Comparative study of three quantum-inspired optimization algorithms for robust tuning of power system stabilizers

Low-frequency oscillations (LFO) are a significant problem for multi-machine electrical power system (EPS). These oscillations are undesirable as they reduce the power transfer capability of the transmission line and thus directly influence competitive electrical markets. The power system stabilizers (PSS) play a vital role in damping low-frequency oscillations to improve the dynamic stability of the power system. However, these controllers must be tuned in a coordinated and robust manner for effective performance. PSS tuning is characterized by a complex optimization problem that may involve hundreds of variables. The tuning procedure is modeled as an optimization problem which aims at maximizing the damping ratio coefficients of the closed-loop power system considering multiple operating points. In this context, this article employs and compares three metaheuristics with quantum characteristics, namely quantum particle swarm optimization (QPSO), quantum flower pollination algorithm (QFPA) and quantum gray wolf optimizer (QGWO). The aforementioned metaheuristics are used in PSS tuning in four test systems with 5 generators–7 buses, 10 generators–39 buses, 50 generators–616 buses and 170 generators–3584 buses. The results obtained by the quantum algorithms are compared through statistical indices and boxplot. The optimization results show that the QFPA and QGWO provide better system damping than the QPSO for the large electrical system.