Wind power prediction power data acquisition and transmission device
By installing sensors such as strain gauges, gyroscopes, and lidar on wind turbine blades, and combining signal conditioning and data processing, the problem of incomplete data acquisition by data acquisition devices has been solved, enabling accurate prediction of wind power and improving the stability and economy of the power system.
Patent Information
- Application Number
- CN202520210672.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-10
- Publication Date
- 2026-01-13
- Estimated Expiration
- 2035-02-10
AI Technical Summary
Traditional data acquisition devices obtain only partial data and cannot accurately predict wind power output.
Strain gauges are used to measure blade strain, gyroscopes to measure rotation angle and angular velocity, and lidar to measure vertical profile data. The data is then connected to a microcontroller via a signal conditioning circuit. Combined with wind vanes, anemometers, temperature sensors, and barometers, the data is cleaned, normalized, and used for wind power prediction.
By comprehensively judging multiple factors, the accuracy and timeliness of wind power forecasting have been improved, enhancing the safety, economy, and controllability of the power system.
Smart Images
Figure CN223794275U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to the field of wind power detection technology, and in particular to a wind power prediction power data acquisition and transmission device. Background Technology
[0002] Wind power forecasting technology refers to predicting the power output of wind farms over a future period to facilitate dispatch planning. Because wind energy is a stable energy source with fluctuating randomness, large-scale wind power integration into the system will inevitably pose new challenges to system stability. Timely and accurate wind power forecasting can significantly enhance the security, temperature stability, economy, and controllability of the power system.
[0003] Before making a prediction, some data needs to be acquired through data acquisition devices, such as wind direction, wind force and wind speed. However, analyzing only factors such as wind direction and wind force is not enough to make a relatively accurate prediction. More factors need to be acquired for comprehensive judgment.
[0004] Therefore, to address the above shortcomings, there is a need to provide a wind power forecasting power data acquisition and transmission device. Utility Model Content
[0005] (a) Technical problems to be solved
[0006] The technical problem this invention aims to solve is that the data acquired by traditional data acquisition devices is relatively one-sided.
[0007] (II) Technical Solution
[0008] To address the aforementioned technical problems, this utility model provides a wind power prediction power data acquisition and transmission device, comprising a microcontroller, strain gauges mounted on the blades to measure the strain of the blades under wind load, gyroscopes mounted on the blades to measure the rotation angle and angular velocity of the blades, and lidar mounted on the outer casing to measure the vertical profile data of the wind field from near the ground to high altitude. The strain gauges, gyroscopes, and lidar are connected to the microcontroller through a signal conditioning circuit to transmit the measurement data to the microcontroller.
[0009] As a further explanation of this utility model, preferably, the signal conditioning circuit includes a signal amplifier, a capacitor, and a resistor to amplify and filter the signal.
[0010] As a further explanation of this utility model, preferably, the outer cover is equipped with a wind vane for measuring wind direction and an anemometer for measuring wind speed.
[0011] As a further explanation of this utility model, preferably, a temperature sensor for measuring the external temperature and a pressure sensor for measuring the external air pressure are installed on the outer cover.
[0012] As a further explanation of this utility model, preferably, a timer is electrically connected to the microcontroller to provide reference timing and serial communication.
[0013] As a further explanation of this utility model, preferably, the microcontroller is electrically connected to a reset circuit to reset the microcontroller to its initial state.
[0014] As a further explanation of this utility model, preferably, a solar panel is fixedly connected to the outer cover to power the measuring device and the microcontroller.
[0015] As a further explanation of this utility model, preferably, the microcontroller is electrically connected to a memory to store the contents of various measurement data within different time periods.
[0016] As a further explanation of this utility model, preferably, the microcontroller is electrically connected to a wireless transmitter to wirelessly transmit the measured data to the control room and receive signals sent back from the control room.
[0017] As a further explanation of this utility model, preferably, the microcontroller is electrically connected to the speed controller on the blade to control the blade speed.
