Method and system for weather anomaly early warning for wind turbines

Abstract

The application provides a weather anomaly early warning method and system for a wind turbine, and relates to the technical field of weather anomaly early warning, and comprises the following steps: continuously monitoring weather conditions of the wind turbine and saving to a corresponding storage interval, the sensor types include a wind speed sensor, a wind direction sensor and a temperature sensor, extracting weather data, obtaining weather anomaly data, constructing a linkage control layer, outputting wind turbine speed adjustment data for triggering a speed adjustment instruction, constructing an emergency safety control model, embedding the linkage control layer in the emergency safety control model, obtaining real-time weather data, judging whether a power grid cut-off instruction is triggered, and if the power grid cut-off instruction is triggered, synchronously outputting a weather anomaly early warning signal. The application solves the technical problems that the prior art lacks timely monitoring and early warning mechanisms for the reaction of the wind turbine under different weather conditions, cannot quickly respond to weather anomaly conditions, and cannot accurately identify and judge weather anomaly events and take corresponding measures.

Description

Technical Field

[0001] This invention relates to the field of meteorological anomaly early warning technology, and specifically to a meteorological anomaly early warning method and system for wind turbine generators. Background Technology

[0002] Weather anomaly early warning for wind turbines is based on monitoring and predicting the meteorological impacts encountered during wind energy utilization to ensure the safe and stable operation of wind power systems. With the continuous growth of global demand for clean energy, wind power has become one of the important renewable energy sources. However, meteorological factors have a significant impact on wind power generation. For example, meteorological anomalies such as strong winds, lightning, and typhoons may cause damage to the units or shutdown. Therefore, in order to improve the reliability and stability of wind power systems, it is urgent to carry out research and application of weather anomaly early warning systems to reduce the accident rate, improve energy utilization efficiency, and promote the sustainable development of the wind power industry.

[0003] Current meteorological anomaly early warning methods for wind turbines have certain drawbacks. Existing technologies lack timely monitoring and early warning mechanisms for wind turbine responses under different weather conditions, making it difficult to quickly respond to meteorological anomalies. Furthermore, sensor data is not fully utilized and analyzed after acquisition, hindering accurate identification and judgment of meteorological anomalies and the implementation of corresponding measures. Therefore, there is still room for improvement in meteorological anomaly early warning systems for wind turbines. Summary of the Invention

[0004] This application provides a meteorological anomaly early warning method and system for wind turbines, aiming to solve the technical problems of existing technologies that lack timely monitoring and early warning mechanisms for the response of wind turbines under different meteorological conditions, are unable to respond quickly to meteorological anomalies, and cannot fully utilize and analyze sensor data after acquisition, thus failing to accurately identify and judge meteorological anomalies and take corresponding measures.

[0005] In view of the above problems, this application provides a method and system for early warning of meteorological anomalies for wind turbine units.

[0006] The first aspect of this application discloses a method for early warning of meteorological anomalies in wind turbine generators. The method includes: continuously monitoring meteorological conditions of wind turbine generators during routine operation and maintenance of a wind farm and saving the data to a corresponding storage area. The data identifiers in the storage area correspond to the sensor types connected to the interface of a sensor network, including wind speed sensors, wind direction sensors, and temperature sensors. The method also includes extracting meteorological data corresponding to the wind turbine generators from the storage area via the sensor network, including wind speed data, wind direction data, and temperature data. Finally, based on the meteorological data, the method obtains meteorological anomaly data, including high wind speed anomalies. The system collects meteorological data, including abnormal wind direction data, abnormal temperature data, abnormal extreme weather data, and abnormal thunderstorm data, where extreme weather includes heavy rain and hail. Based on the meteorological data and the abnormal meteorological data, a linkage control layer is constructed. The output of the linkage control layer is wind turbine speed adjustment data, which is used to trigger a single speed adjustment command. Based on the meteorological data, the abnormal meteorological data, and the wind turbine speed adjustment data, an emergency safety control model is constructed, and the linkage control layer is embedded in the emergency safety control model. Real-time meteorological data is acquired, and the emergency safety control model is used to determine whether a power grid disconnection command is triggered. If triggered, a meteorological anomaly warning signal is output synchronously.

[0007] Another aspect of this application discloses a meteorological anomaly early warning system for wind turbine generators. The system is used in the aforementioned method and includes: a meteorological condition monitoring module, used to continuously monitor meteorological conditions of wind turbine generators during the daily operation and maintenance of a wind farm and save the data to a corresponding storage area. The data identifiers in the storage area correspond to the sensor types connected to the interface of a sensor network, including wind speed sensors, wind direction sensors, and temperature sensors; a meteorological data extraction module, used to extract meteorological data corresponding to the wind turbine generators from the storage area corresponding to the wind turbine generators via the sensor network, the meteorological data including wind speed data, wind direction data, and temperature data; and an anomaly data acquisition module, used to acquire meteorological anomaly data based on the meteorological data, the meteorological anomaly data packet being... The system includes data on high wind speed anomalies, wind direction anomalies, temperature anomalies, extreme weather anomalies, and thunderstorm anomalies, with extreme weather including heavy rain and hail; a linkage control layer construction module, which constructs a linkage control layer based on the meteorological data and the meteorological anomaly data, and the output of the linkage control layer is wind turbine speed adjustment data, which is used to trigger a speed adjustment command; a safety control model construction module, which constructs an emergency safety control model based on the meteorological data, the meteorological anomaly data, and the wind turbine speed adjustment data, with the linkage control layer embedded in the emergency safety control model; and a judgment module, which acquires real-time meteorological data and uses the emergency safety control model to determine whether a power grid disconnection command has been triggered, and if so, synchronously outputs a meteorological anomaly warning signal.

[0008] A third aspect of this application provides a meteorological anomaly early warning system for wind turbine generators, comprising: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method described in the first aspect.

[0009] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The system continuously monitors meteorological conditions of wind turbines and saves the data to corresponding storage areas. Sensor types include wind speed, wind direction, and temperature sensors. Meteorological data is extracted, and abnormal meteorological data is acquired. A linkage control layer is constructed, outputting wind turbine speed adjustment data to trigger a primary speed adjustment command. An emergency safety control model is built, with the linkage control layer embedded within it. Real-time meteorological data is acquired, and the emergency safety control model determines whether a grid disconnection command should be triggered. If triggered, a meteorological anomaly warning signal is output synchronously. This addresses the technical problems of existing technologies, such as the lack of timely monitoring and early warning mechanisms for wind turbine responses under different meteorological conditions, the inability to quickly respond to meteorological anomalies, and the inability to fully utilize and analyze acquired sensor data to accurately identify and judge meteorological anomalies and take corresponding measures. The system achieves the technical effect of constructing an emergency safety control model based on meteorological and anomaly data, efficiently identifying and judging meteorological anomalies, promptly detecting meteorological anomalies, and generating corresponding control commands, thereby improving the sensitivity and accuracy of meteorological anomalies.

[0012] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0013] Figure 1 A schematic flowchart of a meteorological anomaly early warning method for wind turbine units is provided for embodiments of this application;

[0014] Figure 2 This application provides a flowchart illustrating the possible abnormal wind speed changes in a meteorological anomaly early warning method for wind turbines.

[0015] Figure 3 This application provides a schematic flowchart illustrating a possible method for triggering a power grid disconnection command in a meteorological anomaly early warning method for wind turbines.

[0016] Figure 4 This application provides a possible structural schematic diagram of a meteorological anomaly early warning system for wind turbine units;

[0017] Figure 5 This is a schematic diagram of the structure of an exemplary electronic device of this application.

[0018] Explanation of reference numerals in the attached diagram: Meteorological condition monitoring module 10, meteorological data extraction module 20, abnormal data acquisition module 30, linkage control layer construction module 40, safety control model construction module 50, judgment module 60, electronic device 300, memory 301, processor 302, communication interface 303, bus architecture 304. Detailed Implementation

[0019] This application provides a meteorological anomaly early warning method for wind turbines, which solves the technical problems of existing technologies that lack timely monitoring and early warning mechanisms for the response of wind turbines under different meteorological conditions, cannot respond quickly to meteorological anomalies, and cannot fully utilize and analyze sensor data after acquisition, thus failing to accurately identify and judge meteorological anomalies and take corresponding measures. It realizes the construction of an emergency safety control model based on meteorological data and anomaly data, efficiently identifies and judges meteorological anomalies, promptly detects meteorological anomalies, and generates corresponding control commands, thereby improving the technical effect of improving the sensitivity and accuracy of meteorological anomalies.

[0020] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0021] Example 1

[0022] like Figure 1 As shown in the embodiment of this application, a meteorological anomaly early warning method for wind turbine generators is provided, the method comprising:

[0023] Step S100: In the daily operation and maintenance of the wind farm, the meteorological conditions of the wind turbine are continuously monitored and saved to the corresponding storage area. The data identifier in the storage area corresponds to the sensor type connected to the interface of the sensor network. The sensor types include wind speed sensor, wind direction sensor, and temperature sensor.

[0024] In a preferred embodiment, wind speed sensors, wind direction sensors, and temperature sensors are installed in the wind farm to establish a sensor network. This sensor network is connected to a storage area via an interface. The sensor network continuously monitors the meteorological conditions around the wind turbines. The wind speed sensors measure the wind speed in the surrounding environment, the wind direction sensors measure the wind direction, and the temperature sensors measure the ambient temperature. The monitored meteorological data is saved to the corresponding storage area. Each sensor type has a specific data identifier to distinguish data from different sensor types.

[0025] Step S200: Extract meteorological data corresponding to the wind turbine from the storage area corresponding to the wind turbine through the sensor network. The meteorological data includes wind speed data, wind direction data, and temperature data.

[0026] In a preferred embodiment, the storage area corresponding to the wind turbine is accessed through a sensor network, and data of different sensor types are obtained according to the data identifier, including wind speed data monitored by the wind speed sensor, wind direction data monitored by the wind direction sensor, and temperature data monitored by the temperature sensor.

[0027] Step S300: Based on the meteorological data, obtain meteorological anomaly data, including high wind speed anomaly data, wind direction anomaly data, temperature anomaly data, extreme climate anomaly data, and thunderstorm anomaly data, wherein the extreme climate includes rainstorms and hail.

[0028] In a preferred embodiment, by setting thresholds, including wind speed threshold, wind direction change threshold, and temperature threshold, it is determined whether there are high wind speed anomalies, wind direction anomalies, and temperature anomalies. For example, if the wind speed exceeds the set threshold, it is identified as high wind speed anomaly data. Furthermore, based on various indicators in the meteorological data, including wind speed, wind direction, and temperature, it is determined whether extreme weather conditions, such as heavy rain and hail, have occurred. When specific conditions are met, it is marked as extreme weather anomaly data. By analyzing relevant parameters in the meteorological data, such as electric field strength, precipitation, and cloud images, it is determined whether there are thunderstorm anomalies.

[0029] Among them, abnormal wind speed indicates that the wind speed is too high, which may exceed the rated operating range of the unit; abnormal wind direction indicates that the wind direction changes suddenly, resulting in unstable wind energy resources and affecting the working efficiency of the wind turbine; abnormal temperature indicates that excessively high temperature may cause the wind turbine to overheat, and excessively low temperature may cause freezing problems; extreme weather indicates that extreme weather events such as rainstorms and hail may cause direct damage to the wind turbine; thunderstorm indicates that thunderstorms may threaten the electrical equipment of the wind turbine, causing fires or damage.

[0030] Step S400: Based on the meteorological data and the meteorological anomaly data, a linkage control layer is constructed. The output of the linkage control layer is the fan speed adjustment data, which is used to trigger a speed adjustment command.

[0031] In a preferred embodiment, a linkage control layer is constructed based on the meteorological data and meteorological anomaly data. This control layer is a model used to generate corresponding wind turbine speed adjustment data according to the current meteorological conditions and anomalies. This data is used to adjust the wind turbine speed to adapt to the current meteorological conditions and anomalies, such as reducing the wind turbine speed to ensure the safe operation of the wind turbine. This data is then used to trigger a speed adjustment command, that is, the adjusted speed command is sent to the wind turbine control system to actually adjust the wind turbine speed.

[0032] Step S500: Based on the meteorological data, the meteorological anomaly data, and the wind turbine speed adjustment data, an emergency safety control model is constructed, and the linkage control layer is embedded in the emergency safety control model;

[0033] In a preferred embodiment, an emergency safety control model is constructed. This model is a system that comprehensively considers meteorological conditions, abnormal situations, and speed adjustments to assess the safety of wind turbine units in real time. The constructed linkage control layer is embedded within the emergency safety control model. This allows the emergency safety control model to directly acquire and utilize the wind turbine speed adjustment data generated by the linkage control layer to formulate safety control strategies.

[0034] Step S600: Obtain real-time meteorological data, and determine whether a power grid disconnection command is triggered through the emergency safety control model. If triggered, output a meteorological anomaly warning signal simultaneously.

[0035] In a preferred embodiment, real-time meteorological data, including parameters such as wind speed, wind direction, and temperature, is acquired through a sensor network. This data is then input into a constructed emergency safety control model for evaluation. The model assesses whether the current weather conditions have reached or exceeded a safety threshold. If the model determines that the current weather conditions have reached the safety threshold for triggering grid disconnection, indicating a serious meteorological anomaly, a grid disconnection command is executed, meaning an instruction is sent to disconnect the wind turbine from the power grid. Simultaneously, a meteorological anomaly warning signal is generated and output. This signal can be communicated to relevant personnel or systems promptly via sound, light, or text messages, prompting appropriate emergency measures to ensure the safe operation of the wind turbine and its surrounding environment.

[0036] Furthermore, such as Figure 2 As shown, step S300 of this application, obtaining meteorological anomaly data, further includes:

[0037] Step S310: Obtain the rated operating range of the wind turbine, wherein the rated operating range includes the rated speed range of the impeller;

[0038] Step S320: Calculate the rate of change of wind speed using the wind speed data;

[0039] Step S330: Determine abnormal wind speed change data by using the wind speed change rate and the rated speed range of the impeller.

[0040] In a preferred embodiment, the rated operating parameters of the wind turbine are obtained according to the technical specifications of the wind turbine, including the rated speed of the impeller. Based on the rated operating parameters, the rated speed range of the impeller is determined, which represents the reasonable speed range that the wind turbine should maintain within the normal operating range.

