Probabilistic assessment method of small-signal stability in power systems based on quantitative PSS analysis

Abstract

Due to the exhaustion of fossil fuel energy and the awareness of environmental protection, the proportion of renewable energy sources (RES) integrated to the grid is increasing rapidly. However, the uncertainty of RES poses great challenges to the stability of power systems. To solve this uncertainty problem, power system small-signal stability analysis is transitioning from deterministic methods to probabilistic methods. Power system stabilizers (PSS) are important components to suppress low-frequency oscillations and improve system dynamic response performance. But there is still a lack of systematic simulation analysis regarding the optimization effect of PSS on probabilistic small-signal stability. In response to this problem, this paper proposes a probabilistic assessment method of small-signal stability based on quantitative PSS analysis. It constructs modified IEEE-14 and IEEE-39 bus systems with RES penetration and presents a joint simulation framework for multi-machine PSS parameter optimization based on the clustered difference mean perturbation particle swarm optimization (CD-PSO) algorithm. Then quantitative analysis is conducted on the impact of PSS integration on small-signal stability under a typical RES penetration rate (20%). Finally, the influence of different RES penetration rates and load levels on small-signal stability of power systems under different PSS scenarios is investigated.