A fan optimization control method and system based on dynamic meteorological data

Abstract

This invention relates to the field of wind turbine control technology, and discloses a wind turbine optimization control method and system based on dynamic meteorological data. The method includes: acquiring real-time meteorological information around the wind turbine; constructing a wind speed prediction model based on the real-time meteorological information to predict wind speed; and adjusting the wind turbine's operating parameters according to the predicted wind speed change trend to achieve optimized wind turbine control. This invention acquires real-time meteorological information including wind speed, wind direction, temperature, and humidity, predicts short-term wind speed change trends based on a constructed LSTM deep learning model, and dynamically adjusts the wind turbine's operating parameters in conjunction with a fluid dynamics model. This invention can optimize the wind turbine's pitch system angle and speed gain in real time, ensuring the rotor always maintains the optimal windward angle, thereby improving wind energy capture efficiency and reducing equipment load.

Description

Technical Field

[0001] This invention relates to the field of wind turbine control technology, specifically to a wind turbine optimization control method and system based on dynamic meteorological data. Background Technology

[0002] Wind power generation, as a clean and renewable energy technology, has been widely used globally, and the operation and control technology of wind turbine generators has also developed rapidly. Existing technologies typically employ traditional methods based on fixed control parameters to regulate wind turbine operation in response to changes in environmental conditions such as wind speed and direction. However, wind resources themselves are inherently intermittent and uncertain, and the operating status of wind turbines is affected by complex meteorological conditions, such as temperature changes and increased humidity, which can lead to fluctuations in turbine power output and increase equipment fatigue load. To address these issues, in recent years, wind turbine operation and control technology has gradually developed towards intelligence and self-adaptation. Some studies have attempted to dynamically adjust wind turbine operating parameters by monitoring environmental parameters such as wind speed and direction in real time. However, most current control strategies are limited to simple proportional-integral-derivative (PID) control or static optimization models, which are insufficient to adapt to complex and changing wind conditions and cannot fully exploit the potential of wind resources. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by this invention is that traditional wind turbine control technology has a slow response speed when faced with large wind speed changes or complex weather conditions, making it difficult to adjust operating parameters in real time.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wind turbine optimization control method based on dynamic meteorological data, comprising: acquiring real-time meteorological information around the wind turbine; constructing a wind speed prediction model based on the real-time meteorological information to predict the wind speed; and adjusting the operating parameters of the wind turbine according to the predicted wind speed change trend to achieve optimized wind turbine control.

[0006] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the real-time meteorological information includes wind speed, wind direction, temperature, and humidity data.

[0007] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the wind turbine load prediction model includes: constructing an LSTM model and inputting historical data on wind speed, wind direction, temperature, and humidity; using the model for real-time training and prediction to predict the wind speed change trend within the next 15 minutes; and combining the prediction results to calculate the dynamic change of wind energy capture efficiency and using it as the basis for subsequent adjustments.

[0008] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the adjustment of the wind turbine's operating parameters includes: dynamically adjusting the angle of the pitch system; calculating the optimal windward angle using a fluid dynamics model based on wind speed and direction data; controlling the servo motor to adjust the pitch angle in real time to keep the wind turbine within the optimal angle range with the wind direction; and adjusting the rate of change of the pitch angle through an optimized control algorithm when the wind speed changes drastically to ensure that its adjustment is synchronized with the wind speed change.

[0009] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the control servo motor includes: inputting real-time wind speed and wind direction data to the main controller; calculating the optimal windward angle through a fluid dynamics optimization module; generating a command signal for the servo motor based on the calculation results; and dynamically adjusting the servo motor when the wind speed changes drastically or exceeds the safe range to ensure the stability of the angle adjustment process.

[0010] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the adjustment of the servo motor includes: dynamically adjusting the generator gain parameters using a PID controller according to the wind speed change trend; increasing the gain parameters to improve the generator's speed response capability when the wind speed increases; decreasing the gain parameters to reduce mechanical stress and energy loss when the wind speed decreases; and optimizing the speed gain parameters in advance based on short-term wind speed forecast results to improve the accuracy and efficiency of the response.

