Analysis of Gaussian fuzzy logic‐sliding model control and flexible AC transmission systems controllers for automatic generation control of hybrid power system under chaotic‐water cycle algorithm approach

Abstract

The article has suggested a hybridized fuzzy logic-sliding model control (FL-SMC) approach for automatic generation control (AGC) of a hybrid power system under various uncertainties. The hybrid structure makes modern power system more complex, and the inherent uncertain of renewable sources further causes power imbalance and frequency oscillations in the system. The AGC incorporates power generation monitoring and consequent frequency control of the hybrid system irrespective of the load demand. To obtain automatic control over power generation, various control actions are employed as secondary loop in the system. This study proposes FL-SMC as secondary controller for monitoring power generation of multigrid power system. Further, a novel chaotic-water cycle algorithm (C-WCA) has been suggested for optimum parameter selection of the proposed controller under different disturbances. The work also has implemented an interline power flow controller to improve AGC of hybrid system. The effectiveness of the proposed controller over sliding mode controller, conventional fuzzy-PID, and PID controller has been synthesized to justify its superiority. The robustness of the proposed controller has been examined through a critical sensitive analysis. Finally, it has been observed that proposed controller advances the settling time of ΔF 1 by 41%, 61.4%, and 86.4% over sliding mode controller, fuzzy-PID, and PID controller, respectively.

Renewable sources with virtual inertia penetration in multi area interconnected AGC using a modified MPC controller

“…According to previous research, choosing the appropriate controller type and settings is important for efficient control action. Fuzzy gain scheduling (FGS) and other conventional optimization techniques were first used to improve the controller settings [19]. However, they have drawbacks, including slumping, needing a large number of iterations, being computationally time-consuming, being caught in local minima, and depending on their initial state to determine the optimal parameters.…”