrate the fault data and the monitoring data to generate a fault report; S8.4 Through the preset interface, upload the fault report to the ring network monitoring system; S8.5 Use a data parsing algorithm to parse the fault report; S8.6 Based on the parsing results, use a fault analysis algorithm to generate analysis results; S8.7 Determine the subsequent processing plan according to the analysis results.

[0013] A control system for communication self-check and emergency shutdown of an offshore wind turbine, characterized in that it adopts an offshore wind turbine communication self-check and emergency shutdown method according to any one of claims 1-9. The system includes: A communication data monitoring module, used to obtain the heartbeat signal data between the wind turbine main control unit and the ring network, analyze the frequency and intensity of signal transmission, and judge whether the signal is within the normal range; A signal interference detection module, used to analyze the signal spectrum characteristics when the heartbeat signal strength is lower than the preset threshold, and judge whether there is external interference or equipment failure; A communication interruption timing module, used to start and record the duration of communication interruption according to the signal interference detection result when external interference or equipment failure is detected; A fault status determination module, used to determine the fault status and trigger the fault status confirmation mechanism when the communication interruption duration exceeds the preset time threshold; An emergency shutdown control module, used to send an emergency shutdown instruction through the wind turbine main control unit to stop the operation of the wind turbine when the communication interruption is confirmed as a fault status according to the fault status confirmation result; A shutdown status monitoring module, used to obtain the operation status data of the wind turbine after the execution of the emergency shutdown instruction, judge whether the wind turbine has completely stopped, and record the shutdown time and the change of operation status; A fault report generation module, used to generate a fault report including fault type, interruption duration, shutdown time and wind turbine status change information according to the shutdown time and operation status data; A report upload module, used to upload the fault report to the ring network monitoring system for remote viewing and analysis by operation and maintenance personnel, providing data support for subsequent fault handling.

[0014] The present invention can achieve the following beneficial effects: 1. Through multi-dimensional analysis of the heartbeat signal data and the progressive detection mechanism of each link, the root cause of communication anomalies can be quickly and accurately located. Whether it is external interference or equipment failure, it can be accurately identified, providing a key basis for subsequent efficient maintenance, and reducing the troubleshooting time and cost.

[0015] 2. When a communication fault is detected and the shutdown condition is reached, an emergency shutdown instruction is sent in time to quickly stop the operation of the wind turbine, effectively avoiding the out-of-control operation of the wind turbine caused by communication faults, reducing the risk of equipment damage, ensuring the safety of the offshore wind turbine itself and surrounding facilities, and maintaining the stability of the offshore wind power operation environment.

[0016] 3. After the shutdown, collect the detailed operating status data of the wind turbine to generate time - series data and feature vectors covering multi - parameter changes. This can not only clearly determine whether the wind turbine has completely stopped, but also evaluate whether the stopping process is normal, comprehensively grasp the operating status of the wind turbine, and help the operation and maintenance personnel fully understand the equipment status.

[0017] 4. The generated fault report contains rich information. After being uploaded to the ring network monitoring system, the operation and maintenance personnel can remotely view and analyze it, and accordingly formulate targeted maintenance strategies, optimize the operation and maintenance process, improve the operation and maintenance efficiency of offshore wind turbines, extend the service life of the equipment, and enhance the overall operation benefit of offshore wind power. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the drawings and embodiments: Figure 1 This is the flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The preferred solution is as Figure 1 shown. A communication self - checking and emergency shutdown method for an offshore wind turbine involves a wind turbine main control unit (PLC or DCS control system), a communication detection module, a backup communication channel (such as 5G, satellite communication or LoRa), and a safety chain control module. The steps are as follows: Step S1: The wind turbine main control unit controls the communication detection module to regularly send detection information to the ring network through a heartbeat signal and wait for a response. At the same time, obtain the heartbeat signal data between the wind turbine main control unit and the ring network, analyze the frequency and intensity of the signal transmission, and determine whether the signal is within the normal range.

[0020] Obtain the heartbeat data stream between the wind turbine main control unit and the ring network end, and extract the numerical values of the signal frequency and signal strength from it. Perform a fast Fourier transform on the signal frequency and signal strength through an analysis model to obtain the spectral characteristics of the frequency value and intensity value.

