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Frontiers in Energy

, Volume 13, Issue 1, pp 71–85 | Cite as

Application of AI techniques in monitoring and operation of power systems

  • David Wenzhong Gao
  • Qiang Wang
  • Fang ZhangEmail author
  • Xiaojing Yang
  • Zhigang Huang
  • Shiqian Ma
  • Qiao Li
  • Xiaoyan Gong
  • Fei-Yue Wang
Research Article
  • 73 Downloads

Abstract

In recent years, the artificial intelligence (AI) technology is becoming more and more popular in many areas due to its amazing performance. However, the application of AI techniques in power systems is still in its infancy. Therefore, in this paper, the application potentials of AI technologies in power systems will be discussed by mainly focusing on the power system operation and monitoring. For the power system operation, the problems, the demands, and the possible applications of AI techniques in control, optimization, and decision making problems are discussed. Subsequently, the fault detection and stability analysis problems in power system monitoring are studied. At the end of the paper, a case study to use the neural network (NN) for power flow analysis is provided as a simple example to demonstrate the viability of AI techniques in solving power system problems.

Keywords

power system operation and monitoring artificial intelligence (AI) deep learning power flow analysis 

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Notes

Acknowledgements

This work was supported by State Grid Corporation of China (SGCC) Science and Technology Project (No. SGTJDK00DWJS-1700060).

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • David Wenzhong Gao
    • 1
    • 2
  • Qiang Wang
    • 3
  • Fang Zhang
    • 4
    Email author
  • Xiaojing Yang
    • 3
  • Zhigang Huang
    • 3
  • Shiqian Ma
    • 5
  • Qiao Li
    • 1
  • Xiaoyan Gong
    • 6
  • Fei-Yue Wang
    • 6
  1. 1.University of DenverDenverUSA
  2. 2.China State Key Laboratory of Power System, Department of Electrical EngineeringTsinghua UniversityBeijingChina
  3. 3.State Grid Tianjin Electric Power CompanyTianjinChina
  4. 4.China State Key Laboratory of Power System, Department of Electrical EngineeringTsinghua UniversityBeijingChina
  5. 5.State Grid Tianjin Electric Power Research InstituteTianjinChina
  6. 6.Chinese Academy of Sciences (SKL-MCCS, CASIA)BeijingChina

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