Modified grasshopper optimization algorithm optimized adaptive fuzzy lead-lag controller for coordinated design of FACTS controller with PSS

Abstract

This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority.