@inproceedings{de8d3d4856af4b1d844888616f3642dc,
title = "A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power",
abstract = "With the rapid development of HVDC projects and renewable energy, reliability of AC/DC hybrid power system with wind power draws more and more attention. To depict the uncertainty of wind power, this paper proposes the wind power BP neural network model to fit the probability distribution of actual wind speed. Compared with traditional wind power models such as Weibull distribution model, the BP neural network model is closer to the actual probability distribution of wind speed according to numerical results. By using Monte Carlo method, the AC/DC hybrid system states are obtained. Then considering the interaction between AC and DC system, a novel minimum load shedding model of hybrid system with HVDC is proposed. IEEE-RTS 96 system is testified with actual Northern China wind data, which illustrates a more accurate wind power modeling as well as a comprehensive reliability evaluation on AC/DC hybrid power system integrated with wind power.",
keywords = "AC/DC hybrid power system, BP neural network, HVDC, Monte-Carlo method, wind power",
author = "Can Wang and Haipeng Xie and Shiyu Liu and Zhaohong Bie",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Electrical Power and Energy Conference, EPEC 2017 ; Conference date: 22-10-2017 Through 25-10-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/EPEC.2017.8286164",
language = "英语",
series = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2017 IEEE Electrical Power and Energy Conference, EPEC 2017",
}
