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 23, 2024, https://igi.indrastra.com/items/show/1951

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