A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

This paper presents an improved Teaching-Learning-Based Optimization (TLBO) for optimal tuning of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. The original TLBO is characterized by easy implementation mainly free control parameters. Unfortunately, may suffer from population diversity losses in some cases, leading to local optimum premature convergence. In this study, three approaches are considered improving the (i) randomness improvement, (ii) new mutation strategies (iii) hyperchaotic perturbation strategy. first approach, all random numbers substituted map sequence boost exploration capability. second mutations carried out explore a promising search space. obtained solution further third strategy implementing equation. proposed HTLBO was evaluated with 26 test functions. results show that outperforms TBLO algorithm state-of-the-art algorithms robustness accuracy almost experiments. Moreover, efficacy justified involving it stability problem. consist Integral Absolute Error (ITAE) eigenvalue analysis electromechanical modes demonstrate superiority potential based PSSs SVC controllers over wide range operating conditions. Besides, advantage coordination design confirmed comparing them tuned individually.