Abstract
In recent times, wind energy receives maximum attention and has become a significant green energy source globally. The wind turbine (WT) entered into several domains such as power electronics that are employed to assist the connection process of a wind energy system and grid. The turbulent characteristics of wind profile along with uncertainty in the design of WT make it highly challenging for prolific power extraction. The pitch control angle is employed to effectively operate the WT at the above nominal wind speed. Besides, the pitch controller needs to be intelligent for the extraction of sustainable secure energy and keep WTs in a safe operating region. To achieve this, proportional-integral-derivative (PID) controllers are widely used and the choice of optimal parameters in the PID controllers needs to be properly selected. With this motivation, this paper designs an oppositional brain storm optimization (OBSO) based fractional order PID (FOPID) design for sustainable and secure energy in WT systems. The proposed model aims to effectually extract the maximum power point (MPPT) in the low range of weather conditions and save the WT in high wind regions by the use of pitch control. The OBSO algorithm is derived from the integration of oppositional based learning (OBL) concept with the traditional BSO algorithm in order to improve the convergence rate, which is then applied to effectively choose the parameters involved in the FOPID controller. The performance of the presented model is validated on the pitch control of a 5 MW WT and the results are examined under different dimensions. The simulation outcomes ensured the promising characteristics of the proposed model over the other methods.