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Cooperative coevolutionary algorithms for optimal PSS tuning based on Monte‐Carlo probabilistic small‐signal stability assessment
International Transactions on Electrical Energy Systems ( IF 1.9 ) Pub Date : 2020-09-22 , DOI: 10.1002/2050-7038.12618
Yasmina Chabane 1 , Ahmed Amine Ladjici 1 , Abdelhafid Hellal 2 , Kumar Dookhitram 3
Affiliation  

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.

中文翻译:

基于蒙特卡罗概率小信号稳定性评估的PSS最优协同调整算法

本文介绍了使用蒙特卡洛方法进行的稳健且最佳的PSS调整,用于不确定性条件下的电力系统概率小信号稳定性分析。不确定性主要与可再生能源有关,包括电力系统的生产和需求。此外,还增加了线路和变压器的意外情况。考虑到它们的特征,构造了这些不确定性的概率模型。随后,基于蒙特卡洛模拟的模态分析,对电力系统进行概率性小信号稳定性评估。通过分析新英格兰纽约基准系统的特征值(稳定,不稳定,首先在确定性框架中确定振荡模式和振荡模式,然后将其扩展到概率环境,因为确定性模式受限于特定的运行状态。此外,对机电振荡的局部和区域间模式进行了分类,并进行了PSS最佳放置和调整,以确保在不确定情况下具有足够的阻尼。已经说明了使用所提出的方法进行稳定性增强的相关讨论。
更新日期:2020-11-12
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