[0021] For the frequency value and intensity value, respectively conduct numerical comparisons within the pre - established normal threshold range to determine the fluctuation conditions of the signal frequency and signal strength. If the fluctuation amplitude of the signal frequency and signal strength exceeds the preset judgment condition, it is determined that the current heartbeat signal is abnormal.

[0022] Adopt an adaptive filtering algorithm to perform noise reduction processing on the abnormal signal frequency and abnormal signal strength to obtain the corrected frequency value and intensity value. Perform a secondary comparison of the corrected frequency value and intensity value with the normal threshold to determine whether the signal frequency and signal strength have returned to the normal range.

[0023] Based on the final judgment results of the signal frequency and signal strength, generate a real - time monitoring report of the heartbeat signal between the wind turbine main control unit and the ring network end.

[0024] In specific operations, heartbeat signal data is obtained from between the main control unit of the fan and the ring network through a data acquisition system. The sampling frequency is set to 1000 times per second to ensure the real-time and accuracy of the data. The collected heartbeat signal data is preprocessed, and a low-pass filter is used to remove high-frequency noise. The cut-off frequency of the filter is set to 50 Hz. Fourier transform is performed on the filtered signal to analyze its spectral characteristics. It is found that the main frequency of the signal is concentrated at 1 Hz, which is within the frequency range of normal heartbeat signals. Further calculate the intensity of the signal, using the root mean square value (RMS) as the intensity index. The measured RMS value of the signal is 5 volts, which is within the normal range compared with the standard range of 3 to 7 volts. To verify the stability of the signal, a sliding window analysis is performed on 10 minutes of continuous data. The window size is 1 minute, and the step size is 30 seconds. Calculate the frequency and intensity of the signal within each window. The results show that the signal frequencies within all windows are between 9 and 1 Hz, and the intensity is between 45 and 55 volts, with a fluctuation range of less than 5%, indicating stable signal transmission. Finally, compare the analysis results with historical data and find that the current signal is basically consistent with the average signal characteristics within the past month, without abnormal fluctuations. It is judged that the heartbeat signal between the main control unit of the fan and the ring network is in a normal state.

[0025] Step S2: If the intensity of the heartbeat signal is lower than the preset threshold, start the signal interference detection module, analyze the spectral characteristics of the signal, and determine whether there is external interference or equipment failure.

[0026] Obtain the intensity of the heartbeat signal and compare it with the preset threshold. If the signal intensity is lower than the preset value, start the signal interference detection module.

[0027] Obtain the spectral characteristics of the signal through a signal processing algorithm, and analyze the signal characteristics using a spectral analysis method.

[0028] Judge whether there is external interference according to the analysis results. If there is external interference, determine the type and source of the interference; if there is no external interference, judge whether there is equipment failure.

[0029] Specifically, when the heart rate signal strength is lower than a preset threshold (such as 50 μV), the system automatically activates the signal interference detection module. First, the fast Fourier transform (FFT) algorithm is used to perform spectral analysis on the signal, converting the time-domain signal into a frequency-domain signal. Then, the system detects whether there are abnormal peaks in the spectrum, such as significant energy concentration near 50 Hz or 60 Hz, which may indicate power frequency interference. If an abnormal peak is detected, the system further calculates the signal-to-noise ratio (SNR) of the signal. If the SNR is lower than 20 dB, it is determined that there is external interference. In addition, the system also analyzes the baseline drift of the signal. If the baseline drift exceeds ±5 mV, it may indicate a device failure. By comprehensively analyzing these results, the system can accurately determine the reason for the decrease in heart rate signal strength and take corresponding measures, such as activating a filter to eliminate interference or issuing a device maintenance alarm, to ensure the accuracy and reliability of the signal.

[0030] Step S3: According to the signal interference detection result, if external interference or device failure is detected, start the communication interruption timer and record the interruption duration.

[0031] Obtain signal characteristics using a preset detection algorithm and analyze whether there is interference or failure based on the signal characteristics.

[0032] If the detection result is external interference or device failure, trigger the communication interruption timer and record the interruption duration.

[0033] Judge the interruption type based on the interruption duration and generate an interruption event record.

[0034] Use time series analysis methods to cluster the interruption events and identify the interruption patterns.

[0035] Compare the interruption pattern with a preset fault library through a pattern matching algorithm to determine the fault category.

[0036] Adjust the detection algorithm parameters according to the fault category to optimize the interference recognition accuracy.

[0037] During the signal interference detection process, when external interference or equipment failure is detected, the system will immediately start the communication interruption timer to record the duration of the interruption. For example, when the detected signal strength is lower than the preset threshold of 80 dBm, the system will determine it as external interference or equipment failure and trigger the timer to start timing. The timer uses a high-precision timestamp algorithm to ensure the accuracy of the recorded time. During the interruption, the system will continuously monitor the signal status, sample every 100 milliseconds, and store the sampled data in memory. If the signal strength recovers above 70 dBm, the timer stops and calculates the interruption duration. Assuming the interruption duration is 13 seconds, the system will record this data in the log file and generate an interruption report, which includes the start time, end time, duration of the interruption, and the stability analysis after the signal recovery. In addition, the system will also analyze the signal fluctuations during the interruption, using the Fast Fourier Transform (FFT) algorithm to convert the time-domain signal into a frequency-domain signal to identify the frequency characteristics of the interference source. For example, if the analysis result shows that the interference source is concentrated in the 4 GHz frequency band, the system will further infer the possible types of interference devices, such as wireless routers or microwave ovens. Finally, based on the analysis results, the system will automatically adjust the communication parameters, such as switching the frequency band or increasing the signal transmission power, to reduce the risk of future interruptions.

[0038] Step S4: If the communication interruption duration exceeds the preset time threshold, it is determined as a fault state, and the fault state confirmation mechanism is triggered. At the same time, a multi-level determination mechanism is introduced: If no ring network feedback signal is received within 10 minutes, the main control unit of the fan attempts to establish a connection through the backup communication path (such as 5G, satellite).

[0039] Backup communication channel confirmation: If the backup communication channel is available, retry the communication. If the communication still cannot be restored, further intelligent analysis is carried out, including detecting the operating parameters of the fan, such as vibration, rotation speed, power generation, etc., to avoid misjudgment.

[0040] If all detections indicate that the communication is lost and the operating state of the fan is abnormal, the communication fault is confirmed. After the fault is confirmed, the system will compare the states of the surrounding fans to determine whether it is a local fault or a global communication problem, and combine historical data and environmental parameters to optimize the accuracy of the shutdown determination.

[0041] Obtain the communication status information and analyze whether the current communication is in an interrupted state. If the communication interruption state persists, calculate the interruption time length. Compare the calculated interruption time length with the preset threshold. If the interruption time length exceeds the preset threshold, it is determined as a fault state. According to the fault state determination result, trigger the fault state confirmation mechanism. Use machine learning algorithms to analyze the information related to the fault state to determine the fault type. Generate fault handling suggestions based on the fault type and the result of the confirmation mechanism.

[0042] During the communication interruption detection process, the system will monitor the signal status of the communication link in real time and record the start time of the interruption. If the interruption duration exceeds the preset 30-second threshold, the system will automatically determine it as a fault state. At this time, the system will start the fault state confirmation mechanism. First, it will try to restore the communication through the retry mechanism. The number of retries is set to 3 times, with a 5-second interval between each retry. If the retry fails, the system will call the fault analysis algorithm to locate the fault by combining historical data and the current state. For example, using a fault diagnosis model based on a Bayesian network to calculate the fault probability of each node. If the fault probability of a certain node exceeds 95%, it is determined that the node is faulty. At the same time, the system will record the fault information in the log and notify relevant personnel via text message or email. To ensure the timeliness of fault handling, the system also sets up an automatic recovery mechanism. For example, after detecting a fault, it will automatically switch to the standby communication link and continuously monitor the status of the standby link to ensure the continuity of communication. Through these measures, the system can efficiently detect and handle communication interruption faults, ensuring the stability and reliability of communication.

