How do you design and test the control strategies for wind turbine dynamic stability?

1 Challenge 1: Nonlinear and Uncertain Dynamics

One of the main challenges for wind turbine control design is the nonlinear and uncertain nature of the wind turbine dynamics. The wind speed, direction, and turbulence vary constantly and affect the aerodynamic forces and moments on the blades and tower. The structural properties of the wind turbine, such as stiffness, damping, and mass, also change with time and temperature. These factors make it difficult to model and predict the wind turbine behavior accurately and robustly.

2 Challenge 2: Multiple Objectives and Constraints

Another challenge for wind turbine control design is the trade-off between multiple objectives and constraints. You want to maximize the power output and efficiency of the wind turbine, but also minimize the fatigue loads and vibrations that can damage the structure and reduce its lifetime. You also need to consider the operational limits of the wind turbine components, such as the generator torque, the blade pitch angle, and the yaw angle. These limits depend on the wind speed and the design specifications of the wind turbine.

3 Method 1: Model-Based Control

One of the common methods for wind turbine control design is model-based control, which uses a mathematical model of the wind turbine dynamics to design a feedback controller that can regulate the desired variables. For example, you can use a linearized model of the wind turbine around an operating point to design a proportional-integral-derivative (PID) controller that can adjust the generator torque and blade pitch angle to track a reference rotor speed. However, model-based control may not perform well when the wind turbine dynamics are nonlinear or uncertain, or when the operating point changes frequently.

4 Method 2: Model-Free Control

Another method for wind turbine control design is model-free control, which does not rely on a mathematical model of the wind turbine dynamics, but instead uses data-driven or learning-based techniques to design a controller that can adapt to the changing conditions. For example, you can use a neural network or a fuzzy logic system to approximate the nonlinear relationship between the wind speed and the optimal control inputs, and then use a reinforcement learning algorithm to update the controller parameters based on the feedback signals. However, model-free control may require more data and computation time, and may not guarantee stability or robustness.

5 Testing 1: Simulation

Before implementing the control strategies on a real wind turbine, you need to test their performance and feasibility in a simulation environment. You can use software tools such as FAST or Simulink to create a realistic model of the wind turbine and the wind field, and then apply the control strategies to the model and observe the results. You can also use different scenarios and disturbances to evaluate the robustness and reliability of the control strategies. Simulation testing can help you identify and correct any errors or flaws in the control design, and also save time and cost.

6 Testing 2: Experiment

After validating the control strategies in a simulation environment, you need to test them on a real or scaled wind turbine in an experiment setting. You can use a wind tunnel or a field site to expose the wind turbine to actual or simulated wind conditions, and then measure the power output, rotor speed, blade pitch angle, yaw angle, and structural loads and vibrations using sensors and instruments. You can also compare the experimental results with the simulation results and the design objectives and constraints. Experiment testing can help you verify and refine the control strategies, and also demonstrate their practical applicability and effectiveness.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?