Intelligent Redundant Group Control Method, Device, Storage Medium and Terminal Equipment for Wind Turbine Units

Abstract

This application provides a method, apparatus, storage medium, and terminal equipment for intelligent redundant group control of wind turbine generators, relating to the field of wind turbine generator control technology. The method includes: acquiring first monitoring data of a target wind turbine generator at the current moment; if the first monitoring data is determined to be abnormal, acquiring second monitoring data of at least one adjacent wind turbine generator of the target wind turbine generator at the current moment; if the second monitoring data is determined to be normal, acquiring first environmental data of the target wind turbine generator; performing a weighted fitting of the second monitoring data based on the first environmental data to obtain third monitoring data, where the third monitoring data is the equivalent data of the second monitoring data under the first environmental data; and using the third monitoring data as the target monitoring data of the target wind turbine generator at the current moment. This application can ensure the normal operation of the wind turbine generator in the event of sensor failure, avoiding prolonged downtime and improving power generation efficiency.

Description

Technical Field

[0001] This application relates to the field of wind turbine control technology, specifically to a wind turbine intelligent redundant group control method, a wind turbine intelligent redundant group control device, a machine-readable storage medium, and a terminal device. Background Technology

[0002] External environmental factors such as wind speed, wind direction, and temperature have a significant impact on the normal operation of wind turbine generators. Since wind speed, wind direction, and temperature are highly variable and unpredictable under most operating conditions, effective and accurate data acquisition of wind speed, wind direction, and temperature is an indispensable and important part of wind turbine generator control.

[0003] Currently, the methods for collecting wind speed, direction, and temperature data for large wind turbine generators are typically as follows: wind speed and direction signals are obtained through anemometers and wind vanes; mechanical methods calculate current wind speed and direction data by rotating the wind cups and the wind vane shaft; ultrasonic methods calculate wind speed and direction data using ultrasonic probes; and temperature is generally collected using Pt100 thermistors. The control method for wind turbine generators is as follows: the anemometer collects the wind speed signal and feeds it back to the main control system. The main control system automatically determines whether the generator needs to connect to the grid for power generation or shut down due to excessive wind speed based on the current wind speed information. The wind vane sensor collects the difference in wind direction angle between the wind direction and the nacelle position and transmits it to the main control system to determine whether the generator needs to yaw to adjust to the wind. The ambient temperature signal determines whether the wind turbine needs to shut down for protection in extreme high or low temperature environments.

[0004] Redundancy control of external equipment such as anemometers, wind vanes, and some temperature sensors in most existing wind turbines is achieved by installing an additional set of equipment, such as two sets of anemometers or wind vanes or two temperature sensors. When equipment 1 malfunctions, data from equipment 2 is temporarily used. However, the current technology increases the overall equipment cost, and when the backup equipment also malfunctions, the turbine must be shut down for maintenance. This situation is particularly evident in wind farms during winter when heavy snow closes off the mountains. Maintenance personnel cannot replace or repair the turbines in a timely manner, resulting in prolonged shutdowns, wasting wind resources and losing electricity revenue.

[0005] Application content

[0006] The purpose of this application is to provide a method, device, storage medium, and terminal equipment for intelligent redundant group control of wind turbine generators to solve the above-mentioned problems.

[0007] To achieve the above objectives, the first aspect of this application provides a method for intelligent redundant group control of wind turbine generators, comprising:

[0008] Obtain the first monitoring data of the target wind turbine at the current moment;

[0009] If the first monitoring data is determined to be abnormal, the second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current time, wherein the adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine.

[0010] If the second monitoring data is determined to be normal, the first environmental data of the target wind turbine is obtained;

[0011] The second monitoring data is weighted and fitted based on the first environmental data to obtain the third monitoring data, which is the equivalent data of the second monitoring data under the first environmental data.

[0012] The third monitoring data is used as the target monitoring data of the target wind turbine at the current moment.

[0013] Optionally, the first monitoring data and the second monitoring data include at least:

[0014] One or more of wind speed, wind direction, and ambient temperature.

[0015] Optionally, determining that the first monitoring data is abnormal includes:

[0016] If the wind speed of the target wind turbine at the current moment is the same as the historical wind speed of the target wind turbine at the previous N moments, the first monitoring data is determined to be abnormal.

[0017] Optionally, if the wind direction of the target wind turbine at the current moment and the historical wind direction of the target wind turbine at the previous N moments are the same, the first monitoring data is determined to be abnormal.

[0018] Optionally, determining that the first monitoring data is abnormal includes:

[0019] Determine the average ambient temperature of all wind turbines in the wind turbine group, excluding the target wind turbine, at the current moment.

[0020] If the difference between the ambient temperature of the target wind turbine at the current moment and the obtained average ambient temperature reaches the ambient temperature difference threshold, the first monitoring data is determined to be abnormal.

[0021] Optionally, the first environmental data includes at least:

[0022] The location and altitude of the target wind turbine.

[0023] Optionally, the second monitoring data is weighted and fitted based on the first environmental data to obtain the third monitoring data, including:

[0024] The weighting coefficients of the first environmental data are determined by a preset relationship table, which includes at least the weighting coefficients of wind speed, wind direction or ambient temperature corresponding to different locations and altitudes.

[0025] The wind speed, wind direction, or ambient temperature of adjacent wind turbines at the current moment are weighted and fitted using the weighting coefficients of the first environmental data, and one or more of the weighted fitted wind speed, wind direction, and ambient temperature are determined as the third monitoring data.

[0026] A second aspect of this application provides an intelligent redundant group control device for wind turbine generators, characterized in that it comprises:

[0027] The data acquisition module is configured to acquire the first monitoring data of the target wind turbine at the current moment;

[0028] If the first monitoring data is determined to be abnormal, second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current moment, wherein the adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine; and

[0029] If the second monitoring data is determined to be normal, the first environmental data of the target wind turbine is obtained;

[0030] A data correction module is configured to perform a weighted fitting of the second monitoring data based on the first environmental data to obtain third monitoring data, wherein the third monitoring data is the equivalent data of the second monitoring data under the first environmental data; and

[0031] The third monitoring data is used as the target monitoring data of the target wind turbine at the current moment.

[0032] In a third aspect, this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned intelligent redundancy group control method for wind turbine generators.

[0033] In a fourth aspect, this application provides a terminal device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described intelligent redundant group control method for wind turbine generators.

[0034] The embodiments provided in this application have the following beneficial effects:

[0035] This application monitors whether the monitoring data of wind turbine units is abnormal. If the monitoring data is determined to be abnormal, the normal monitoring data of the wind turbine units adjacent to the current wind turbine unit is obtained. The monitoring data of the adjacent wind turbine units is weighted and fitted based on the environmental data of the current wind turbine unit and the adjacent wind turbine units to obtain the equivalent monitoring data of the current wind turbine unit. This allows the monitoring data of the wind turbine unit to be determined when the sensor fails without adding additional sensor equipment, thereby ensuring the normal operation of the wind turbine unit in the event of sensor failure, avoiding long-term downtime and improving power generation efficiency.

[0036] Other features and advantages of the embodiments or implementations of this application will be described in detail in the following detailed description section. Attached Figure Description

[0037] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0038] Figure 1 The schematic diagram illustrates a method flowchart of the intelligent redundancy group control method for wind turbines according to an embodiment of this application;

[0039] Figure 2 The schematic diagram illustrates the redundant control logic of a wind turbine according to an embodiment of this application;

[0040] Figure 3 A schematic block diagram of the intelligent redundant group control device for wind turbines according to an embodiment of this application is shown.

[0041] Figure 4 The schematic diagram illustrates a terminal device structure according to an embodiment of this application.

[0042] Explanation of reference numerals in the attached figures

[0043] 10 - Terminal device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0044] The specific embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the embodiments of this application.

[0045] Understandably, the method of this application can be applied to wind turbine control systems. The main control PLC of the wind turbine uses a Bahman controller, and the wind turbine control system adopts a B / S architecture (browser and server architecture mode) to facilitate data exchange and interaction among all wind turbines within the wind farm. This enables intelligent redundant control of external devices such as anemometers, vanes, and temperature sensors of the wind turbines. The server can be a Goahead embedded WebServer, which accesses and connects to all wind turbines within the wind farm via HTTP polling. For example, AJAX can be used to interact with the Goahead embedded server through polling, and the GoForms procedure of the Goahead server can be used to process front-end commands. Then, the Web_SviRead and Web_SviWrite methods can be used to read and write SVI variables in the Bahman controller.

[0046] In this application, the wind turbine control system consists of a redundant control and monitoring system, a wind turbine peripheral signal acquisition system, a control signal and redundant data input system, and an intelligent redundant control system. The redundant control and monitoring system monitors the external equipment signals of all wind turbines in real time. When a problem occurs with the external equipment of a wind turbine in the wind farm, such as a sensor, a redundant control signal is generated to control the switching of data collected from the faulty equipment. The intelligent redundant control system identifies the redundant control signals of the wind turbine with the faulty external equipment, selects nearby normally operating wind turbines based on the location of the faulty wind turbine, and activates the wind turbine peripheral signal acquisition system to collect peripheral data from these normally operating wind turbines. Then, it calculates the peripheral data for the current faulty turbine location by weighted equivalent fitting based on factors such as location proximity, altitude, and temperature. Finally, the control signal and redundant data input system writes the converted data to the faulty turbine location. This allows for the acquisition of operational data between wind turbines and corresponding control without adding extra hardware, ensuring that the wind turbines can operate normally even when sensor equipment is damaged, preventing prolonged shutdowns and improving power generation efficiency. The front-end redundant control and monitoring system interface implemented through the Goahead server can combine HTML+CSS+Javascript with the data visualization tool Echarts, and use AJAX (asynchronous JavaScript and XML) to achieve partial data updates on the interface.

[0047] like Figure 1 and Figure 2 As shown, the first aspect of this application provides a method for intelligent redundant group control of wind turbine generators, comprising:

[0048] S100: Obtain the first monitoring data of the target wind turbine at the current moment;

[0049] S200. If the first monitoring data is determined to be abnormal, the second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current moment. The adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine.

[0050] S300. If the second monitoring data is determined to be normal, the first environmental data of the target wind turbine is obtained, and the second monitoring data is weighted and fitted based on the first environmental data to obtain the third monitoring data. The third monitoring data is the equivalent data of the second monitoring data under the first environmental data.

[0051] S400, using the third monitoring data as the target monitoring data for the target wind turbine at the current moment.

[0052] Thus, this application monitors whether the monitoring data of the wind turbine is abnormal. If the monitoring data is determined to be abnormal, the normal monitoring data of the wind turbine adjacent to the current wind turbine is obtained. The monitoring data of the adjacent wind turbine is weighted and fitted based on the environmental data of the current wind turbine and the adjacent wind turbine to obtain the equivalent monitoring data of the current wind turbine. This allows the monitoring data of the wind turbine to be determined when the sensor fails without adding additional sensor equipment, thereby ensuring the normal operation of the wind turbine in the event of sensor failure, avoiding long-term downtime and improving power generation efficiency.

[0053] Specifically, in step S100, monitoring data of all wind turbines in the same wind farm can be collected at pre-set monitoring times. For example, monitoring data of the wind turbines can be collected once every second. It is understood that the monitoring data of the target wind turbine can be collected by wind speed and direction sensors, temperature sensors, etc., installed on the target wind turbine. The first monitoring data includes at least one or more of wind speed, wind direction, and ambient temperature. In this application, the first monitoring data includes the wind speed, wind direction, and ambient temperature corresponding to the target wind turbine.

[0054] In step S200, after acquiring the first monitoring data of the target wind turbine, the system first determines whether the first monitoring data is abnormal. Determining that the first monitoring data is abnormal includes: if the wind speed of the target wind turbine at the current moment is the same as the historical wind speed of the target wind turbine at the previous N moments, then the first monitoring data is determined to be abnormal. For example, taking the data acquisition frequency as times / second, after acquiring the wind speed of the target wind turbine at the current moment, the acquired wind speed at the current moment is compared with the wind speed of the target wind turbine in the previous 29 seconds. If the wind speed at the current moment is the same as the wind speed in the previous 29 seconds, that is, the wind speed acquired by the anemometer of the target wind turbine has remained unchanged for 30 seconds, then it can be determined that the anemometer of the target wind turbine is faulty, and the first monitoring data is abnormal.

