A Novel Design Framework for Smart Operating Robot in Power System

作     者：Qiang Wang Xiaojing Yang Zhigang Huang Shiqian Ma Qiao Li David Wenzhong Gao Fei-Yue Wang 

作者机构：State Grid Tianjin Electric Power Company Tianjin 300010 China State Grid Tianjin Electric Power Research Institute Tianjin 300384 China University of Denver Denver Colorado 80210 USA Institute of Automation Chinese Academy of Sciences Beijing 100190 China IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2018年第5卷第2期

页      面：531-538页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 08[工学] 0802[工学-机械工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by State Grid Corporation of China(SGCC)Science and Technolgy Project(SGTJDK00DWJS1700060) 

主　　题：Index Terms-Artificial intelligence deep learning human-in-the-loop power system control center power system operatingrobot machine learning. 

摘      要：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.