Enhanced Hiking Optimization Algorithm for Robust PID Control in Doubly‐Fed Induction Generator Systems for Wind Energy Applications

ABSTRACT This paper addresses the critical control challenges inherent in doubly fed induction generator (DFIG) systems, which are pivotal components of modern wind energy conversion systems (WECS). These systems often face performance degradation due to their nonlinear dynamics, sensitivity to grid disturbances, and difficulty in achieving robust control under fluctuating operational conditions. To tackle these issues, this study proposes an innovative approach for optimizing proportional‐integral‐derivative (PID) controller parameters using the hiking optimization algorithm (HOA). Inspired by Tobler's walking function, HOA is integrated with an enhanced version of the Zwe‐Lee Gaing (ZLG) objective function that incorporates penalty terms for overshoot, settling time, control effort, and abrupt signal variations. This enables a robust balance between transient and steady‐state performance in dynamic environments. Extensive simulations validate the effectiveness of the HOA‐optimized PID controller against five state‐of‐the‐art met heuristic algorithms: starfish optimization algorithm, grey wolf optimizer (GWO), dragonfly algorithm (DA), flow direction algorithm (FDA), and sine‐cosine algorithm (SCA). The results demonstrate that HOA achieves superior performance across all key metrics, including zero overshoot, rapid settling time (0.08922 s), and minimal steady‐state error. Statistically, HOA maintains the highest reliability with a standard deviation of just 0.0013 over 30 independent trials. In the frequency domain, HOA outperforms competitors by achieving the highest phase margin (87.163) and gain margin (26.11 dB), ensuring robust stability. The proposed controller also excels in disturbance rejection and input tracking under varying conditions. These findings establish HOA as a powerful and reliable optimization tool for advanced PID control of DFIG systems, with broader applicability in industrial control systems requiring high performance and adaptability.