A meteorological information mining-based wind speed model for adequacy assessment of power systems with wind power. (December 2017)

• Record Type: Journal Article
• Authors: Guo, Yifei; Gao, Houlei; Wu, Qiuwei
• Is Part Of: International journal of electrical power & energy systems. Volume 93(2017)
• Page Start: 406
• Page End: 413
• Publication Date: 2017-12
• Subjects: Adequacy assessment -- Clustering analysis -- Markov chain -- Meteorological factors -- Wind speed model; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2017.05.031
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: An improved classification method of meteorological states. A two-step wind speed model considering meteorological and seasonal factors. Replicate the wind speed characteristics accurately. Effectiveness of proposed model in adequacy assessment. Abstract: Accurate wind speed simulation is an essential prerequisite to analyze the power systems with wind power. A wind speed model considering meteorological conditions and seasonal variations is proposed in this paper. Firstly, using the path analysis method, the influence weights of meteorological factors are calculated. Secondly, the meteorological data are classified into several states using an improved Fuzzy C-means (FCM) algorithm. Then the Markov chain is used to model the chronological characteristics of meteorological states and wind speed. The proposed model was proved to be more accurate in capturing the characteristics of probability distribution, auto-correlation and seasonal variations of wind speed compared with the traditional Markov chain Monte Carlo (MCMC) and autoregressive moving average (ARMA) model. Furthermore, the proposed model was applied to adequacy assessment of generation systems with wind power. The assessment results of the modified IEEE-RTS79 and IEEE-RTS96 demonstrated the effectiveness and accuracy of the proposed model.