A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Qiang Wang, Xiaojing Yang, Zhigang Huang, Shiqian Ma, Qiao Li, David Wenzhong Gao and Fei-Yue Wang, "A Novel Design Framework for Smart Operating Robot in Power System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 531-538, Mar. 2018. doi: 10.1109/JAS.2017.7510838
Citation: Qiang Wang, Xiaojing Yang, Zhigang Huang, Shiqian Ma, Qiao Li, David Wenzhong Gao and Fei-Yue Wang, "A Novel Design Framework for Smart Operating Robot in Power System," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 531-538, Mar. 2018. doi: 10.1109/JAS.2017.7510838

A Novel Design Framework for Smart Operating Robot in Power System

doi: 10.1109/JAS.2017.7510838
Funds:

State Grid Corporation of China (SGCC) Science and Technolgy Project SGTJDK00DWJS1700060

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  • This paper proposes the concept and framework of smart operating system based on the artificial intelligence (AI) techniques. The demands and the potential applications of AI technologies in power system control centers is discussed in the beginning of the paper. The discussion is based on the results of a field study in the Tianjin Power System Control Center in China. According to the study, one problem in power systems is that the power system analysis system in the control center is not fast and powerful enough to help the operators in time to deal with the incidents in the power system. Another issue in current power system control center is that the operation tickets are compiled manually by the operators, so that it is less efficient and human errors cannot be avoided. Based on these problems, a framework of the smart operating robot is proposed in this paper, which includes an intelligent power system analysis system and a smart operation ticket compiling system to solve the two problems in power system control centers. The proposed framework is mainly based on the AI techniques, especially the neural network with deep learning, since it is faster and more capable of dealing with the highly nonlinear and complex power system.

     

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