Prediction Model for the DC Flashover Voltage of a Composite Insulator Based on a BP Neural Network

Zhenan Zhou, Haowei Li, Silun Wen and Chuyan Zhang ()
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Zhenan Zhou: School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China
Haowei Li: School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China
Silun Wen: School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China
Chuyan Zhang: School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China

Energies, 2023, vol. 16, issue 2, 1-9

Abstract: To be able to predict the DC flashover characteristics of composite insulators, a four-layer BP neural network model is established with composite insulator shed structure parameters as the input. Three algorithms (gradient descent with momentum, RMSProp gradient descent, and Adam gradient descent) are applied, and the DC pollution flashover experimental data of composite insulators are used as training data. The results show that all three algorithms have good prediction capabilities. Among them, the Adam gradient descent model has the best prediction result, which can make the average prediction with an error of less than 4% and a maximum error of less than 8%, so these results can provide a reference for the design of composite insulators in DC voltage and product performance verifications.

Keywords: composite insulator; DC flashover performance; shed optimization; BP neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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