[0055] Similarly, if the wind direction of the target wind turbine at the current moment and the historical wind direction of the target wind turbine at the previous N moments are the same, the first monitoring data is determined to be abnormal. After collecting the wind direction of the target wind turbine at the current moment, the collected wind direction at the current moment is compared with the wind direction of the target wind turbine in the previous 29 seconds. If the wind direction at the current moment is the same as the wind direction in the previous 29 seconds, that is, the wind direction collected by the wind vane of the target wind turbine has not changed for 30 seconds, then it can be determined that the wind vane of the target wind turbine is faulty, and the first monitoring data is abnormal.

[0056] If the first monitoring data includes ambient temperature, then the first monitoring data is determined to be abnormal. It also includes determining the average ambient temperature of all wind turbines in the wind turbine group (excluding the target wind turbine) at the current moment. If the difference between the ambient temperature of the target wind turbine at the current moment and the obtained average ambient temperature reaches an ambient temperature difference threshold, the first monitoring data is determined to be abnormal. For example, if the current wind farm includes wind turbine 1, wind turbine 2, wind turbine 3, ..., wind turbine N, and wind turbine 1 is determined to be the target wind turbine, after collecting the ambient temperature t1 of wind turbine 1 at the current moment, the ambient temperatures t2 to tN of wind turbines 2 to N at the current moment are obtained, and the average value t0 of t2 to tN is calculated. t1 is compared with t0. If the difference between t1 and t0 is greater than the preset ambient temperature difference threshold, that is, the temperature collected at wind turbine 1 differs too much from the temperature of other wind turbines in the wind farm, then the temperature sensor of wind turbine 1 can be judged to be faulty.

[0057] Understandably, in step S200, the adjacent wind turbines of the target wind turbine can be determined based on the actual layout of the wind farm and the characteristic information of the target wind turbine. For example, one or more wind turbines in the same wind farm that are closest to the target wind turbine can be considered as adjacent wind turbines; in this application, there are two adjacent wind turbines. Another example is that wind turbines with similar altitudes to the target wind turbine can be considered as adjacent wind turbines. For instance, among wind turbines whose altitude difference from the target wind turbine is within a set threshold, the two wind turbines closest to the target wind turbine are identified as adjacent wind turbines. The second monitoring data has the same elements as the first monitoring data, namely, it includes the wind speed, wind direction, and ambient temperature corresponding to the adjacent wind turbines.

[0058] In step S300, after acquiring the second monitoring data of adjacent wind turbines, it is necessary to determine whether the second monitoring data is abnormal. Taking two adjacent wind turbines as an example, if the collected second monitoring data determines that the peripherals of both adjacent wind turbines are in normal operating condition, then the second monitoring data of both adjacent wind turbines are collected. If the peripherals of one turbine are damaged, then the second monitoring data of the other turbine is collected. If the peripherals of both adjacent wind turbines are damaged, then the second monitoring data of both adjacent wind turbines are not collected, and redundant control of the external equipment of the target wind turbine is stopped. It can be understood that if the external equipment of both adjacent wind turbines is in normal operating condition, then the second monitoring data of either adjacent wind turbine is used as the basis for calculating the target detection data of the target wind turbine, or the average of the second monitoring data of the two adjacent wind turbines is used as the basis for the basis monitoring data. If only the external equipment of one adjacent wind turbine is in normal operating condition, then the second monitoring data corresponding to that adjacent wind turbine is used as the basis for the basis monitoring data.

[0059] After determining the basic monitoring data used for conversion, the weighting coefficients for data conversion are first determined based on the first environmental data. The first environmental data includes at least the location and altitude of the target wind turbine, where the location can be the coordinates of the target wind turbine or the distance between the target wind turbine and adjacent wind turbines. The second monitoring data is then weighted and fitted based on the first environmental data to obtain the third monitoring data, which includes:

[0060] The weighting coefficients of the first environmental data are determined through a pre-defined relationship table. This table includes at least the weighting coefficients for wind speed, wind direction, or ambient temperature at different locations and altitudes. The impact of wind turbines on wind speed, wind direction, or ambient temperature at different altitudes can be determined beforehand through experiments or simulations. Based on the impact of different altitudes on different parameters, the weighting coefficients corresponding to different altitudes are determined. Similarly, the impact of wind turbines at different locations within the wind farm on wind speed, wind direction, or ambient temperature can be determined, and the weighting coefficients corresponding to different locations for different parameters can be established. It is understood that if the first stage data includes both the location and altitude of the wind turbines, a comprehensive weighting coefficient corresponding to the location and altitude for different parameters can be obtained through simulation experiments. Alternatively, the weighting coefficients for location and altitude for different parameters can be obtained separately, and a comprehensive weighting coefficient can be obtained by weighted fitting of these weighting coefficients. This approach is not limited here.

[0061] In a specific instance, the steps for constructing a relational table include:

[0062] S1. Obtain the first average wind speed of the target wind turbine and the second average wind speed of each wind turbine adjacent to the target wind turbine within a preset monitoring period. Taking a group of three adjacent wind turbines (unit 1, unit 2, and unit 3) as an example, where unit 1 is the target wind turbine and units 2 and 3 are adjacent wind turbines of unit 1, the average wind speed data of units 1, 2, and 3 within 10 minutes are collected under the condition that the wind speed acquisition equipment of the three units is normal. These average wind speeds are recorded as the wind speed of unit 1, unit 2, and unit 3, respectively. It can be understood that the wind speed of unit 1 is the first average wind speed, and the wind speeds of units 2 and 3 are the second average wind speeds.

[0063] S2. Calculate the wind speed ratio between each of the first average wind speed and all the second average wind speeds and the remaining average wind speeds. Specifically, calculate the wind speed ratios of Group 1 to Group 2, Group 1 to Group 3, Group 2 to Group 1, Group 2 to Group 3, Group 3 to Group 1, and Group 3 to Group 2. This will give you the ratio of the wind speeds of the three units over 10 minutes.

[0064] S3. Divide the 10-minute average wind speed of each unit into wind speed segments. For example, based on the 10-minute average wind speed of each unit, the wind speed segments can be divided into 3m-4m, 4m-5m, 5m-6m, and so on. Calculate the ratio of the average wind speed of any two units in different wind speed segments over N preset monitoring periods, such as within 1 hour, thereby constructing a table showing the ratio relationship of different wind speeds corresponding to different wind turbine units, i.e., different locations and altitudes, i.e., the weighting coefficient relationship table. Specifically, the hourly wind speed ratios of Unit 1, Unit 2, and Unit 3 in each wind speed range are statistically analyzed. Taking Unit 1 as an example, the hourly wind speed ratios of Unit 1 and Unit 2 and Unit 3 in the 3m-4m wind speed range are statistically analyzed for each of the six preset monitoring cycles. For example, the wind speed ratios of Unit 1 and Unit 2 in the first 10 minutes, ..., and the sixth 10 minutes are wind speed ratio 1, wind speed ratio 2, wind speed ratio 3, wind speed ratio 4, wind speed ratio 5, and wind speed ratio 6, respectively. The average hourly wind speed ratio of Unit 1 and Unit 2 in the 3m-4m wind speed range is calculated as (wind speed ratio 1 + wind speed ratio 2 + wind speed ratio 3 + wind speed ratio 4 + wind speed ratio 5 + wind speed ratio 6) / 6. By analogy, the ratio of each unit's average hourly wind speed to that of adjacent units in different wind speed ranges is calculated. This allows the construction of a matrix relationship between each unit and the other two adjacent units for the ratio of wind speed to average wind speed in each wind speed range. If the anemometer of one unit fails, the wind speed of one or two units can be used to perform an equivalent conversion within each wind speed range. Understandably, the longer the units have been operating, the more accurate the proportionality coefficient will be. Therefore, in this application, the ratio of average wind speed of different units in different wind speed ranges includes, but is not limited to, the ratio of average wind speed over one hour.

[0065] Similarly, the above method can be used to obtain the average temperature ratio of unit 1 to unit 2 and unit 3 in different temperature ranges, and the average temperature ratio of the unit to the adjacent units per hour. The temperature range can be divided into intervals of 3°C in the range of -30°C to 40°C. The calculation process of the average temperature ratio of the target unit and its adjacent units in different temperature ranges is the same as that of the average wind speed ratio, and will not be repeated here.

