wind power generation

The application of automation in wind power generation is extensive and profound, primarily reflected in the following aspects: wind turbine control, power regulation, power filtering, remote monitoring and fault diagnosis, data acquisition and supervisory control systems, as well as the application of intelligent technologies. Here is a detailed summary of these aspects:

1. Wind Turbine Control

• Enhancing Stability through Automation: Electrical automation technology improves the autonomy and adaptability of wind turbine control systems, enabling them to respond effectively to complex and changing environmental conditions, thereby increasing wind power generation.
• Specific Control Processes: The wind turbine control system covers multiple processes such as startup, operation, and shutdown. Electrical automation technology allows these processes to automatically respond to environmental changes such as wind speed and direction. For example, when the rotational speed of the wind turbine exceeds the maximum limit, the wind power generator automatically disconnects from the grid, and the blades open promptly to implement soft braking, stopping the blades from rotating and effectively limiting speed and performing emergency shutdowns.
• Yaw and Cable Unwinding Control: By applying automatic control systems, yaw and cable unwinding can be controlled automatically. Due to uncertain wind directions, continuous tracking may cause the cables to become entangled. The automatic control system can perform cable unwinding functions to ensure the cables remain untangled.

2. Power Regulation

• Maximum Power Point Tracking (MPPT) Control: Modern wind turbines use MPPT systems to regulate output power. Electrical automation technology monitors and analyzes the real-time output of wind turbines, adjusting MPPT system parameters to accurately track the maximum power point and improve wind power utilization efficiency.
• Inverters and Soft Starters: Power regulation in wind power generation relies on inverters and soft starters. For example, Siemens wind turbines in Germany have inverters with response times of less than 0.2 seconds, enabling power regulation across a range of 20% to 100%.

3. Power Filtering

• Filtering Harmonics and Interference Signals: Due to the instability and variability of wind energy, wind turbines generate many harmonics and interference signals during power generation, affecting turbine efficiency and grid stability. Electrical automation technology monitors and analyzes the real-time output current and voltage of wind turbines, filtering out harmonics and interference signals to ensure turbine efficiency and grid stability.

4. Remote Monitoring and Fault Diagnosis

• Remote Monitoring Centers: By establishing remote monitoring centers, wind turbines can be monitored and analyzed in real-time, enabling the timely detection of potential faults and early warnings.
• Fault Diagnosis Technologies: Fault diagnosis technologies are used to intelligently analyze the operational status of turbines, providing maintenance personnel with fault location and solutions. For example, by comparing monitoring data with equipment logs, faults such as abnormal components within inverters can be located.

5. Data Acquisition and Supervisory Control Systems

• Data Acquisition Layer: Sensors (such as wind speed sensors and vibration sensors) collect real-time data, providing basic data for the control system. For example, a wind farm in Germany has installed 200 sensors on its wind turbines, transmitting 100,000 data points per second.
• Control Layer: PLCs (Programmable Logic Controllers) and DCSs (Distributed Control Systems) are used to adjust the operational status of wind turbines in real-time based on inputs from the data acquisition layer, achieving optimal power generation.
• Application Layer: Provides a visual interface and remote monitoring functions, displaying the real-time status of wind turbines and supporting remote fault diagnosis. It also offers data analysis and optimization functions to help operation and maintenance personnel better manage wind farms.

6. Application of Intelligent Technologies

• Intelligent Sensors and Data Acquisition Technologies: Intelligent sensors monitor the operational status of wind turbines in real-time, providing accurate data for the control system. Data acquisition systems transmit data via industrial Ethernet, ensuring efficient real-time monitoring.
• Edge Computing Technologies: By processing data near the data source, edge computing reduces data transmission latency and improves the response speed of the control system. For example, Goldwind’s wind power cloud platform in China reduces data processing time from 100 milliseconds to 10 milliseconds through edge computing nodes.
• Fuzzy Logic and Artificial Intelligence Control Algorithms: Fuzzy logic control algorithms use expert rules for power regulation, neural network algorithms predict wind changes, and reinforcement learning optimizes control strategies. These algorithms enable the control system to better adapt to complex and changing wind environments, improving power generation efficiency.