Control Strategy of PMSM for Variable Pitch Based on Improved Whale Optimization Algorithm

A PI control approach grounded in an optimized improved whale algorithm is devised to tackle the characteristics of multivariable, nonlinear, strong coupling, and uncertain and fluctuating wind speeds in electric variable pitch systems. In the improved whale algorithm optimization algorithm, the reverse learning mechanism is utilized within the population initialization stage, the nonlinear inertial weight coefficient is introduced in the global and local search processes of whale predation, and the convergence factor is updated by the exponential function, which effectively addresses the issue of sluggish convergence speed and low convergence efficiency of the whale optimization algorithm. The position control of the electric variable pitch system is implemented with the application of the improved whale optimization algorithm. According to the performance index of the position ring, the appropriate objective function is established, and the adaptive control of the position ring is realized through the adaptive adjustment of PI parameters. The simulation outcomes demonstrate that the PI control, which is founded on an improved whale optimization algorithm, is superior to the PI control based on the whale optimization algorithm in dynamic and steady performance. When the load torque changes, using PI control based on the improved whale optimization algorithm, the pitch angle reaches the steady-state value in 0.06 s without overshoot, while using PI control based on the whale optimization algorithm, the pitch angle reaches the steady-state value in 0.09 s with a maximum overshoot of 2.4°. When the load torque is constant, PI control based on the improved whale optimization algorithm can achieve pitch angle tracking in 0.16 s, while PI control based on the whale optimization algorithm can achieve pitch angle tracking in 0.48 s.