[0041] Based on the requirements, determine the time interval for calculating the rate of change of wind speed. Shorter time intervals provide more refined information on the rate of change, while longer time intervals provide a smoother trend. Within the selected time interval, compare the wind speed data at two consecutive time points. By calculating the difference between the wind speed data at the two points and then dividing the difference by the time interval, the rate of change of wind speed between the two points can be obtained. Repeat this calculation, sequentially calculating the rate of change of wind speed for the entire wind speed data sequence according to the selected time interval, until all data points have been calculated.

[0042] The wind speed change rate is compared with the rated speed range of the impeller. If the wind speed change rate exceeds the limit of the rated speed range, it can be identified as abnormal wind speed change data. This indicates that the wind speed change rate of the wind turbine is faster than the design range, which may pose a potential safety risk to the unit. The detected abnormal wind speed change data is marked for subsequent processing and analysis.

[0043] Furthermore, such as Figure 3 As shown, step S600 of this application, which involves determining whether a power grid disconnection command is triggered through the emergency safety control model, further includes:

[0044] Step S610: Use the fan speed adjustment data to trigger a speed adjustment command to reduce the speed of the motor unit and obtain the motor unit speed adjustment data.

[0045] Step S620: If the speed adjustment data of the motor set does not fall within the rated speed range of the impeller, a power grid disconnection command is triggered.

[0046] In a preferred embodiment, a primary speed adjustment command is triggered based on the wind turbine speed adjustment data. This means sending a command to reduce the wind turbine's speed to adapt to current weather conditions and abnormal situations. Based on the primary speed adjustment command, corresponding control actions are executed by adjusting equipment such as the generator control system, pitch system, or frequency converter to reduce the wind turbine's speed. After the primary speed adjustment is completed, the actual turbine speed data after the primary speed adjustment is obtained through monitoring the wind turbine.

[0047] Check whether the speed adjustment data of the generator set falls within the rated speed range of the impeller. If it does not, it indicates that an abnormal speed has occurred beyond the safe range, which will trigger a grid disconnection command. According to the triggering conditions, when an abnormal speed occurs, a corresponding command is sent to disconnect the connection between the wind turbine and the grid through electrical equipment such as control switches or circuit breakers, so as to protect the unit and the connected network from potential risks.

[0048] Furthermore, in step S400 of this application: constructing a linkage control layer based on the meteorological data and the meteorological anomaly data, the following steps are also included prior to:

[0049] Step S400-1: Obtain the fault maintenance records corresponding to the wind speed sensor, wind direction sensor, and temperature sensor. The fault maintenance records include the time period of sensor failure.

[0050] Step S400-2: Based on the meteorological data and the meteorological anomaly data, set a data fault marker to mark the meteorological data and meteorological anomaly data during the fault period of the sensor fault period;

[0051] Step S400-3: Based on the meteorological data and the meteorological anomaly data, perform preprocessing to obtain meteorological preprocessed data and meteorological anomaly preprocessed data;

[0052] Step S400-4: Compare the meteorological data and meteorological anomaly data during the fault period with the meteorological preprocessing data and meteorological anomaly preprocessing data. If there is no overlap in the data, the sensor operation interruption signal will be directly triggered after the corresponding fault type in the fault maintenance record occurs.

[0053] In a preferred embodiment, the equipment maintenance team or monitoring system obtains the fault maintenance records corresponding to the wind speed sensor, wind direction sensor, and temperature sensor. The fault maintenance records include the start and end times of the fault periods of the wind speed sensor, wind direction sensor, and temperature sensor. The fault maintenance records of all sensors are integrated together for subsequent processing and analysis.

[0054] Based on the sensor failure period, meteorological data within that period is marked. Similarly, abnormal meteorological data within the same failure period is also marked accordingly. This allows for clear differentiation of abnormal situations during the failure period. The marked data failure information is recorded, establishing a fault marking record corresponding to the meteorological data, to better process and interpret meteorological data and abnormal situations during the failure period.

[0055] The acquired meteorological data undergoes data cleaning, including removing duplicate data points, repairing missing data, and handling outliers to ensure data integrity and accuracy. If missing data points or large data gaps exist, data interpolation operations, such as linear interpolation and spline interpolation, are performed to fill in the missing or gapped data. After data cleaning and interpolation, preprocessed meteorological data is obtained, i.e., meteorological preprocessed data. Similarly, the meteorological anomaly data undergoes similar preprocessing operations, including data cleaning and interpolation, to obtain meteorological anomaly preprocessed data.

[0056] The meteorological data during the fault period is compared with the pre-processed meteorological data. Similarly, the abnormal meteorological data during the fault period is compared with the pre-processed abnormal meteorological data to check for overlap and identify invalid data. If no overlap is found, the data during the fault period is considered invalid. Based on the fault type in the fault maintenance record, and after confirming that there is no data overlap, a sensor operation interruption signal is directly triggered. This will notify relevant personnel or the system administrator that the sensor needs to be repaired, replaced, or otherwise handled as necessary.

[0057] Furthermore, this application also includes:

[0058] Step S400-5: If data overlap exists, obtain meteorological overlap data and meteorological anomaly overlap data;

[0059] Step S400-6: Determine the period of continuous failure that can be operated using the meteorological overlap data and meteorological anomaly overlap data;

[0060] Step S400-7: After a fault type corresponding to the working continuous fault period in the fault maintenance record occurs, keep the corresponding sensor in working state.

[0061] In a preferred embodiment, if data overlap exists, a timestamp or other identifier is used to determine the start and end times of the data overlap, and meteorological data within the overlapping period is extracted, including meteorological parameters such as wind speed, wind direction, and temperature. Similarly, meteorological anomaly data within the overlapping period is extracted, which includes abnormal conditions observed during the overlapping period, such as sudden changes in wind speed or abnormal wind direction.

[0062] Further analysis is conducted on overlapping meteorological data, including comparing and verifying the accuracy of the data and clarifying the meteorological conditions during overlapping periods. Similarly, for overlapping meteorological data with anomalies, analysis and evaluation are performed to identify anomalies and risks within the overlapping periods. Based on the analysis results and business needs, such as relevant wind power industry specifications and standards, the tolerable fault periods and abnormal data ranges are determined to ensure that wind turbines can still operate safely during fault periods without causing greater risks or anomalies. This determines the time periods during which continuous operation is possible, i.e., the continuous fault periods, during which, although faults and abnormal data exist, sensors can still function normally and provide reliable meteorological data.

[0063] Once the period of continuous failure that can be operated is determined, the corresponding sensors are kept operational during that period. This means that even during the failure, the wind speed sensor, wind direction sensor, or temperature sensor continues to take measurements and provide meteorological data.

[0064] Furthermore, step S400 of this application, which involves constructing a linkage control layer based on the meteorological data and the meteorological anomaly data, further includes:

[0065] Step S410: Check the wear of the bearings and gears of the wind turbine and whether there is any abnormal noise, and obtain bearing-gear meshing inspection information;

[0066] Step S420: The bearing-gear meshing check information is used as constraint information and input into the linkage control layer of the emergency safety control model. The bearing-gear meshing check information is used to trigger a secondary speed adjustment command.

[0067] In a preferred embodiment, the bearings and gears of the wind turbine are inspected, including visual inspection, vibration analysis, and acoustic testing. Visual inspection involves observing the surface to check for obvious wear, cracks, or other abnormalities. Vibration analysis uses vibration sensors to detect vibration levels and analyzes their frequency and amplitude; abnormal vibration signals indicate problems. Acoustic testing uses sound sensors to monitor noise generated during operation and analyzes noise characteristics to determine if any abnormalities exist. The inspection results of the bearings and gears are compiled into bearing-gear meshing inspection information, which includes the condition of the bearings and gears, the degree of wear, and abnormal noise.

[0068] In the linkage control layer, bearing-gear meshing check information is used as constraint information to trigger a secondary speed adjustment command. This indicates that when wear or abnormal noise is detected in the bearing or gear, the linkage control layer will adjust the speed of the wind turbine accordingly to reduce the load or prevent further damage.

[0069] Furthermore, this application also includes:

[0070] Step S710: Analyze the stability of the wind turbine tower and blades. If tower vibration or blade vibration exists, obtain the unit vibration inspection information.

[0071] Step S720: Check the balance of the rotor, shaft and bearings of the wind turbine. If the rotor is eccentric and / or the shaft and bearings are misaligned, obtain the unit balance check information.

[0072] Step S730: The unit vibration inspection information and the unit balance inspection information are designated as prohibited objects, a prohibited table is set and saved to the emergency safety control model.

[0073] In a preferred embodiment, vibration sensors are installed to monitor the vibration of the tower and blades. If the abnormal vibration of the tower or blades exceeds a preset threshold, a vibration problem may exist. The results of the tower and blade vibration inspection are recorded, including information such as the location, frequency, and amplitude of the vibration. This information is used to assess the vibration status of the unit, help detect any abnormal vibrations, and serve as a basis for subsequent linkage control and maintenance decisions.

[0074] When the unit is stopped, the mass distribution of the wind turbine rotor is detected and calibrated using balancing equipment or mass calibration devices to determine whether there is any eccentricity or imbalance in the rotor. When the unit is running, vibration sensors and other equipment are used to monitor the vibration of the rotor. By analyzing the vibration frequency and amplitude, it can be determined whether there is any imbalance problem in the rotor. The results of static and dynamic balance checks are recorded, including information such as the rotor's balance status, eccentricity, vibration frequency, and amplitude.