[0011] As a preferred embodiment of the wind turbine optimization control method based on dynamic meteorological data described in this invention, the adjustment servo motor further includes controlling the output power of the frequency converter, dynamically adjusting the frequency output of the frequency converter, and optimizing power distribution according to real-time load requirements; when the wind speed is low, the frequency converter maintains minimum power output through its low-speed mode; when the wind speed is stable and reaches the optimal operating range, the frequency converter is switched to high-efficiency mode to improve the wind turbine output efficiency; during the output power adjustment process, the generator vibration signal and temperature changes are monitored in real time to ensure operational stability.

[0012] A wind turbine optimization control system based on dynamic meteorological data using any of the methods described in this invention, comprising: a data acquisition module for acquiring real-time meteorological information around the wind turbine; a prediction module for constructing a wind speed prediction model based on the real-time meteorological information to predict wind speed; and a control module for adjusting the wind turbine's operating parameters according to the predicted wind speed change trend to achieve optimized wind turbine control.

[0013] A computer device includes: a memory and a processor; the memory stores a computer program, including: the steps of the processor executing the computer program to implement the method described in any one of the present invention.

[0014] A computer-readable storage medium having a computer program stored thereon, comprising the steps of implementing the method described in any one of the present invention when the computer program is executed by a processor.

[0015] The beneficial effects of this invention are as follows: This method acquires meteorological information in real time, including wind speed, wind direction, temperature, and humidity. Based on a constructed LSTM deep learning model, it predicts the short-term trend of wind speed changes and dynamically adjusts the wind turbine's operating parameters by combining this with a fluid dynamics model. This invention can optimize the pitch system angle and speed gain of the wind turbine in real time, ensuring that the rotor always maintains the optimal windward angle, thereby improving wind energy capture efficiency and reducing equipment load. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0017] Figure 1 The above is an overall flowchart of a wind turbine optimization control method based on dynamic meteorological data, provided as an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0019] Example 1, referring to Figure 1 As an embodiment of the present invention, a wind turbine optimization control method based on dynamic meteorological data is provided, comprising:

[0020] S1: Obtain real-time weather information around the wind turbine.

[0021] Real-time meteorological data is acquired through a high-precision sensor network. Sensors are deployed at key locations on the wind turbine, including the top of the nacelle, the blade roots, and near the rotor, to ensure comprehensive and accurate data. Wind speed and direction data are collected by ultrasonic sensors or mechanical anemometers, providing high-frequency instantaneous values ​​and trends. This data is transmitted in real-time to the main control system's communication module, providing dynamic support for subsequent adjustments to wind turbine operating parameters. Temperature and humidity data are acquired through precision environmental sensors to monitor the thermodynamic properties of the air and humidity changes, ensuring the system's adaptability to complex meteorological conditions.

[0022] During data acquisition, sensors achieve synchronous data collection at a uniform sampling frequency (typically 5 to 10 times per second) to avoid affecting the accuracy of the prediction model due to data asynchrony. The acquired data is preprocessed using a built-in filtering algorithm to remove outliers and noise interference. The processed data is then transmitted in real-time to the wind turbine's main control system via a wireless transmission module, ensuring efficient and stable data transmission. Simultaneously, the system timestamps the collected wind speed, wind direction, temperature, and humidity data for subsequent data fusion analysis.

[0023] It should be noted that wind speed is the core variable in wind power generation, determining the magnitude and stability of the turbine's power output; wind direction directly affects the rotor's angle of attack and wind energy capture efficiency. Obtaining real-time wind speed and direction data is a fundamental prerequisite for dynamically adjusting the turbine's operating parameters.

[0024] Temperature not only affects the accuracy of wind speed prediction models, but also impacts the operating status of critical wind turbine components such as generators and gearboxes. Extreme temperatures can cause thermal expansion or contraction of components, thereby affecting the normal operation of the wind turbine.

[0025] Humidity data reflects the water content in the air, a parameter that is important for predicting blade icing that may occur in low-temperature environments. Furthermore, changes in humidity can affect the dynamic viscosity of the air, thus influencing the aerodynamic characteristics of the wind turbine.