[0018] (III) Beneficial Effects
[0019] The above-mentioned technical solution of this utility model has the following advantages:
[0020] This invention measures the strain of the blades under wind load by installing strain gauges inside or on the surface of the blades, thereby measuring the wind energy capture efficiency when the blades are subjected to uneven stress. A gyroscope is also installed on the blades to measure their rotation angle and angular velocity, thus determining the impact of blade vibration on the wind turbine and improving wind power forecasting. Attached Figure Description
[0021] Figure 1 This is the logic diagram of this utility model. Detailed Implementation
[0022] To make the objectives, technical solutions, and advantages of the embodiments of this utility model clearer, the technical solutions of the embodiments of this utility model will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this utility model. Based on the embodiments of this utility model, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this utility model.
[0023] A wind power forecasting power data acquisition and transmission device, such as Figure 1As shown, the system includes a microcontroller, strain gauges mounted on the blades, gyroscopes mounted on the blades, a wind vane, an anemometer, a temperature sensor, a barometric pressure sensor, and a lidar mounted on the outer casing. The wind vane, anemometer, temperature sensor, barometric pressure sensor, strain gauge, gyroscope, and lidar are connected to the microcontroller via a signal conditioning circuit to transmit measurement data. Since the data collected from each sensor may contain noise, outliers, and missing values, the data first needs to be cleaned to remove obviously erroneous or unreasonable data. For example, for wind speed data, if negative wind speeds or values outside the normal range occur, they can be judged and corrected by comparing with historical data or using statistical methods. For missing values, interpolation methods (such as linear interpolation, polynomial interpolation, etc.) can be used to supplement the data to ensure its integrity. Data collected by different sensors have different dimensions and value ranges; to facilitate subsequent data processing and model training, the data can also be normalized according to the actual situation.
[0024] like Figure 1 As shown, the wind vane can be a potentiometer-type angle sensor. For a potentiometer-type angle sensor, based on the correspondence between its output voltage and angle, the analog voltage is converted into a digital value through an A / D converter, and then the wind direction angle is obtained through calculation.
[0025] An ultrasonic anemometer can be used to measure the time difference of ultrasonic waves propagating in two directions, and the wind speed can be calculated using known sound speeds and measurement principle formulas. The calculated wind speed data is stored in the microcontroller's memory for further processing.
[0026] Temperature sensors can be thermocouples. A dedicated thermocouple amplifier is used to amplify the thermoelectric potential and compensate for the cold junction. Then, the amplified voltage is measured by A / D conversion, and the temperature is calculated according to the thermocouple calibration table.
[0027] The barometric pressure sensor can be a piezoresistive barometric pressure sensor, which usually needs to be connected to a bridge circuit to convert the change in resistance into a change in voltage. The voltage output of the bridge is measured by the A / D conversion pin of the microcontroller, and the barometric pressure value is calculated based on the calibration data.
[0028] By utilizing vertical wind field profile data provided by lidar, a more comprehensive understanding of wind resources can be achieved. Wind turbine blades are relatively long, and wind speed and direction at different heights affect the turbine's power output. Data collected by lidar can better account for factors such as vertical wind shear, which is crucial for accurate wind power prediction, especially for large turbines (with high hub heights). Furthermore, lidar can detect distant wind conditions in advance, greatly helping to adjust turbine operating strategies and improve the timeliness of power prediction.
[0029] Strain gauges are installed inside or on the surface of the blades to measure their strain under wind loads. This data reflects the actual forces acting on the blades. When the wind force exceeds the design limits or uneven stress occurs, blade deformation occurs, affecting the wind turbine's wind energy capture efficiency and power output. By collecting this data, the operating status of the wind turbine can be analyzed more accurately, and combined with external factors such as wind speed and direction, the accuracy of wind power prediction can be improved.
[0030] The blades are equipped with gyroscopes to measure their rotation angle and angular velocity. This data is crucial for assessing blade health and operational stability. For example, excessive blade vibration can lead to energy loss and is a significant indicator of blade failure. Monitoring this data allows for the timely detection of blade anomalies. Furthermore, this data can be incorporated as characteristic parameters into wind power prediction models, playing a particularly important role when considering the impact of turbine failures or abnormal operation on power output.
[0031] The STM32 series microcontroller can be selected, which has powerful processing capabilities and rich peripheral interfaces such as timers, serial ports, SPI, and I2C, which can meet the requirements of this system. The data acquisition interval is controlled using the microcontroller's internal timer. For example, the timer is set to a 1-second interval, triggering an interrupt every 1 second, and sensor data is acquired in the interrupt service routine. By properly setting the timer's divider coefficient and initial count value, the data acquisition frequency can be precisely controlled. The microcontroller also has a reset circuit to ensure reliable reset when the system powers on, powers off, or encounters abnormal conditions. A power-on reset circuit is preferred; upon power-on, the capacitor charges, keeping the microcontroller's reset pin high for a period of time, thus achieving a reset. A solar panel is fixed to the outer casing to power the measuring devices and the microcontroller.
[0032] To store the collected data and parameters of the wind power prediction model, external memory is needed. EEPROM (Electrically Erasable Programmable Read-Only Memory) or Flash memory can be used. EEPROM offers advantages such as data retention even when power is off and ease of reading and writing, making it suitable for storing important configuration information and small amounts of historical data. Flash memory, on the other hand, features large storage capacity and low cost, and can be used to store large amounts of sensor data and model training data. The external memory is connected to the microcontroller via SPI or I2C interfaces to enable data reading and writing operations.
[0033] Common wireless transmission technologies include Wi-Fi, Bluetooth, and LoRa. Since long-distance data transmission is required but the data transmission rate requirement is not high, a LoRa module is preferred. The wireless transmission module is connected to a microcontroller via a serial port or SPI interface, and data is sent and received from a remote control room according to the corresponding communication protocol.
[0034] Meteorological data and blade data collected by sensors at different time periods are used as input features, and wind power is used as the output label to train an SVM model. By selecting an appropriate kernel function (such as radial basis function) and adjusting model parameters, the SVM model can learn the mapping relationship between input features and wind power. During training, methods such as cross-validation are used to optimize model parameters and improve the model's generalization ability. This approach, compared to analyzing only external environmental factors, also analyzes changes in blade strain and angular velocity, resulting in more accurate power predictions.
[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this utility model, and not to limit it. Although this utility model has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this utility model.
Claims
1. A wind power prediction power data acquisition and transmission device, characterized by: The single-chip microcomputer, the strain gauge installed on the blade to measure the strain of the blade under the action of wind load and reflect the actual force acting on the blade, the gyroscope installed on the blade to measure the rotation angle and angular velocity of the blade, and the laser radar installed on the outer cover to measure the vertical profile data of the wind field from the ground to high altitude are connected to the single-chip microcomputer through a signal conditioning circuit to transmit measurement data to the single-chip microcomputer.
2. The wind power prediction data acquisition and transmission device according to claim 1, characterized in that: The signal conditioning circuit is provided with a signal amplifier, a capacitor and a resistor to amplify and filter the signal.
3. The wind power prediction data acquisition and transmission device according to claim 2, characterized in that: A wind vane for measuring wind direction and an anemometer for measuring wind speed are installed on the outer cover.
4. The wind power prediction data acquisition and transmission device according to claim 3, characterized in that: A temperature sensor for measuring the temperature outside and an air pressure sensor for measuring the air pressure outside are installed on the outer cover.
5. The wind power prediction data acquisition and transmission device according to claim 4, characterized in that: A timer is electrically connected to the single-chip microcomputer to provide a reference timing and serial communication.
6. The wind power prediction data acquisition and transmission device according to claim 5, characterized in that: A reset circuit is electrically connected to the single-chip microcomputer to reset the single-chip microcomputer to the initial state.
7. The wind power prediction data acquisition and transmission device according to claim 6, characterized in that: A solar cell panel is fixedly connected to the outer cover to supply power to the measurement device and the single-chip microcomputer.
8. The wind power prediction data acquisition and transmission device according to claim 7, characterized in that: A memory is electrically connected to the single-chip microcomputer to store the contents of the measurement data at different time periods.
9. The wind power prediction data acquisition and transmission device according to claim 8, characterized in that: A wireless transmitter is electrically connected to the single-chip microcomputer to wirelessly transmit the measured data to the control room and receive the signals sent back by the control room.
10. The wind power prediction data acquisition and transmission device according to claim 9, characterized in that: The single-chip microcomputer is electrically connected to the speed regulator on the blade to control the rotation speed of the blade.