Tuning Improvement of Power System Stabilizer using Hybrid Harris Hawk Optimization-Equilibrium Optimizer Algorithm

Abstract

A Harris Hawk Optimization (HHO) become popular in recent works to resolve various power system cases, including tuning Power System Stabilizer (PSS). However, HHO has several challenges, such as premature convergence can occur due to less diversity in solutions with an unbalanced exploration and exploitation process. Therefore, this paper proposed The Hybrid Harris Hawk Optimization-Equilibrium Optimizer Algorithm (HHHO-EOA) to enhance HHO performance and acquire better optimal tuning for PSS. The EOA is a well-balanced exploration and exploitation algorithm. A Linearized Heffron-Phillips model for Single-Machine Infinite Bus (SMIB) is modeled to evaluate the proposed algorithm in tuning PSS. Various performance indices are used as the objective functions. The HHHO-EOA has a significantly better enhancement in the convergence curve with consistent results than previous algorithms. The minimum objective function achieved by the proposed algorithm is 78.3% and 60% lower than conventional EOA and HHO. Moreover, the PSS based on the proposed algorithm is 10.24% and 19.69% better in damping the overshoot than conventional EOA and HHO in both frequency and power angle deviation responses.