Categorizing 15 kV High-Voltage HDPE Insulator’s Leakage Current Surges Based on Convolution Neural Network Gated Recurrent Unit

Wen-Bin Liu, Phuong Nguyen Thanh, Ming-Yuan Cho and Thao Nguyen Da ()
Additional contact information
Wen-Bin Liu: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 800, Taiwan
Phuong Nguyen Thanh: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 800, Taiwan
Ming-Yuan Cho: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 800, Taiwan
Thao Nguyen Da: Department of Business Intelligence, National Kaohsiung University of Science and Technology, Kaohsiung 800, Taiwan

Energies, 2023, vol. 16, issue 5, 1-19

Abstract: The leakage currents are appropriate for determining the contamination level of insulators in the power distribution system, which are efficiently cleaned or replaced during the maintenance schedule. In this research, the hybrid convolution neural network and gated recurrent unit model (CNN-GRU) are developed to categorize the leakage current pulse of the 15 kV HDPE insulator in the transmission towers in Taiwan. Many weather parameters are accumulated in the online monitoring system, which is installed in different transmission towers in coastal areas that suffer from heavy pollution. The Pearson correlation matrix is computed for selecting the high correlative features with the leakage current. Hyperparameter optimization is employed to decide the enhancing framework of the CNN-GRU methodology. The performance of the CNN-GRU is completely analyzed with other deep learning algorithms, which comprise the GRU, bidirectional GRU, LSTM, and bidirectional LSTM. The developed CNN-GRU acquired the most remarkable improvements of 79.48% CRE, 83.54% validating CRE, 14.14% CP, 20.89% validating CP, 66.24% MAE, 63.59% validating MAE, 73.24% MSE, and 71.59% validating MSE benchmarks compared with other methodologies. Therefore, the hybrid CNN-GRU methodology provides comprehensive information about the contamination degrees of insulator surfaces derived from the property of leakage currents.

Keywords: classify 15 kV HDPE insulator’s leakage current; convolutional neural network; gated recurrent unit; deep learning machine; hyperparameter optimization (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
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/5/2500/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/5/2500/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:5:p:2500-:d:1089260

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().