Open AccessResearch on Deep Belief Network of Wind Power Control Management Unit Based on Attack Identification
Author(s) -
Liu Wei,
Zhiwei Huang,
Rui Chen,
Kai Ding,
Xiaofan Zhu,
Jinghong Zhou,
Guoqing Zhou,
Shengguo He,
Hongyan He,
Shengyuan Xiao,
Feng Lu,
Guoyou Wang,
Bing Ning,
Qing Ding
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1646/1/012120
Subject(s) - wind power , renewable energy , computer science , computer security , identification (biology) , reliability (semiconductor) , smart grid , environmental economics , control (management) , power (physics) , reliability engineering , engineering , electrical engineering , artificial intelligence , botany , physics , quantum mechanics , economics , biology
With the continuous improvement of the level of economic development and the increasingly serious environmental problems, countries around the world are focusing more on renewable energy. Wind energy is an important category of renewable energy because of its advantages. However, wind power is very dependent on the climate environment. It operates in an open operating environment, and its communication depends on the network interaction method. With the proposal of the Internet of Everything, the power grid is developing in the direction of information and intelligence. There are more and more attacks, and the security and stability of wind power interface devices have been threatened. As the power grid involves many areas and households, once the power outage occurs, the economic losses will be huge and even cause major security accidents. This paper proposes a deep belief network research of wind power control management unit based on attack recognition to improve the safety and operational reliability of wind power generation.