A High-Accuracy of Transmission Line Faults (TLFs) Classification based on Convolutional Neural Network

Authors

  • Syifaul Fuada Universitas Pendidikan Indonesia
  • Hasbi Ash Shiddieqy University Center of Excellence on Microelectronics, Institut Teknologi Bandung
  • Trio Adiono Electrical Engineering Department, School of Electrical Engineering and Informatics, Institut Teknologi Bandung

Abstract

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.

Author Biographies

Syifaul Fuada, Universitas Pendidikan Indonesia

Syifaul Fuada received a B.A. in Electrical Engineering Education from Universitas Negeri Malang (UM), Indonesia, and an M.Sc. in Electrical Engineering option Microelectronics from the School of Electrical Engineering and Informatics, Institut Teknologi Bandung (ITB), Indonesia. He was with the University Center of Excellence at Microelectronics ITB from 2016-2018 as a main researcher. Now, he is with the Program Studi Sistem Telekomunikasi Universitas Pendidikan Indonesia (UPI) as a Lecturer. His research interests include analog circuit design and instrumentation, circuit simulation, engineering education, IoT, multimedia learning development and Visible Light Communication.

Hasbi Ash Shiddieqy, University Center of Excellence on Microelectronics, Institut Teknologi Bandung

Hasbi Ash Shiddieqy received a B.Eng. in electrical engineering from Telkom University (Tel-U), Indonesia, in 2013 and M.Sc. in Electrical Engineering, Microelectronics option from Institut Teknologi Bandung (ITB), Indonesia, in 2018. Currently, he is with the University Center of Excellence on Microelectronics, ITB. His research interests include VLSI design, machine learning, power system, and embedded system.

Trio Adiono, Electrical Engineering Department, School of Electrical Engineering and Informatics, Institut Teknologi Bandung

Trio Adiono received a B.Eng. in electrical engineering and an M.Eng. in microelectronics from Institut Teknologi Bandung (ITB), Indonesia, in 1994 and 1996, respectively. He obtained his Ph.D. in VLSI Design from the Tokyo Institute of Technology, Japan, in 2002. He holds a Japanese Patent on a High-Quality Video Compression System. He is now a Full professor and a senior lecturer at the School of Electrical Engineering and Informatics. He formerly serves as the Head of the Microelectronics Center, ITB. His research interests include VLSI design, signal and image processing, VLC, smart cards, and electronics solution design and integration.

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Published

2024-04-19

Issue

Vol. 66 No. 4 (2020)

Section

Power, Industrial Electronics

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