A Bio-Inspired Gazelle Optimization Algorithm for Parameter Tuning of Coordinated PSS-SSSC to Damp Low-Frequency Oscillation in Power Systems

Abstract

This study presents a novel Gazelle optimization algorithm that improves power system stability by damping
low-frequency oscillations. The Gazelle optimization algorithm (GOA) optimizes the controller parameters of coordinated PSS and SSSC controllers, with the main objective of reducing rotor speed deviation. The GOA effectiveness was validated with its benchmark functions and demonstrated in a multi-machine power system as a test system. A statistical analysis has been carried out with different benchmark functions to demonstrate the superiority of the suggested algorithm along with three other optimization techniques, which are the sinecosine algorithm, moth-flame algorithm and ant lion optimization algorithm. A nemenyi hypothesis test was implemented to determine the lowest mean rank, along with the Wilcoxon signed-rank test, which shows the effecacy of the proposed algorithm. The results, which are backed by statistical analysis, demonstrate that the suggested GOA approach performs better than three alternative heuristic algorithms. The GOA-tuned parameters are finally implemented with a multimachine power system network as a test system. Different parameters, such as speed deviation between different machines, voltage injected by SSSC, and tie-line power, are compared with three other optimization techniques in the results section. From the result analysis, it has been concluded that the proposed Gazelle optimization algorithm gives superior performance than others, which proves its real-time applicability in the modern power system world.