[0075] By conducting actual measurements, the clearance between the wind turbine's shaft and bearings is checked to determine if there is any misalignment between the shaft and bearings, and the results of the shaft and bearing alignment checks are recorded. By checking the balance and alignment of the rotor, shaft, and bearings, it is possible to determine if the wind turbine has rotor eccentricity or imbalance, or shaft and bearing misalignment issues. Balance check information is obtained from these checks to assess the wind turbine's balance status and provide a basis for subsequent control and maintenance decisions.

[0076] Based on the collected unit vibration and balance inspection information, a taboo list is set up. The taboo list has a high priority and contains constraint rules that prohibit or restrict specific operations or states. The taboo list is stored in the emergency safety control model and participates in the decision-making process of the control system during operation. This ensures that this information is taken into account in the safety control model in emergency situations. Specifically, if any of the tower vibration, blade vibration, or dynamic balance problems occur, a grid disconnection command is directly triggered through the taboo list to carry out timely maintenance (without taking a secondary speed reduction approach). This prevents further damage or failure caused by unit vibration and balance problems.

[0077] In summary, the meteorological anomaly early warning method and system for wind turbine units provided in this application have the following technical effects:

[0078] Meteorological conditions of wind turbines are continuously monitored and saved to the corresponding storage area. Sensor types include wind speed sensors, wind direction sensors, and temperature sensors. Meteorological data is extracted, meteorological anomaly data is acquired, a linkage control layer is constructed, and the output is wind turbine speed adjustment data, which is used to trigger a speed adjustment command. An emergency safety control model is constructed, and the linkage control layer is embedded in the emergency safety control model. Real-time meteorological data is acquired, and the emergency safety control model determines whether to trigger a grid disconnection command. If triggered, a meteorological anomaly warning signal is output synchronously.

[0079] This invention addresses the technical problems of existing technologies, such as the lack of timely monitoring and early warning mechanisms for wind turbines under different meteorological conditions, the inability to quickly respond to meteorological anomalies, and the inability to fully utilize and analyze sensor data to accurately identify and judge meteorological anomalies and take corresponding measures. It enables the construction of an emergency safety control model based on meteorological and anomaly data, which can efficiently identify and judge meteorological anomalies, promptly detect meteorological anomalies, and generate corresponding control commands, thereby improving the sensitivity and accuracy of meteorological anomalies.

[0080] Example 2

[0081] Based on the same inventive concept as the meteorological anomaly early warning method for wind turbines in the foregoing embodiments, such as Figure 4 As shown, this application provides a meteorological anomaly early warning system for wind turbine generators, the system comprising:

[0082] The meteorological condition monitoring module is used to continuously monitor the meteorological conditions of the wind turbines during the daily operation and maintenance of the wind farm and save the data to the corresponding storage area. The data identifiers in the storage area correspond to the sensor types connected to the interface of the sensor network. The sensor types include wind speed sensors, wind direction sensors, and temperature sensors.

[0083] The meteorological data extraction module is used to extract meteorological data corresponding to the wind turbine from the storage area corresponding to the wind turbine through a sensor network. The meteorological data includes wind speed data, wind direction data, and temperature data.

[0084] An abnormal data acquisition module is used to acquire meteorological abnormal data based on the meteorological data. The meteorological abnormal data includes high wind speed abnormal data, wind direction abnormal data, temperature abnormal data, extreme climate abnormal data, and thunderstorm abnormal data. The extreme climate includes rainstorms and hail.

[0085] A linkage control layer construction module is used to construct a linkage control layer based on the meteorological data and the meteorological anomaly data. The output of the linkage control layer is fan speed adjustment data, which is used to trigger a speed adjustment command.

[0086] A safety control model construction module is used to construct an emergency safety control model based on the meteorological data, the meteorological anomaly data, and the wind turbine speed adjustment data. The linkage control layer is embedded in the emergency safety control model.

[0087] The judgment module is used to acquire real-time meteorological data and determine whether a power grid disconnection command is triggered through the emergency safety control model. If triggered, a meteorological anomaly warning signal is output synchronously.

[0088] Furthermore, the system also includes:

[0089] The operating range acquisition module is used to acquire the rated operating range of the wind turbine, which includes the rated speed range of the impeller.

[0090] The rate of change calculation module is used to calculate the rate of change of wind speed using the wind speed data;

[0091] The abnormal data determination module is used to determine abnormal data of sudden wind speed changes by comparing the wind speed change rate with the rated speed range of the impeller.

[0092] Furthermore, the system also includes:

[0093] The primary adjustment data acquisition module is used to trigger a primary speed adjustment command using the fan speed adjustment data, reduce the speed of the motor unit, and acquire the primary speed adjustment data of the motor unit.

[0094] The disconnection command triggering module is used to trigger a power grid disconnection command if the speed adjustment data of the motor set does not fall within the rated speed range of the impeller.

[0095] Furthermore, the system also includes:

[0096] The fault maintenance record acquisition module is used to acquire the fault maintenance records corresponding to the wind speed sensor, wind direction sensor, and temperature sensor. The fault maintenance records include the time period of sensor failure.

[0097] An abnormal data marking module is used to set data fault markers based on the meteorological data and the abnormal meteorological data, and to mark the meteorological data and abnormal meteorological data during the fault period of the sensor fault period.

[0098] The preprocessing module is used to preprocess the meteorological data and the meteorological anomaly data to obtain meteorological preprocessed data and meteorological anomaly preprocessed data.

[0099] The interrupt signal triggering module is used to compare the meteorological data and meteorological anomaly data during the fault period with the meteorological preprocessing data and meteorological anomaly preprocessing data. If there is no overlap in the data, the sensor operation interruption signal is directly triggered after the corresponding fault type in the fault maintenance record occurs.

[0100] Furthermore, the system also includes:

[0101] The overlapping data acquisition module is used to acquire meteorological overlapping data and meteorological anomaly overlapping data if data overlap exists.

[0102] The working period acquisition module is used to determine the working continuous fault period through the meteorological overlap data and meteorological anomaly overlap data;

[0103] The sensor setting module is used to keep the corresponding sensor in working state after the fault type corresponding to the working continuous fault period in the fault maintenance record occurs.

[0104] Furthermore, the system also includes:

[0105] The abnormal noise detection module is used to check the wear of the bearings and gears of the wind turbine and whether there is abnormal noise, and to obtain bearing-gear meshing inspection information;

[0106] The inspection information input module is used to input the bearing-gear meshing inspection information as constraint information into the linkage control layer of the emergency safety control model. The bearing-gear meshing inspection information is used to trigger a secondary speed adjustment command.

[0107] Furthermore, the system also includes:

[0108] The vibration inspection information acquisition module is used to analyze the stability of the tower and blades of the wind turbine. If tower vibration or blade vibration exists, the module acquires the vibration inspection information of the turbine.

[0109] The balance check information acquisition module is used to check the balance of the rotor, shaft and bearing of the wind turbine. If the rotor is eccentric and / or the shaft and bearing are misaligned, the balance check information of the unit is acquired.

[0110] The taboo list setting module is used to set taboo lists and save the unit vibration inspection information and the unit balance inspection information as taboo objects to the emergency safety control model.

[0111] Exemplary electronic devices

[0112] The following is for reference. Figure 5 To describe the electronic device of this application, based on the same inventive concept as the meteorological anomaly early warning method for wind turbines in the foregoing embodiments, this application also provides a meteorological anomaly early warning system for wind turbines, including: a processor coupled to a memory for storing a program, which, when executed by the processor, causes the system to perform the method described in any one of the embodiments.

[0113] The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, processor 302, and memory 301 can be interconnected via the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0115] Communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0116] Memory 301 can be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory can exist independently and be connected to the processor via bus architecture 304. Memory can also be integrated with the processor.

[0117] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the crystallizer copper plate quality optimization method based on thermal conductivity evaluation provided in the above embodiments of this application.

[0118] Optionally, the computer execution instructions in this application may also be referred to as application code, and this application does not specifically limit them.

[0119] Through the foregoing detailed description of the meteorological anomaly early warning method for wind turbine units, those skilled in the art can clearly understand the meteorological anomaly early warning method and system for wind turbine units in this embodiment. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section description.

[0120] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for early warning of meteorological anomalies in wind turbine generators, characterized in that, The method includes: In the daily operation and maintenance of wind farms, meteorological conditions of wind turbine units are continuously monitored and saved to the corresponding storage area. The data identifiers in the storage area correspond to the sensor types connected to the interface of the sensor network. The sensor types include wind speed sensors, wind direction sensors, and temperature sensors. The meteorological data corresponding to the wind turbine is extracted from the storage area corresponding to the wind turbine through a sensor network. The meteorological data includes wind speed data, wind direction data, and temperature data. Based on the meteorological data, meteorological anomaly data is obtained, including high wind speed anomaly data, wind direction anomaly data, temperature anomaly data, extreme climate anomaly data, and thunderstorm anomaly data, including heavy rain and hail. Based on the meteorological data and the meteorological anomaly data, a linkage control layer is constructed. The output of the linkage control layer is the fan speed adjustment data, which is used to trigger a speed adjustment command. Based on the meteorological data, the meteorological anomaly data, and the wind turbine speed adjustment data, an emergency safety control model is constructed, and the linkage control layer is embedded in the emergency safety control model; Acquire real-time meteorological data, and determine whether to trigger a power grid disconnection command through the emergency safety control model. If triggered, output a meteorological anomaly warning signal simultaneously. Before constructing the linkage control layer based on the meteorological data and the meteorological anomaly data, the method further includes: Obtain the fault maintenance records corresponding to the wind speed sensor, wind direction sensor, and temperature sensor. The fault maintenance records include the time period of sensor failure. Based on the meteorological data and the meteorological anomaly data, a data fault marker is set to mark the meteorological data and meteorological anomaly data during the fault period that are within the sensor fault time period. Preprocessing is performed on the meteorological data and the meteorological anomaly data to obtain meteorological preprocessed data and meteorological anomaly preprocessed data. If the meteorological data and meteorological anomaly data during the fault period are compared with the meteorological preprocessing data and meteorological anomaly preprocessing data, and if there is no overlap in the data, then after the fault type corresponding to the fault maintenance record occurs, the sensor operation interruption signal is directly triggered.

