Wind power system based state estimation and measurement using weighted Grey Wolf Optimization

Abstract

Wind power systems are a key element in sustainable development and provide a stable and secure model for communication through the power grid. The research proposes a control strategy called AGCOPI for wind power systems that focuses on frequency and inertia control. The proposed strategy employs a weighted Grey Wolf Optimization (WGWO) algorithm that is integrated with a Proportional-Integral (PI) controller to enhance the frequency response and stability of the power grid. The AGCOPI scheme uses primary frequency modulation to manage the generated power in the wind farm. The response time is computed for a specific area using a State Estimation and Measurement System (SEMS) unit for measurement. The results demonstrate that the AGCOPI model offers superior performance compared to the conventional ABC-PID controller. The primary regulation model achieves a response time of less than 2 s and maintains a control deviation in the rated capacity value of ±1%.