[0043] Step S5: According to the fault state confirmation result, if the communication interruption is confirmed as a fault state, send an emergency shutdown instruction through the fan main control unit to stop the fan operation. The fan main control immediately performs safety chain operations, including: Gradually decelerate to avoid mechanical shock to the fan.

[0044] Adjust the pitch angle to 90° to minimize the power output of the fan.

[0045] Perform multi-level safety verification to ensure that the fan main control unit is fault-free and try to broadcast the shutdown signal to surrounding fans to form a cluster linkage mechanism.

[0046] For the fan control system, obtain the operating status data of the communication module to determine whether the communication is interrupted. If the communication is interrupted, it is identified as a fault state and the fault handling mechanism is triggered. According to the fault state, the fan main control unit generates an emergency shutdown instruction. The emergency shutdown instruction is sent to the fan execution unit through the main control unit. The fan execution unit receives the shutdown instruction and performs the shutdown operation. Monitor the operating status of the fan to confirm that the fan has stopped running. Record the shutdown event and fault information and store it in the system log.

[0047] When the communication interruption is confirmed as a fault state, the system first detects the loss of communication signal through the real-time monitoring module. The monitoring frequency is once per second. If the signal is not received continuously for 10 times, it is determined as a communication interruption. The system then starts the fault analysis algorithm, calculates the duration of the communication interruption, and compares it with a preset threshold (such as 30 seconds). If it exceeds the threshold, the fault confirmation mechanism is triggered. At this time, the system sends an emergency shutdown instruction through the fan main control unit. The instruction contains the fan number, shutdown timestamp, and fault type code to ensure the uniqueness and traceability of the instruction. After receiving the instruction, the main control unit immediately executes the shutdown operation. During the shutdown process, the system monitors the fan speed in real time. When the speed drops to the safe range (such as below 10 revolutions per minute), it is confirmed that the fan has stopped running completely. At the same time, the system records the shutdown event, including the fault occurrence time, duration, shutdown instruction sending time, and fan status changes, for subsequent fault analysis and maintenance. To ensure the reliability and safety of the system, after sending the shutdown instruction, the system also starts the standby communication channel and attempts to restore communication with the fan. If communication cannot be restored within 60 seconds, the shutdown state is maintained, and a remote alarm signal is sent to notify the operation and maintenance personnel for further processing.

[0048] Step S6: After the emergency shutdown instruction is executed, obtain the fan operation status data, determine whether the fan has stopped completely, and record the shutdown time and operation status changes.

[0049] Obtain the real-time status data corresponding to the fan number, and extract the rotation speed value, power value, voltage value, current value, temperature value, and vibration value.

[0050] Compare the status data with the preset stop threshold. If the rotation speed value is less than the preset stop rotation speed threshold, it is determined that the fan has stopped.

[0051] Generate change values based on the status data, including the rotation speed change amount, power change amount, voltage change amount, current change amount, temperature change amount, and vibration change amount.

[0052] Extract the instruction execution time point, and combine it with the status data change values to generate the time series data of the fan stop process.

[0053] Adopt the time series data, calculate the change rates of each status parameter during the fan stop process, and generate the stop process feature vector.

[0054] According to the feature vector, use the classification algorithm to judge whether the fan stop process is normal. If the feature vector is within the preset normal range, it is determined as a normal stop.

[0055] Store the stop time, stop status, stop process feature vector, and stop determination result in the fan operation status database.

[0056] After the emergency shutdown instruction is executed, the system collects the fan speed data in real time through sensors. Assuming the current speed is 1200 revolutions per minute, the system uses the Kalman filtering algorithm to process the data. After removing the noise interference, the true speed is obtained as 1195 revolutions per minute. Subsequently, the system samples once every 5 seconds and predicts the speed change trend through a linear regression model. When the sampled data is lower than 50 revolutions per minute for three consecutive times and the predicted slope approaches 0, it is determined that the fan has completely stopped. The system records the shutdown time as 14:35:20 on October 15, 2023, and at the same time updates the fan operation status from "running" to "stopped". During the shutdown process, the system also analyzes the status data of each key component. For example, the temperature of the gearbox drops from 85°C to 45°C, and the generator current drops from 1200A to 0A. These data are all recorded in the operation log to provide a reference for subsequent equipment maintenance.

