Cooperative coevolutionary algorithms for optimal PSS tuning based on Monte-Carlo probabilistic small-signal stability assessment

摘要

This article presents a robust and optimal PSS tuning with the Monte-Carlo approach for probabilistic small-signal stability analysis in electric power systems under uncertainties. The uncertainties are mainly related to renewable energy and include production and demand in power systems. Additionally, lines and transformers contingencies have also been added. Probabilistic models of these uncertainties are constructed considering their characteristics. Subsequently, probabilistic small-signal stability assessment of the power system is carried out based on modal analysis via Monte-Carlo simulation. The proposed method is tested by analyzing the eigenvalues of New England New York benchmark system, where stable, unstable, and oscillatory modes are first identified in the deterministic framework and extended to a probabilistic context as the deterministic one is limited for particular operating states. Additionally, local and inter-area modes of electromechanical oscillation are classified, and PSS optimal placement and tuning are performed to ensure sufficient damping under uncertainties. Relevant discussion of stability enhancement using the proposed approach has been illustrated.