Probabilistic small signal stability analysis with large scale integration of wind power considering dependence. (March 2017)

• Record Type: Journal Article
• Authors: Xu, Jin; Kanyingi, Peter Kairu; Wang, Keyou; Li, Guojie; Han, Bei; Jiang, Xiuchen
• Is Part Of: Renewable & sustainable energy reviews. Volume 69(2017)
• Page Start: 1258
• Page End: 1270
• Publication Date: 2017-03
• Subjects: DFIG Doubly Fed Induction Generator -- MCS Monte Carlo Simulation -- PDF Probability distribution function -- CDF Cumulative distribution function -- PEM Point Estimation Method -- SSSA Small Signal Stability Analysis -- SSS Small signal stability -- QMC Quasi Monte Carlo -- FOSMM First order second moment method -- PCM Probabilistic collocation method -- CM Cumulant-based Method -- LCC Linear Correlation Coefficient -- NC Normal Copula -- PC Pair Copula -- sPC simplified Pair Copula; Wind -- Uncertainties -- Correlation -- Probabilistic small signal stability -- Copula; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2016.12.041
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Modern power systems are experiencing an escalation in the penetration of wind power sources which have both positive and negative impact on small signal stability. Wind power being stochastic in nature introduces uncertainties which make it necessary to account for such uncertainties during power system modeling and analysis. Probabilistic methodologies are appropriate choices in accounting for such uncertainties. These methodologies have recently developed concerns with regard to the probabilistic small signal stability analysis of power system integrated with wind power generation. Accounting for the dependence existing between wind power sources is considered as an important option to aid in simulating the impact of the wind power integration towards small signal stability. Numerous dependence modeling methods exist, each with its strengths and weaknesses. This paper presents an overview of the probabilistic methods applied in small signal stability analysis and methodologies used to model dependence or correlation in the probabilistic stability analysis. A comparative analysis of different dependence models is performed. The model accuracy is compared under different scenarios and the efficiency is discussed.