Load frequency control with an event-triggered mechanism for nonlinear power systems based on the improved grey wolf optimization algorithm

This study focused on the load frequency control problem of power systems by integrating the event-triggered mechanism (ETM) and the improved grey wolf optimization (IGWO) algorithm. First, a nonlinear power system (NPS) model incorporating an energy storage system (ESS) and renewable energy sources (RESs) was constructed. The model considers the nonlinear characteristics of governors and turbines, and the takagi-sugeno (T-S) fuzzy theory is introduced to handle the nonlinear terms in the model. Subsequently, during the sampling process, an ETM is introduced, and the improved grey wolf algorithm is used to find the optimal event-triggered parameters to reduce unnecessary information transmission. Second, based on the Lyapunov functional, the stability criterion of the system under disturbance conditions was derived, a controller was designed, and the control gain was determined by solving linear matrix inequalities (LMIs). Finally, the performance differences between the traditional ETM and the ETM combined with IGWO were compared. The results show that the optimization method can further reduce the bandwidth resource consumption while ensuring system stability and control effectiveness.