[0066] Similarly, this application can also use the above method to obtain the average wind direction ratio of unit 1 with unit 2 and unit 3 in different wind direction segments, and the average wind direction ratio of adjacent units per hour. The wind direction needs to be divided into wind direction deviation value segments. For example, in the range of -30° to 30°, the wind direction segments are divided into intervals of 2°. The calculation process of the average wind direction ratio of the target unit and its adjacent units in different wind direction segments is the same as that of the average wind speed ratio, and will not be repeated here.

[0067] Through the above steps, relationship tables are constructed for different wind turbine units (i.e., different locations and altitudes) with adjacent wind turbine units at different wind speed ranges, different wind turbine units with different average temperature ranges, and different wind turbine units with different average wind direction ranges. Therefore, when performing data conversion, it is only necessary to obtain the weighting coefficients (ratios) between the target wind turbine unit and its adjacent wind turbine units from the constructed relationship tables based on the wind speed, wind direction, or ambient temperature range to which the adjacent wind turbine units belong. The monitoring data of the adjacent wind turbine units can then be converted. The converted wind speed, wind direction, and ambient temperature are used as the third monitoring data for the target wind turbine unit. The equivalent operating data of the target wind turbine unit is then replaced with the fault data (i.e., the first monitoring data of the abnormality) through the Web_SviWrite process in the GoForms process of the Goahead server, ensuring that the target wind turbine unit can operate normally without prolonged downtime. Understandably, if the third monitoring data, calculated based on the second monitoring data of the adjacent wind turbine, is still abnormal, the target wind turbine will be shut down to facilitate inspection and maintenance.

[0068] like Figure 3 As shown, in a second aspect of this application, a smart redundant group control device for wind turbine generators is provided, characterized in that it includes:

[0069] The data acquisition module is configured to acquire the first monitoring data of the target wind turbine at the current moment;

[0070] If the first monitoring data is determined to be abnormal, second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current moment, wherein the adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine; and

[0071] If the second monitoring data is confirmed to be normal, the first environmental data of the target wind turbine unit is obtained;

[0072] The data correction module is configured to perform a weighted fitting of the second monitoring data based on the first environmental data to obtain the third monitoring data, which is the equivalent data of the second monitoring data under the first environmental data; and

[0073] The third monitoring data is used as the target monitoring data for the target wind turbine at the current moment.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] In a third aspect, this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned intelligent redundancy group control method for wind turbine generators.

[0076] In a fourth aspect, this application provides a terminal device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned intelligent redundant group control method for wind turbine generators.

[0077] like Figure 4 The diagram shown is a schematic representation of a terminal device provided in an embodiment of this application. Figure 4 As shown, the terminal device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the above method embodiments. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the above device embodiments.

[0078] For example, computer program 102 may be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 102 in terminal device 10.

[0079] Terminal device 10 may be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. Terminal device 10 may include, but is not limited to, a processor 100 and a memory 101. Those skilled in the art will understand that... Figure 4 This is merely an example of terminal device 10 and does not constitute a limitation on terminal device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, terminal device may also include input / output devices, network access devices, buses, etc.

[0080] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0081] The memory 101 can be an internal storage unit of the terminal device 10, such as a hard disk or RAM of the terminal device 10. The memory 101 can also be an external storage device of the terminal device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal device 10. Furthermore, the memory 101 can include both internal and external storage units of the terminal device 10. The memory 101 is used to store computer programs and other programs and data required by the terminal device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] In summary, this application achieves fully automatic control of wind turbine units through the monitoring, identification, calculation, and substitution of abnormal data, avoiding a series of drawbacks associated with manual switching operations. It significantly reduces prolonged downtime caused by external equipment failures in harsh environments, thereby improving the economic benefits of the wind turbine units while ensuring their safe and stable operation. Furthermore, this application effectively reduces the number of external redundant devices, further lowering the overall manufacturing cost. Additionally, this application is simple to use; the front-end redundant control and monitoring system only requires adding corresponding functions to the ring network computer, and can directly utilize the industrial control display screen at the base of the wind turbine tower, eliminating the need for additional equipment costs. The data interaction method with adjacent turbine locations in this application is simple and reliable, requiring no additional communication equipment for adjacent turbine locations.

