Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines
Dublin Core
Title
Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines
Subject
Flashover
Smart grid
Transmission and distribution line
IoT
Description
Flashover on transmission and distribution line insulators occurs when the insulator’s resistance drops to a critical level and causes frequent power outages. Thin layers of dust, salt, and airborne particles, gradually deposited on the surface of insulators, as well as humidity, form an electrolyte which causes flashover. In this paper, a flashover prevention system using IoT technology and machine learning is proposed in order to reduce loss and increase power reliability. The system includes an IoT module, a service and clients. The IoT module prototype was installed at a distribution line pole located in Pracha-utit, Bangkok, Thailand and had collected data for thirty-four months. The data were pre-processed and split for the training process and evaluation. In this study, we built and compared four models including linear regression, polynomial regression, Auto-regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The results revealed that the LSTM model outperformed (R2=.931, RMSE= 530.74) the others.
Creator
Saraubon, Kobkiat
Wiriyanuruknakon, Nuttapong
Tangthirasunun, Natdanai
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 11 (2021); pp. 34-48
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2021-06-04
Rights
Copyright (c) 2021 Kobkiat Saraubon
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Kobkiat Saraubon, Nuttapong Wiriyanuruknakon and Natdanai Tangthirasunun, Flashover Prevention System using IoT and Machine Learning for Transmission and Distribution Lines, International Association of Online Engineering (IAOE), Vienna, Austria, 2021, accessed November 7, 2024, https://igi.indrastra.com/items/show/1951