2. The meteorological anomaly early warning method for wind turbine units as described in claim 1, characterized in that, The method for acquiring meteorological anomaly data further includes: Obtain the rated operating range of the wind turbine, wherein the rated operating range includes the rated speed range of the impeller; The wind speed change rate is calculated using the wind speed data. By using the wind speed change rate and the impeller rated speed range, abnormal data of sudden wind speed changes are determined.

3. The meteorological anomaly early warning method for wind turbine units as described in claim 2, characterized in that, The method for determining whether to trigger a power grid disconnection command using the emergency safety control model includes: The fan speed adjustment data is used to trigger a speed adjustment command to reduce the speed of the motor unit and obtain the motor unit speed adjustment data. If the speed adjustment data of the motor unit does not fall within the rated speed range of the impeller, a power grid disconnection command is triggered.

4. The meteorological anomaly early warning method for wind turbine generators as described in claim 1, characterized in that, The method further includes: If data overlaps, obtain overlapping meteorological data and overlapping meteorological anomalies. By using the aforementioned meteorological overlap data and meteorological anomaly overlap data, the period of continuous failure that can be operated can be determined; After a fault type corresponding to a working continuous fault period occurs in the aforementioned fault maintenance record, the corresponding sensor remains in working condition.

5. The meteorological anomaly early warning method for wind turbine generators as described in claim 1, characterized in that, Based on the meteorological data and the meteorological anomaly data, a linkage control layer is constructed, and the method further includes: Check the wear of the bearings and gears of the wind turbine and whether there is any abnormal noise, and obtain bearing-gear meshing inspection information; The bearing-gear meshing check information is used as constraint information and input into the linkage control layer of the emergency safety control model. The bearing-gear meshing check information is used to trigger a secondary speed adjustment command.

6. The meteorological anomaly early warning method for wind turbine units as described in claim 5, characterized in that, The method for determining whether a power grid disconnection command is triggered using the aforementioned emergency safety control model further includes: Analyze the stability of the wind turbine tower and blades. If tower vibration or blade vibration exists, obtain the unit vibration inspection information. Check the balance of the rotor, shaft and bearings of the wind turbine. If the rotor is eccentric and / or the shaft and bearings are misaligned, obtain the balance check information of the unit. The unit vibration inspection information and the unit balance inspection information are designated as prohibited objects, and a prohibited table is set and saved to the emergency safety control model.

7. A meteorological anomaly early warning system for wind turbine generators, characterized in that, The method for implementing the meteorological anomaly early warning method for wind turbine units according to any one of claims 1-6 includes: The meteorological condition monitoring module is used to continuously monitor the meteorological conditions of the wind turbines during the daily operation and maintenance of the wind farm and save the data to the corresponding storage area. The data identifiers in the storage area correspond to the sensor types connected to the interface of the sensor network. The sensor types include wind speed sensors, wind direction sensors, and temperature sensors. The meteorological data extraction module is used to extract meteorological data corresponding to the wind turbine from the storage area corresponding to the wind turbine through a sensor network. The meteorological data includes wind speed data, wind direction data, and temperature data. An abnormal data acquisition module is used to acquire meteorological abnormal data based on the meteorological data. The meteorological abnormal data includes high wind speed abnormal data, wind direction abnormal data, temperature abnormal data, extreme climate abnormal data, and thunderstorm abnormal data. The extreme climate includes rainstorms and hail. A linkage control layer construction module is used to construct a linkage control layer based on the meteorological data and the meteorological anomaly data. The output of the linkage control layer is fan speed adjustment data, which is used to trigger a speed adjustment command. A safety control model construction module is used to construct an emergency safety control model based on the meteorological data, the meteorological anomaly data, and the wind turbine speed adjustment data. The linkage control layer is embedded in the emergency safety control model. The judgment module is used to acquire real-time meteorological data and determine whether a power grid disconnection command is triggered through the emergency safety control model. If triggered, a meteorological anomaly warning signal is output synchronously.

8. A meteorological anomaly early warning system for wind turbine generators, characterized in that, include: A processor coupled to a memory for storing a program, which, when executed by the processor, causes the system to perform the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

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