[0026] By acquiring multi-dimensional meteorological information including wind speed, wind direction, temperature, and humidity, this invention provides comprehensive data support for subsequent wind speed prediction models, avoiding the large prediction errors caused by relying solely on wind speed and direction data in existing technologies. The fusion of multi-dimensional data enables wind turbine operating parameter adjustments to have higher accuracy and robustness, thereby significantly improving wind energy utilization efficiency.

[0027] Compared with existing technologies, this invention solves the problems of prediction errors and low wind turbine control efficiency caused by incomplete meteorological data. By incorporating temperature and humidity data, the wind speed prediction model of this invention can better cope with changes in complex meteorological conditions, such as changes in aerodynamic characteristics caused by high humidity and performance degradation of mechanical components under extreme temperatures.

[0028] S2: Construct a wind speed prediction model based on real-time meteorological information to predict wind speed.

[0029] The wind turbine load prediction model includes: constructing an LSTM model, inputting historical data on wind speed, wind direction, temperature, and humidity; using the model for real-time training and prediction to predict the wind speed change trend within the next 15 minutes; and combining the prediction results to calculate the dynamic change of wind energy capture efficiency, which is then used as the basis for subsequent adjustments.

[0030] Furthermore, by using an LSTM model for real-time wind speed prediction, this invention introduces artificial intelligence technology into the field of wind turbine control, solving the problem that traditional methods struggle to handle complex nonlinear meteorological data. Traditional prediction methods (such as those based on linear regression or simple statistical analysis) exhibit significant limitations when dealing with dynamic changes in multiple variables, especially under conditions of high fluctuating wind speeds, where they are prone to large errors. The introduction of the LSTM model significantly improves the accuracy and reliability of wind speed prediction, enabling more precise adjustments to wind turbine operating parameters.

[0031] In this embodiment, the input variables include wind speed, wind direction, temperature, and humidity. These variables are highly correlated with the dynamic changes in wind speed, providing comprehensive environmental information support for the model.

[0032] The LSTM model is trained offline using a large amount of historical meteorological data to obtain initial model parameters. In actual operation, the latest real-time meteorological data is used for online training to continuously update the model weights to adapt to the dynamic changes in meteorological conditions.

[0033] Input gates, forget gates, and output gates control the flow of information, adapting to the temporal characteristics of meteorological data. Hidden states and cell states jointly store historical information. Multi-layer stacked LSTMs (e.g., 2-3 layers) are used to enhance the model's ability to capture nonlinear dynamic relationships.

[0034] Using a trained LSTM model as input to the current multidimensional meteorological data sequence, the model predicts the wind speed trend over the next T time steps. The model automatically adjusts the prediction range to cope with different wind speed change patterns (such as low-frequency stability or high-frequency fluctuations).

[0035] Based on the predicted wind speed, combined with the power curve and fluid dynamics model of the wind turbine, the future wind energy capture efficiency is dynamically calculated. According to the predicted wind speed changes and wind energy capture efficiency, the wind turbine operating parameters are optimized and adjusted.

[0036] S3: Adjust the operating parameters of the fan according to the predicted wind speed change trend to achieve optimized fan control.

[0037] Furthermore, the adjustment of the wind turbine's operating parameters includes dynamically adjusting the pitch system angle, calculating the optimal windward angle using a fluid dynamics model based on wind speed and direction data; controlling the servo motor to adjust the pitch angle in real time to keep the wind turbine within the optimal angle range with the wind direction; and adjusting the pitch angle change rate through an optimized control algorithm when the wind speed changes drastically to ensure that its adjustment is synchronized with the wind speed change.

[0038] The control servo motor includes inputting real-time wind speed and direction data to the main controller, calculating the optimal windward angle through a fluid dynamics optimization module, generating command signals for the servo motor based on the calculation results, and dynamically adjusting the servo motor when the wind speed changes drastically or exceeds the safe range to ensure the stability of the angle adjustment process.

[0039] The adjustment of the servo motor includes dynamically adjusting the generator's gain parameters using a PID controller based on wind speed change trends; when the wind speed increases, increasing the gain parameters enhances the generator's speed response capability; when the wind speed decreases, reducing the gain parameters reduces mechanical stress and energy loss; and combining short-term wind speed forecast results to optimize the speed gain parameters in advance, improving the accuracy and efficiency of the response.

