Improving frequency regulation of wind‐integrated multi‐area systems using LFA‐fuzzy PID control

Abstract

Due to the complexity and importance of the electricity grid stability, sophisticated control systems and computational approaches are essentially required to solve dynamical system frequency problems in multi-area systems. Participation of wind turbines in power system frequency control in the form of stepwise inertial control in the presence of a proposed adequately tuned fuzzy-PID load-frequency control is investigated in this paper. A two-area power system is studied in which it is linked through tie-line and equipped with fuzzy-PID control systems. To increase the energy transition and flexibility of the entire system against frequency events and parameters uncertainties, the fuzzy-PID controllers are first tuned using lightning flash algorithm (LFA). The tuned controllers are then used in each area to optimally counterbalance the frequency deviations and the tie-line power of the interconnected areas against varying load disturbances, wind power fluctuations, and to keep the renewable integrated system in a stable state. The sensitivity analysis is also conducted with a wide range of system parameter variations, uncertainties, and disturbances. Furthermore, the LFA-tuned fuzzy-PID load-frequency control is also tested in a two-area isolated microgrid, comprising wind turbine, PV panels, fuel cells, micro-turbine, and diesel engine generator. The results obtained using the proposed approach are compared with other state-of-the-arts algorithms and control systems in the literature. The compared results show the efficiency and robustness of the proposed optimization-based controller for better frequency control in multi-area power systems and isolated microgrids. The results also demonstrate that a finely tuned automatic generation controller gives a significant boost to grid frequency control when wind turbines participate in the inertial control, suggesting migration from conventional PID-based automatic generation control to more adaptive control approaches.