[0057] Step S7: Generate a fault report based on the shutdown time and operation status data, including the fault type, interruption duration, shutdown time, and fan status change information.

[0058] By obtaining the fan operation status data, extract the fan state, status value, operation value, and time point information.

[0059] For the extracted status value, if it is lower than the preset operation value, it is judged as an abnormal value, and the time point is recorded.

[0060] Based on the abnormal value and the corresponding time point, calculate the interruption time and shutdown time to determine the interruption duration.

[0061] By analyzing the characteristics of the abnormal value and combining with the fan state change value, classify the fault category.

[0062] Use a clustering algorithm to aggregate the fault categories to distinguish different fault types.

[0063] Based on the interruption time, shutdown time, and fault category information, generate the report content.

[0064] According to the report content, output a fault report including the fault type, interruption duration, shutdown time, and fan status change information.

[0065] Based on the input downtime and operating status data, first through time series analysis, it is identified that the wind turbine experienced an abnormal shutdown during the period from 14:30 to 16:45 on October 15, 2023. By comparing the data under normal operating conditions, it is found that the vibration frequency of the wind turbine suddenly rose from the normal 12 Hz to 35 Hz before shutdown. Subsequently, at 14:30, the wind speed recorded by the wind speed sensor was 8 m / s, but the output power of the wind turbine dropped sharply to 0 kW, indicating that the wind turbine entered the shutdown state. Using a fault diagnosis algorithm and combining historical fault data, it is determined that the cause of this shutdown is "blade failure". This type of fault occurred 2 times in the past year, and the average repair time was 2 hours and 15 minutes. Further analysis shows that the duration of this interruption was 2 hours and 15 minutes, which is consistent with the historical average. During the shutdown, the status of the wind turbine changed from "operating" to "shutdown" and returned to the "standby" state at 16:45. Through a fault prediction model and combining current environmental parameters such as wind speed, temperature, and humidity, the probability of a blade failure occurring again within the next 24 hours is predicted to be 5%. Based on the above analysis, a fault report is generated, which details the fault type, interruption duration, shutdown time, and information on the change in the wind turbine status, providing data support for subsequent maintenance decisions.

[0066] Step S8: Upload the fault report to the ring network monitoring system for remote viewing and analysis by the operation and maintenance personnel, providing data support for subsequent fault handling.

[0067] Obtain the fault data, match the fault data with a preset fault model, and if the match is successful, determine the fault type.

[0068] Extract the associated monitoring data according to the fault type.

[0069] Integrate the fault data and the monitoring data to generate a fault report.

[0070] Upload the fault report to the ring network monitoring system through a preset interface.

[0071] Adopt a data parsing algorithm to parse the fault report.

[0072] Based on the parsing results, adopt a fault analysis algorithm to generate an analysis result.

[0073] Determine the subsequent processing plan according to the analysis result.

[0074] When a fault occurs, the system automatically collects the operation data of relevant devices, such as parameters like voltage, current, and temperature. Data is collected once per second at a preset sampling frequency and packaged into a structured format, such as JSON or XML files. The system uses the TCP / IP protocol to encrypt and transmit the data to the ring network monitoring system to ensure data security. To optimize transmission efficiency, the system employs compression algorithms like GZIP to compress the data, with a compression rate of over 70%. After the data arrives at the monitoring system, the system first performs data verification, using the CRC32 algorithm to verify data integrity and ensure no data loss or damage. Subsequently, the system stores the data in a distributed database, such as Cassandra or MongoDB, which supports high-concurrency access and massive data storage. Operation and maintenance personnel can view the fault data in real-time through the Web interface of the monitoring system. The system provides visual charts, such as line charts and bar charts, to help operation and maintenance personnel quickly analyze the fault trend. The system also has a built-in intelligent analysis module that uses machine learning algorithms like random forest or support vector machine to train historical fault data and predict possible future fault types, with an accuracy rate of over 85%. Based on the analysis results, the system automatically generates a fault report, including detailed information such as fault type, occurrence time, and impact scope, and notifies relevant operation and maintenance personnel via email or text message to ensure timely handling. All operation records and fault handling processes are automatically recorded by the system to form log files for subsequent auditing and review. The log files are marked with timestamps and unique identifiers to ensure traceability.

[0075] Step S9: After the fan stops, the system will automatically execute a timed restart strategy and attempt to restore communication every set time (such as 30 minutes).

[0076] If communication restoration is detected, the fan will gradually restart according to the established process.

[0077] If communication still cannot be restored, the fan will remain in the stopped state to avoid safety risks.

[0078] Before restarting, the fan needs to go through a three-level verification mechanism, including communication restoration confirmation, detection of the fan's own operating status, and inspection of grid fluctuations, to ensure the safe startup of the fan.

[0079] An intelligent restoration strategy is adopted, combined with the overall load situation of the wind farm, to dynamically adjust the restoration priority of the fan to ensure grid stability.

[0080] Step S10: Multi-point redundancy design: Intelligent routing mechanism: When the primary communication path fails, the system automatically detects available backup communication channels (5G, satellite, LoRa) and selects the path with the best signal.

[0081] Distributed data synchronization: Adopt a distributed data storage and transmission mechanism to ensure that the status information among multiple wind turbines can be synchronized. Even if the communication of some wind turbines is damaged, the signals of neighboring wind turbines can be used for data relay.

[0082] Intelligent communication switching strategy: Under different working conditions, the activation methods of backup communication channels are different. For example: High wind and wave environment: Give priority to using satellite communication with high bandwidth and high stability to ensure that the status of the wind turbine can be monitored.

[0083] Short-term communication interruption: Try local self-healing and restore short-distance communication through LoRa or 5G network.

[0084] Full-field disconnection situation: The system will enter the island mode, and the wind turbines will operate independently while making intermittent communication attempts to reduce power consumption.

[0085] Step S11: Shutdown response strategy: Local failure (single wind turbine or a small number of wind turbines are disconnected): Only the affected wind turbines execute shutdown to avoid affecting the power generation capacity of the entire wind farm.

[0086] Large-scale failure (communication interruption of multiple wind turbines): The system conducts a full-field linkage analysis to determine whether to enable the regional shutdown strategy to ensure the stability of the overall power grid.

[0087] Sudden extreme situations (such as tsunamis, hurricanes): After confirming the communication disconnection, the wind turbines immediately enter the emergency shutdown mode and adjust the pitch angle to face the wind with the lowest wind resistance to ensure safety.

[0088] Step S12: Emergency status record: The system will record the shutdown reason and time of the wind turbine through logs, and use blockchain technology to deposit evidence of the event to ensure the authenticity and traceability of the data.

[0089] The above embodiments are only the preferred technical solutions of the present invention and should not be regarded as limitations on the present invention. The protection scope of the present invention should be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for communication self-check and emergency shutdown of an offshore wind turbine, characterized in that It includes the following steps: Obtain the heartbeat signal data between the main control unit of the fan and the ring network, analyze the frequency and intensity of signal transmission, and determine whether the signal is within the normal range; If the heartbeat signal intensity is lower than the preset threshold, start the signal interference detection module, analyze the signal spectrum characteristics, and determine whether there is external interference or equipment failure; according to the signal interference detection result, if external interference or equipment failure is detected, start the communication interruption timer and record the interruption duration; If the communication interruption duration exceeds the preset time threshold, it is determined as a fault state, and the fault state confirmation mechanism is triggered; according to the fault state confirmation result, if the communication interruption is confirmed as a fault state, send an emergency shutdown instruction through the main control unit of the fan to stop the fan operation; after the emergency shutdown instruction is executed, obtain the fan operation state data, determine whether the fan has completely stopped, and record the shutdown time and the change of operation state; according to the shutdown time and the fan operation state data, generate a fault report, including the fault type, interruption duration, shutdown time and the change information of the fan state; upload the fault report to the ring network monitoring system for remote viewing and analysis by the operation and maintenance personnel, providing data support for subsequent fault handling.

