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Analysis and Prospect of Deep Learning Application in Smart Grid

State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University, Chongqing 400044, China


Deep learning is a new field of machine learning. Its powerful data analysis, prediction, and classification capabilities satisfy the needs of big data applications in smart grid. Firstly, this paper summarizes the basic ideas of deep learning, introduces the structures, basic principles and training methods of five models of deep learning(generative adversarial network, recurrent neural network, convolution neural network, stacked auto encoder and deep belief network), and summarizes their application characteristics. The applications of deep learning techniques in fault diagnosis, transient stability analysis, load and new energy power forecasting, operation control in power system are summarized. Based on the technical characteristics of deep learning and the production links of power system, the application framework of deep learning technology in power system is constructed. Finally, the application of deep learning is prospected in the aspects of multi-energy system operation regulation, power electronic system security analysis, flexible equipment fault diagnosis, and cyber-physical power system security protection.



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[1]ZHOU Niancheng, LIAO Jianquan, WANG Qianggang, et al. Analysis and Prospect of Deep Learning Application in Smart Grid[J]. Automation of Electric Power Systems,2019,43(4):180-191. DOI:10.7500/AEPS20180323002
  • Received:March 23,2018
  • Revised:December 04,2018
  • Adopted:September 21,2018
  • Online: November 30,2018
  • Published: