Advanced Control Strategies for Wind Turbine Blade Angle Systems: A Comparative Study of Optimization Algorithms and Controllers

Abstract

Wind energy is a critical component of renewable energy systems, but the stochastic nature of wind speed poses significant challenges for consistent power generation. This paper addresses these challenges by proposing advanced control strategies to enhance the performance of wind turbine blade angle systems. We compare two optimization algorithms: harmony search algorithm (HSA) and exponential distribution optimizer (EDO) for tuning proportional‐integral‐derivative (PID) controllers under various operating conditions, including normal operation and fault scenarios. The EDO algorithm demonstrates superior performance in optimizing blade angle control, leading to improved system stability and faster response times. Building on this, we further evaluate three controllers: PID, proportional‐derivative‐derivative, and adaptive proportional‐integral (API) using the EDO algorithm. The API controller, with its adaptive gains, outperforms both PID and proportional double derivative (PD 2 ) controllers, achieving smoother pitch angle adjustments and more stable active power output under varying wind conditions. The results highlight the API controller’s ability to maintain rated power levels with minimal oscillations, even during rapid wind speed changes and fault conditions. This study provides valuable insights into the optimization of wind turbine blade angle systems, offering a robust framework for improving power extraction efficiency and system reliability. The findings suggest that the combination of EDO optimization and API control represents a promising approach for enhancing wind turbine performance in dynamic environments.