Risk Assessment Method for Smart Substation Secondary System Based on Deep Neural Network
- 322 Downloads
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.
KeywordsSmart substations Secondary systems Risk assessment Deep neural network Deep auto-encoders
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).
- 3.Hengtian WU, Jiao C, Cui X et al (2017) Analysis and simulated experiment for port disturbance voltage due to switching operation in GIS substation. High Voltage Eng 43(10):3387–3395Google Scholar
- 7.Chen B, Li C, Qin H et al (2018) Evaluation of typhoon resilience of distribution network considering grid reconstruction and disaster recovery. Autom Electr Power Syst 42(6):47–52 Google Scholar
- 9.Yin Q, Duan B, Shen M et al (2019) Intelligent diagnosis method for open-circuit fault of sub-modules in modular five-level inverter. Autom Electr Power Syst 43(01):162–170Google Scholar