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Transient Voltage Stability Assessment and Risk Quantification Based on Convolutional Neural Network

1.School of Electric Power, South China University of Technology, Guangzhou 510640, China;2.Maintenance Branch Company of State Grid Jiangsu Electric Power Co., Ltd., Nanjing 211102, China;3.Electric Power Research Institute of China Southern Power Grid Co., Ltd., Guangzhou 510663, China;4.Power Dispatching Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510660, China


A method is proposed for assessing transient voltage stability and quantifying its risk. Firstly, the correlation and matching between the convolutional neural network (CNN) and transient voltage stability assessment are discussed and the CNN based assessment model for the transient voltage stability is built. Secondly, the quaternary assessment structure is introduced in the credibility framework, which can effectively solve the dependence of CNN on the time-domain simulation in the stable boundary recognition. Then, the transient voltage stability margin is obtained through the assessment results. Combined with the credibility, a risk function is constructed to realize the quantitative classification of the risk of transient voltage stability. Validity of the method is verified by the analysis results of an actual power grid example.



This work is supported by National Natural Science Foundation of China (No. U1766213) and China Southern Power Grid Company Limited (No. GDKJXM20198236).

Get Citation
[1]CHEN Da, ZHU Lin, ZHANG Jian, et al. Transient Voltage Stability Assessment and Risk Quantification Based on Convolutional Neural Network[J]. Automation of Electric Power Systems,2021,45(14):65-71. DOI:10.7500/AEPS20200615002
  • Received:June 15,2020
  • Revised:January 15,2021
  • Adopted:
  • Online: July 21,2021
  • Published: