High Voltage Insulators Condition Analysis using Convolutional Neural Network

Arailym Serikbay; Mehdi Bagheri; Amin Zollanvari

doi:10.1109/EEEIC/ICPSEurope51590.2021.9584624

High Voltage Insulators Condition Analysis using Convolutional Neural Network

Arailym Serikbay, Mehdi Bagheri, Amin Zollanvari

Nazarbayev University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

High voltage insulators are nonconductive materials that are in use to isolate the conductor from the earthed transmission tower. However, they are regularly subjected to operational environmental contamination and detrimental electrical or mechanical stress. Undesirable operational conditions can cause insulations' failure and ultimately the power transmission line outage. Therefore, monitoring and condition analysis of the insulators are essential tasks for the utility operators as well as transmission companies. With the widespread applications of deep learning techniques in engineering, in this study, we propose Convolutional Neural Network (CNN) as the backbone data analysis method and aerial images as the primary dataset. We conducted a model selection through an exhaustive search within a limited hyperparameter space based on our computational resources. We show that the constructed CNN classifier achieved a remarkable accuracy of 83.3% in classifying a clean insulator surface from the one covered with water droplets.

Original language	English
Title of host publication	21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings
Editors	Zbigniew M. Leonowicz
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665436120
DOIs	https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584624
Publication status	Published - 2021
Event	21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Bari, Italy Duration: Sept 7 2021 → Sept 10 2021

Publication series

Name	21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings

Conference

Conference	21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021
Country/Territory	Italy
City	Bari
Period	9/7/21 → 9/10/21

Funding

This work was supported in part by Collaborative Research Project (CRP) Grant of Nazarbayev University under grant 021220CRP0322.

Keywords

condition monitoring
Convolutional Neural Network
Outdoor insulator
TensorFlow
Unmanned Aerial Vehicle
Deep Learning
Machine Learning

ASJC Scopus subject areas

Artificial Intelligence
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Environmental Engineering
Control and Optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/EEEIC/ICPSEurope51590.2021.9584624

Cite this

Serikbay, A., Bagheri, M., & Zollanvari, A. (2021). High Voltage Insulators Condition Analysis using Convolutional Neural Network. In Z. M. Leonowicz (Ed.), 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings (21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584624

High Voltage Insulators Condition Analysis using Convolutional Neural Network. / Serikbay, Arailym; Bagheri, Mehdi ; Zollanvari, Amin.
21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings. ed. / Zbigniew M. Leonowicz. Institute of Electrical and Electronics Engineers Inc., 2021. (21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Serikbay, A, Bagheri, M & Zollanvari, A 2021, High Voltage Insulators Condition Analysis using Convolutional Neural Network. in ZM Leonowicz (ed.), 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings. 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021, Bari, Italy, 9/7/21. https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584624

Serikbay A, Bagheri M , Zollanvari A. High Voltage Insulators Condition Analysis using Convolutional Neural Network. In Leonowicz ZM, editor, 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. (21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings). doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584624

Serikbay, Arailym ; Bagheri, Mehdi ; Zollanvari, Amin. / High Voltage Insulators Condition Analysis using Convolutional Neural Network. 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings. editor / Zbigniew M. Leonowicz. Institute of Electrical and Electronics Engineers Inc., 2021. (21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings).

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abstract = "High voltage insulators are nonconductive materials that are in use to isolate the conductor from the earthed transmission tower. However, they are regularly subjected to operational environmental contamination and detrimental electrical or mechanical stress. Undesirable operational conditions can cause insulations' failure and ultimately the power transmission line outage. Therefore, monitoring and condition analysis of the insulators are essential tasks for the utility operators as well as transmission companies. With the widespread applications of deep learning techniques in engineering, in this study, we propose Convolutional Neural Network (CNN) as the backbone data analysis method and aerial images as the primary dataset. We conducted a model selection through an exhaustive search within a limited hyperparameter space based on our computational resources. We show that the constructed CNN classifier achieved a remarkable accuracy of 83.3\% in classifying a clean insulator surface from the one covered with water droplets.",

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author = "Arailym Serikbay and Mehdi Bagheri and Amin Zollanvari",

note = "Funding Information: This work was supported in part by Collaborative Research Project (CRP) Grant of Nazarbayev University under grant 021220CRP0322. Publisher Copyright: {\textcopyright} 2021 IEEE; 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 ; Conference date: 07-09-2021 Through 10-09-2021",

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AB - High voltage insulators are nonconductive materials that are in use to isolate the conductor from the earthed transmission tower. However, they are regularly subjected to operational environmental contamination and detrimental electrical or mechanical stress. Undesirable operational conditions can cause insulations' failure and ultimately the power transmission line outage. Therefore, monitoring and condition analysis of the insulators are essential tasks for the utility operators as well as transmission companies. With the widespread applications of deep learning techniques in engineering, in this study, we propose Convolutional Neural Network (CNN) as the backbone data analysis method and aerial images as the primary dataset. We conducted a model selection through an exhaustive search within a limited hyperparameter space based on our computational resources. We show that the constructed CNN classifier achieved a remarkable accuracy of 83.3% in classifying a clean insulator surface from the one covered with water droplets.

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High Voltage Insulators Condition Analysis using Convolutional Neural Network

Abstract

Publication series

Conference

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

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