Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on Distributed Current Measurement and Neural Network Model

Wang Yizhuo, Li Zhonlian, Li Long, Li Runhua, Cui Xinglei, Fang Zhi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that the generalisation ability of the CNN model based on image recognition is better than that of the BP neural network. This study is of great reference significance for real-time online diagnosis and industrial application of DBD material modification.

Original languageEnglish
Title of host publicationProceedings of the 1st Electrical Artificial Intelligence Conference, Volume 1 - EAIC 2024
EditorsRonghai Qu, Zhengxiang Song, Zhiming Ding, Gang Mu, Rui Xiong, Li Han
PublisherSpringer Science and Business Media Deutschland GmbH
Pages89-103
Number of pages15
ISBN (Print)9789819648559
DOIs
StatePublished - 2025
Event1st Electrical Artificial Intelligence Conference, EAIC 2024 - Nanjing, China
Duration: 6 Dec 20248 Dec 2024

Publication series

NameLecture Notes in Electrical Engineering
Volume1394 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference1st Electrical Artificial Intelligence Conference, EAIC 2024
Country/TerritoryChina
CityNanjing
Period6/12/248/12/24

Keywords

  • distributed current measurement
  • insulation material modification
  • neural network
  • wide DBD

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