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Risk Assessment Method for Smart Substation Secondary System Based on Deep Neural Network

  • Zhian ZengEmail author
  • Shuyou Yao
  • Tingbai Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 585)

Abstract

In the risk assessment for the smart substations, the traditional Monte Carlo-based methods rely on prior distribution knowledge and cannot cover all the potential failure scenarios. In this paper, a risk assessment method based on the deep neural network for the smart substation secondary system is proposed. Firstly, a deep neural network established by the deep auto-encoders is proposed to quantitatively evaluate the operational risk of the smart substation. Secondly, the key indicators that affect the substation operations are hierarchically combed and refined, and are used as the inputs of the deep neural network. Finally, numerical simulation results from an actual smart substation show that compared with the traditional Monte Carlo-based assessment methods, the accuracy of the proposed method for assessing the operation states of the smart substation can be improved by 48.03% under the same iterations. In addition, the running time of the proposed method is less by 12.3% than the time of traditional method in the case with the same iterations. Hence, effectiveness and feasibility of the proposed method can be verified.

Keywords

Smart substations Secondary systems Risk assessment Deep neural network Deep auto-encoders 

Notes

Acknowledgements

This work was supported by Science and Technology Project of State Grid Chongqing Electric Power Company in 2018. The project name is “Integrated Operational Support Technology of Intelligent Substations Based on Total Service Data” (No. 2018#35).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.State Grid Chongqing Electric Power CompanyChongqingChina

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