Cooperative coevolutionary algorithms for optimalPSStuning based onMonte‐Carloprobabilistic small‐signal stability assessment

Abstract

Summary 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.

Small-Signal Stability Constrained Optimal Power Flow Model Based on BP Neural Network Algorithm

“…The small-signal stability analysis of the power systems mainly studies the ability of the system to maintain synchronization after suffering small disturbances [4]. The power systems stabilizer (PSS) is used to improve system damping [5], but it is not sufficient to ensures that the system is free from the low frequency oscillation due to the changes in system operating conditions [6]. Therefore, the SC-OPF model that applies power generation rescheduling technology [7,8] is proposed.…”