A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power

Can Wang; Haipeng Xie; Shiyu Liu; Zhaohong Bie

doi:10.1109/EPEC.2017.8286164

A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power

Can Wang
, Haipeng Xie
, Shiyu Liu
, Zhaohong Bie

电气工程学院

Xi'an Jiaotong University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

5 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	2017 IEEE Electrical Power and Energy Conference, EPEC 2017
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1-6
页数	6
ISBN（电子版）	9781538608173
DOI	https://doi.org/10.1109/EPEC.2017.8286164
出版状态	已出版 - 2 7月 2017
活动	2017 IEEE Electrical Power and Energy Conference, EPEC 2017 - Saskatoon, 加拿大期限: 22 10月 2017 → 25 10月 2017

出版系列

姓名	2017 IEEE Electrical Power and Energy Conference, EPEC 2017
卷	2017-October

会议

会议	2017 IEEE Electrical Power and Energy Conference, EPEC 2017
国家/地区	加拿大
市	Saskatoon
时期	22/10/17 → 25/10/17

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/EPEC.2017.8286164

其它文件与链接

链接到 Scopus 的出版物

引用此

Wang, C., Xie, H., Liu, S., & Bie, Z. (2017). A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power. 在 2017 IEEE Electrical Power and Energy Conference, EPEC 2017 (页码 1-6). (2017 IEEE Electrical Power and Energy Conference, EPEC 2017; 卷 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EPEC.2017.8286164

@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",

}

Wang, C, Xie, H, Liu, S & Bie, Z 2017, A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power. 在 2017 IEEE Electrical Power and Energy Conference, EPEC 2017. 2017 IEEE Electrical Power and Energy Conference, EPEC 2017, 卷 2017-October, Institute of Electrical and Electronics Engineers Inc., 页码 1-6, 2017 IEEE Electrical Power and Energy Conference, EPEC 2017, Saskatoon, 加拿大, 22/10/17. https://doi.org/10.1109/EPEC.2017.8286164

A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power. / Wang, Can; Xie, Haipeng; Liu, Shiyu 等.
2017 IEEE Electrical Power and Energy Conference, EPEC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 页码 1-6 (2017 IEEE Electrical Power and Energy Conference, EPEC 2017; 卷 2017-October).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power

AU - Wang, Can

AU - Xie, Haipeng

AU - Liu, Shiyu

AU - Bie, Zhaohong

PY - 2017/7/2

Y1 - 2017/7/2

N2 - 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.

AB - 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.

KW - AC/DC hybrid power system

KW - BP neural network

KW - HVDC

KW - Monte-Carlo method

KW - wind power

UR - https://www.scopus.com/pages/publications/85050360869

U2 - 10.1109/EPEC.2017.8286164

DO - 10.1109/EPEC.2017.8286164

M3 - 会议稿件

AN - SCOPUS:85050360869

T3 - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017

SP - 1

EP - 6

BT - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Electrical Power and Energy Conference, EPEC 2017

Y2 - 22 October 2017 through 25 October 2017

ER -

A novel reliability evaluation method of AC/DC hybrid power system with the injection of wind power

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此