TY - GEN
T1 - Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on Distributed Current Measurement and Neural Network Model
AU - Yizhuo, Wang
AU - Zhonlian, Li
AU - Long, Li
AU - Runhua, Li
AU - Xinglei, Cui
AU - Zhi, Fang
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - distributed current measurement
KW - insulation material modification
KW - neural network
KW - wide DBD
UR - http://www.scopus.com/inward/record.url?scp=105003628419&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-4856-6_8
DO - 10.1007/978-981-96-4856-6_8
M3 - 会议稿件
AN - SCOPUS:105003628419
SN - 9789819648559
T3 - Lecture Notes in Electrical Engineering
SP - 89
EP - 103
BT - Proceedings of the 1st Electrical Artificial Intelligence Conference, Volume 1 - EAIC 2024
A2 - Qu, Ronghai
A2 - Song, Zhengxiang
A2 - Ding, Zhiming
A2 - Mu, Gang
A2 - Xiong, Rui
A2 - Han, Li
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st Electrical Artificial Intelligence Conference, EAIC 2024
Y2 - 6 December 2024 through 8 December 2024
ER -