[0084] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0085] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for intelligent redundant group control of wind turbine generators, characterized in that, include: Obtain the first monitoring data of the target wind turbine at the current moment; If the first monitoring data is determined to be abnormal, the second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current time, wherein the adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine. If the second monitoring data is determined to be normal, the first environmental data of the target wind turbine is obtained; The second monitoring data is weighted and fitted based on the first environmental data to obtain the third monitoring data, which is the equivalent data of the second monitoring data under the first environmental data. The third monitoring data is used as the target monitoring data of the target wind turbine at the current moment; The first environmental data includes at least the location and altitude of the target wind turbine. Based on the first environmental data, the second monitoring data is weighted and fitted to obtain the third monitoring data, which includes: The weighting coefficients of the first environmental data are determined by a preset relationship table, which includes at least the weighting coefficients of wind speed, wind direction or ambient temperature corresponding to different locations and altitudes. The wind speed, wind direction or ambient temperature of the adjacent wind turbines at the current moment are weighted and fitted using the weighting coefficient of the first environmental data, and one or more of the weighted and fitted wind speed, wind direction and ambient temperature are determined as the third monitoring data. The steps for constructing the relation table include: S1. Obtain the first average wind speed of the target wind turbine and the second average wind speed of each wind turbine adjacent to the target wind turbine within a preset monitoring period. S2. Calculate the wind speed ratio between the first average wind speed and each of the second average wind speeds and the remaining average wind speeds respectively. S3. Divide the average wind speed of each unit into wind speed segments, calculate the ratio of the average wind speed of any two units in N preset monitoring cycles in different wind speed segments, and obtain a table showing the relationship of weighting coefficients for different wind speeds corresponding to different locations and altitudes of different wind turbine units.

2. The intelligent redundant group control method for wind turbine generators according to claim 1, characterized in that, The first monitoring data and the second monitoring data include at least: One or more of wind speed, wind direction, and ambient temperature.

3. The intelligent redundant group control method for wind turbine generators according to claim 2, characterized in that, Determining that the first monitoring data is abnormal includes: If the wind speed of the target wind turbine at the current moment is the same as the historical wind speed of the target wind turbine at the previous N moments, the first monitoring data is determined to be abnormal.

4. The intelligent redundant group control method for wind turbine generators according to claim 2, characterized in that, If the wind direction of the target wind turbine at the current moment is the same as the historical wind direction of the target wind turbine at the previous N moments, the first monitoring data is determined to be abnormal.

5. The intelligent redundant group control method for wind turbine generators according to claim 2, characterized in that, Determining that the first monitoring data is abnormal includes: Determine the average ambient temperature of all wind turbines in the wind turbine group, excluding the target wind turbine, at the current moment. If the difference between the ambient temperature of the target wind turbine at the current moment and the obtained average ambient temperature reaches the ambient temperature difference threshold, the first monitoring data is determined to be abnormal.

6. A wind turbine intelligent redundant group control device, employing the wind turbine intelligent redundant group control method according to any one of claims 1 to 5, characterized in that, The device includes: The data acquisition module is configured to acquire the first monitoring data of the target wind turbine at the current moment; If the first monitoring data is determined to be abnormal, second monitoring data of at least one adjacent wind turbine of the target wind turbine is obtained at the current moment, wherein the adjacent wind turbines of the target wind turbine belong to the same wind turbine group as the target wind turbine; and If the second monitoring data is determined to be normal, the first environmental data of the target wind turbine is obtained; A data correction module is configured to perform a weighted fitting of the second monitoring data based on the first environmental data to obtain third monitoring data, wherein the third monitoring data is the equivalent data of the second monitoring data under the first environmental data; and The third monitoring data is used as the target monitoring data of the target wind turbine at the current moment.

7. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions that, when executed by a processor, cause the processor to be configured to perform the intelligent redundancy group control method for wind turbines as described in any one of claims 1 to 5.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent redundant group control method for wind turbine generators as described in any one of claims 1 to 5.

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