A review of machine learning for new generation smart dispatch in power systems. (February 2020)

• Record Type: Journal Article
• Authors: Yin, Linfei; Gao, Qi; Zhao, Lulin; Zhang, Bin; Wang, Tao; Li, Shengyuan; Liu, Hui
• Is Part Of: Engineering applications of artificial intelligence. Volume 88(2020)
• Publication Date: 2020-02
• Subjects: 00-01 -- 99-00; Machine learning -- Smart dispatch -- Deep neural networks -- Parallel systems; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.103372
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper analyzes the characteristics and challenges of the new generation smart dispatch systems, and proposes the framework of smart dispatch. Secondly, the development of the new generation artificial intelligence technology is represented, especially the development of machine learning algorithms. Thirdly, the applications of machine learning in power systems, e.g. smart generation control, optimal power flow, security assessment, smart dispatch, are listed. Finally, the framework of dispatching robot technology based on parallel learning is present. Highlights: Framework and challenges of new generation smart dispatch systems are present. Applications about machine learning (ML) algorithms in power systems are present. Smart dispatch group robot based on parallel learning systems is displayed. ML for optimal power flow and smart generation control problems are reviewed. ML for security assessment and smart dispatch in power systems are present.