State Perception Method of Intelligent Substation Secondary System Based on FCE and DCNN
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Aiming at second equipment lacks comprehensive and effective state detection and simple and reliable evaluation method, a state perception method of intelligent substation secondary system based on fuzzy comprehensive evaluation (FCE) and deep convolutional neural network (DCNN) is proposed. Firstly, combining with the factors of auxiliary equipment state evaluation, the FCE method is adopted to evaluate the influence degree of each secondary equipment. Secondly, DCNN was used to learn the regional and edge features respectively, and the significance and non-significance confidence of the detected region was obtained. Finally, combined with the influence degree of each secondary equipment and the significant and non-significant confidence level, the state of the secondary equipment in intelligent substation is evaluated. The experiment results indicate that the proposed method can effectively solve the deficiency of the corresponding equipment status detection and evaluation method of intelligent substation.
KeywordsDeep convolutional neural network Intelligent substation Secondary system Method of state perception State detection Fuzzy comprehensive evaluation
This work is 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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