Enhanced Chameleon Swarm Algorithms for Nested Identical Control of Load Frequency in Autonomous Microgrid

Abstract

This paper presents a novel Nested Identical Control (NIC) method for load frequency control of multi-area microgrid systems employing two novel variants of the Chameleon Swarm Algorithm (CSA). Load frequency control, a common strategy to counter power system instabilities from load fluctuations, poses a significant challenge. Maintaining system stability amidst substantial load shifts becomes particularly challenging in complex power networks spanning multiple areas with several generators. To address this, we first designed two novel variants of CSA called Quasi-oppositional CSA (QCSA) and Quasi-Levy-oppositional CSA (QLCSA). In QCSA, a quasi-oppositional-based learning operator is used to improve the diversification and intensification of CSA. The QLCSA incorporates the Levy flight operator into the QCSA to avoid stagnation and local minimal entrapment. We evaluated the efficiency of these variants against the original CSA and five other highly performing algorithms on the IEEE Congress on Evolutionary Computation benchmark functions. Subsequently, we employed the new CSA variants to design a NIC for load frequency control of a two-area microgrid system, considering different cases of uncertainties. The NIC strategy is designed such that the controller parameters of each loop in one area are inherited by the controller of the corresponding loop in the second area, with a target to achieve < 3% overshoot in every frequency fluctuation and < 10 sec of settling time. Simulated results on the benchmark functions prove the superiority of QLCSA and QCSA over CSA and the selected highly performing algorithms. The dynamic responses of the NIC-controlled microgrid system in numerical time-domain simulations show the effectiveness of the proposed control approaches when compared with PID controller tuned using MATLAB’s control system tuner.