[0040] The adjustment of the servo motor also includes controlling the output power of the frequency converter, dynamically adjusting the frequency output of the frequency converter, and optimizing power distribution according to real-time load requirements; when the wind speed is low, the frequency converter maintains minimum power output through the low-speed mode; when the wind speed is stable and reaches the optimal operating range, the frequency converter is switched to high-efficiency mode to improve the output efficiency of the fan; during the output power adjustment process, the generator vibration signal and temperature change are monitored in real time to ensure operational stability.

[0041] Example 2, in an exemplary embodiment, also provides a wind turbine optimization control system based on dynamic meteorological data, including a data acquisition module, a prediction module, and a control module.

[0042] The data acquisition module obtains real-time meteorological information around the wind turbine.

[0043] The prediction module constructs a wind speed prediction model based on real-time meteorological information to predict wind speed.

[0044] The control module adjusts the operating parameters of the fan based on the predicted wind speed change trend to achieve optimized fan control.

[0045] If the above functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0046] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0047] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0048] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wind turbine optimization control method based on dynamic meteorological data, characterized in that, include: Obtain real-time weather information around the wind turbine; A wind speed prediction model is constructed based on real-time meteorological information to predict wind speed. Adjust the operating parameters of the wind turbine according to the predicted wind speed change trend to achieve optimized wind turbine control; The wind speed prediction model includes constructing an LSTM model and inputting historical data on wind speed, wind direction, temperature, and humidity. The model is used for real-time training and prediction to predict wind speed trends over the next 15 minutes. The dynamic changes in wind energy capture efficiency are calculated based on the forecast results, and this is used as the basis for subsequent adjustments. The adjustment of the wind turbine's operating parameters includes dynamically adjusting the angle of the pitch system and calculating the optimal windward angle using a fluid dynamics model based on wind speed and direction data. The servo motor is controlled to adjust the pitch angle in real time, so that the wind turbine is kept within the optimal angle range with the wind direction; When wind speed changes drastically, the rate of change of the pitch angle is adjusted by optimizing the control algorithm to ensure that the adjustment is synchronized with the wind speed change. The control servo motor includes, Real-time wind speed and direction data are input to the main controller, and the optimal windward angle is calculated through the fluid dynamics optimization module. The command signal for the servo motor is generated based on the calculation results; When wind speed changes drastically or exceeds the safe range, the servo motor is dynamically adjusted to ensure the stability of the angle adjustment process. The adjustment servo motor includes, Based on the wind speed change trend, the gain parameters of the generator are dynamically adjusted using a PID controller; When the wind speed increases, the generator's speed response capability can be improved by increasing the gain parameter; When the wind speed decreases, reduce the gain parameter to reduce mechanical stress and energy loss; By combining short-term wind speed forecast results, the rotational speed gain parameters can be optimized in advance to improve the accuracy and efficiency of the response; The adjustment servo motor also includes controlling the output power of the frequency converter, dynamically adjusting the frequency output of the frequency converter, and optimizing power distribution according to real-time load requirements. When the wind speed is low, the inverter maintains minimum power output through the low-speed mode. When the wind speed is stable and reaches the optimal operating range, switch the frequency converter to high-efficiency mode to improve the output efficiency of the fan. During the output power adjustment process, the generator vibration signal and temperature change are monitored in real time to ensure operational stability.

2. The wind turbine optimization control method based on dynamic meteorological data as described in claim 1, characterized in that: The real-time meteorological information includes wind speed, wind direction, temperature, and humidity data.

3. A wind turbine optimization control system based on dynamic meteorological data, employing the method described in any one of claims 1 to 2, characterized in that, include, The data acquisition module obtains real-time meteorological information around the wind turbine. The prediction module builds a wind speed prediction model based on real-time meteorological information to predict wind speed. The control module adjusts the operating parameters of the fan based on the predicted wind speed change trend to achieve optimized fan control.

4. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the wind turbine optimization control method based on dynamic meteorological data as described in any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the wind turbine optimization control method based on dynamic meteorological data as described in any one of claims 1-2.

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