Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

Xin Wei Wang; Jian Hua Zhang; Cheng Jiang; Lei Yu

doi:10.4028/www.scientific.net/AMM.291-294.536

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 291-294Probabilistic Assessment of Wind Farm Active Power...

Probabilistic Assessment of Wind Farm Active Power Based on Monte-Carlo Simulation

Abstract:

The conventional deterministic methods have been unable to accurately assess the active power output of the wind farm being the random and intermittent of wind power, and the probabilistic methods commonly used to solve this problem. In this paper the multi-state fault model is built considering run, outage and derating state of wind turbine, and then the reliability model of the wind farm is established considering the randomness of the wind speed, the wind farm wake effects and turbine failure. The active wind farm output probability assessment methods and processes based on the Monte Carlo method. The related programs are written in MATLAB, and the probability assessment for active power output of a wind farm in carried out, the effectiveness and adaptability of built reliability models and assessment methods are illustrated by analysis of the effects of reliability parameters and model parameters on assessment results.

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Periodical:

Applied Mechanics and Materials (Volumes 291-294)

Pages:

536-540

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.291-294.536

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Online since:

February 2013

Authors:

Xin Wei Wang, Jian Hua Zhang, Cheng Jiang, Lei Yu

Keywords:

Monte-Carlo Method (MC-Method), Multi-State, Probabilistic Assessment, Wind Farm

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References

[1] Liying Zhang, Tinglu Ye: Proceedings of the CSEE. Vol. 30(25)( 2010), P. 1 (In Chinese).

Google Scholar

[2] Wenyi Li，Yinsha Wang: East China Electric power. Vol. 39(7)(2011), P. 1062 (In Chinese).

Google Scholar

[3] Yichun Wu, Ming Ding: Electric Power Automation Equipment. Vol. 24(12)( 2004), P. 70(In Chinese).

Google Scholar

[4] Wen Hua，Zheng Xu: High Voltage Apparatus. Vol. 46(12) (2010), P. 36 (In Chinese).

Google Scholar

[5] F A Bhuiyan, A Yazdani: IET Renewable Power Generation. Vol. 4 (3)(2010), P. 211.

Google Scholar

[6] Mingming Yang: North China Electric Power University, a master's degree thesis，2009 (In Chinese).

Google Scholar

[7] F Chen, K B Athreya, V V Sastry: IEEE Trans. on Power Systems. Vol. 19(2)(2004), P. 826.

Google Scholar

[8] Yuanzhang Sun, Jun Wu: Proceedings of the CSEE. Vol. 29(4)(2009), P. 41 (In Chinese).

Google Scholar