2. The communication self-check and emergency shutdown method for an offshore wind turbine unit according to claim 1, wherein The obtaining the heartbeat signal data between the main control unit of the fan and the ring network, analyzing the frequency and intensity of signal transmission, and determining whether the signal is within the normal range includes: Obtain the heartbeat data stream between the main control unit of the fan and the ring network end, and extract the values of the signal frequency and the signal intensity from it; Perform a fast Fourier transform on the signal frequency and the signal intensity through an analysis module to obtain the spectrum characteristics of the frequency value and the intensity value; For the frequency value and the intensity value, respectively perform numerical comparison within the pre-established normal threshold range to determine the fluctuation conditions of the signal frequency and the signal intensity; If the fluctuation amplitude of the signal frequency and the signal intensity exceeds the preset judgment condition, it is determined that the current heartbeat signal is abnormal; Adopt an adaptive filtering algorithm to perform noise reduction processing on the abnormal signal frequency and the abnormal signal intensity to obtain the corrected frequency value and intensity value; Perform a secondary comparison of the corrected frequency value and intensity value with the normal threshold to determine whether the signal frequency and the signal intensity have returned to the normal range; Based on the final judgment result of the signal frequency and the signal intensity, generate a real-time monitoring report of the heartbeat signal between the main control unit of the fan and the ring network end.

3. A method for communication self-check and emergency shutdown of an offshore wind turbine unit according to claim 1, characterized in that, The if the heartbeat signal intensity is lower than the preset threshold, start the signal interference detection module, analyze the signal spectrum characteristics, and determine whether there is external interference or equipment failure includes: Obtain the heartbeat signal intensity and compare it with the preset threshold; If the signal intensity is lower than the preset value, start the signal interference detection module; Obtain the signal spectrum characteristics through a signal processing algorithm; Adopt a spectrum analysis method to analyze the signal characteristics; Judge whether there is external interference according to the analysis result; If there is external interference, determine the interference type and source; If there is no external interference, judge whether there is equipment failure.

4. A method for communication self-check and emergency shutdown of an offshore wind turbine unit according to claim 1, characterized in that, The according to the signal interference detection result, if external interference or equipment failure is detected, start the communication interruption timer and record the interruption duration includes: Adopt a preset detection algorithm to obtain the signal characteristics, and analyze whether there is interference or failure according to the signal characteristics; If the detection result is external interference or equipment failure, trigger the communication interruption timer and record the interruption duration; Judge the interruption type according to the interruption duration and generate an interruption event record; Use the time series analysis method to cluster the interruption events and identify the interruption patterns; Compare the interruption patterns with the preset fault library through the pattern matching algorithm to determine the fault category; Adjust the detection algorithm parameters according to the fault category to optimize the interference recognition accuracy.

5. A method for communication self-check and emergency shutdown of an offshore wind turbine unit according to claim 1, characterized in that, If the communication interruption duration exceeds the preset time threshold, it is determined as a fault state, and trigger the fault state confirmation mechanism, including: Obtain the communication status information and analyze whether the current communication is in an interrupted state; If the communication interruption state persists, calculate the interruption duration; Compare the calculated interruption duration with the preset threshold; If the interruption duration exceeds the preset threshold, it is determined as a fault state; Trigger the fault state confirmation mechanism according to the fault state determination result; Use the machine learning algorithm to analyze the information related to the fault state and determine the fault type; Generate fault handling suggestions according to the fault type and the result of the confirmation mechanism.

6. The communication self-checking and emergency shutdown method for an offshore wind turbine unit according to claim 1, characterized in that According to the fault state confirmation result, if the communication interruption is confirmed as a fault state, send an emergency stop instruction through the fan main control unit to stop the fan operation, including: For the fan control system, obtain the operation status data of the communication module and judge whether the communication is interrupted; If the communication is interrupted, it is identified as a fault state and trigger the fault handling mechanism; According to the fault state, the fan main control unit generates an emergency stop instruction; Send the emergency stop instruction to the fan execution unit through the main control unit; The fan execution unit receives the stop instruction and executes the stop operation; Monitor the fan operation status and confirm that the fan has stopped running; Record the stop event and fault information and store it in the system log.

7. A communication self-check and emergency shutdown method for an offshore wind turbine unit according to claim 1, characterized in that, After the emergency stop instruction is executed, obtain the fan operation status data, judge whether the fan has completely stopped, and record the stop time and the change of operation status, including: Obtain the real-time status data corresponding to the fan number and extract the rotation speed value, power value, voltage value, current value, temperature value and vibration value; Compare the status data with the preset stop threshold. If the rotation speed value is less than the preset stop rotation speed threshold, it is determined that the fan has stopped; Generate change values according to the status data, including the rotation speed change amount, power change amount, voltage change amount, current change amount, temperature change amount and vibration change amount; Extract the instruction execution time point and combine it with the status data change value to generate the time series data of the fan stop process; Use the time series data to calculate the change rates of each status parameter in the fan stop process and generate the stop process feature vector; According to the feature vector, use the classification algorithm to judge whether the fan stop process is normal. If the feature vector is within the preset normal range, it is determined as a normal stop; Store the stop time, stop state, stop process feature vector and stop determination result in the fan operation status database.

8. A method for communication self-check and emergency shutdown of an offshore wind turbine unit according to claim 1, characterized in that, Generate a fault report according to the stop time and operation status data, including the fault type, interruption duration, stop time and fan status change information, including: Extract the fan state, status value, operation value and time point information by obtaining the fan operation status data; For the extracted status value, if it is lower than the preset operating value, it is judged as an abnormal value and the time point is recorded; According to the abnormal value and the corresponding time point, calculate the interruption time and shutdown time to determine the interruption duration; By analyzing the characteristics of the abnormal value and combining with the fan state change value, classify the fault category; Use the clustering algorithm to aggregate the fault categories to distinguish different fault types; Based on the interruption time, shutdown time and fault category information, generate the report content; According to the report content, output a fault report including the fault type, interruption duration, shutdown time and fan state change information.

9. A method for communication self-check and emergency shutdown of an offshore wind turbine unit according to claim 1, characterized in that, Upload the fault report to the ring network monitoring system for remote viewing and analysis by the operation and maintenance personnel, providing data support for subsequent fault handling, including: Obtain the fault data, match the fault data with the preset fault model, and if the match is successful, determine the fault type; Extract the associated monitoring data according to the fault type; Integrate the fault data and the monitoring data to generate a fault report; Upload the fault report to the ring network monitoring system through the preset interface; Use the data parsing algorithm to parse the fault report; Based on the parsing result, use the fault analysis algorithm to generate the analysis result; Determine the subsequent processing plan according to the analysis result.

10. A control system for communication self-check and emergency shutdown of an offshore wind turbine, characterized in that: Adopt a method for communication self-check and emergency shutdown of an offshore wind turbine according to any one of claims 1-9, and the system includes: A communication data monitoring module, which is used to obtain the heartbeat signal data between the fan main control unit and the ring network, analyze the frequency and intensity of signal transmission, and judge whether the signal is within the normal range; A signal interference detection module, which is used to analyze the signal spectrum characteristics and judge whether there is external interference or equipment failure when the heartbeat signal strength is lower than the preset threshold; A communication interruption timing module, which is used to start and record the communication interruption duration according to the signal interference detection result when external interference or equipment failure is detected; A fault status determination module, which is used to determine the fault status and trigger the fault status confirmation mechanism when the communication interruption duration exceeds the preset time threshold; An emergency shutdown control module, which is used to send an emergency shutdown instruction through the fan main control unit to stop the fan operation according to the fault status confirmation result when the communication interruption is confirmed as the fault status; A shutdown status monitoring module, which is used to obtain the fan operation status data after the emergency shutdown instruction is executed, judge whether the fan has completely stopped, and record the shutdown time and the change of the operation status; A fault report generation module, which is used to generate a fault report including the fault type, interruption duration, shutdown time and fan state change information according to the shutdown time and the operation status data; A report upload module, which is used to upload the fault report to the ring network monitoring system for remote viewing and analysis by the operation and maintenance personnel, providing data support for subsequent fault handling.

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