TY - GEN
T1 - Remote Monitoring of Outdoor High Voltage Insulator using Deep Learning-based Image Processing
AU - Baktiyar, Akzhol
AU - Baizhan, Darkhan
AU - Bagheri, Mehdi
AU - Zollanvari, Amin
AU - Murzabulatov, Alimzhan
AU - Serikbay, Arailym
N1 - Funding Information:
The work was supported in part by the Faculty Development Competitive Research Grant (FDCRG) of Nazarbayev University (Grant no. 021220FD1251).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - The outdoor high voltage insulators are some of the essential parts for electrical and mechanical assistance of transmission lines. Monitoring the insulators' health frequently on a regular basis is an indispensable routine for ensuring uninterrupted power transmission. Traditionally, the monitoring is accomplished manually by linemen, which is time-consuming and implies additional logistics costs due to the long distances and various terrains associated with transmission lines. One of the most economic solutions can be condition monitoring by analysis of insulators' images using Unmanned Aerial Vehicles (UAVs). In this regard, this study focuses on outdoor high-voltage insulators' health monitoring by deep learning-based classification of hazardous surface conditions. To this end, Convolutional Neural Networks (CNNs) with hyperparameter optimization as well as fine-tuning pretrained deep learning models are employed to classify insulator surface conditions into one of the following four categories: clean, or covered either with snow, ice, or dust. Applying cross-validation external to the CNN hyperparameter optimization and the fine-tuning process of pretrained models yielded remarkable accuracies of 92.07% and 97.80%, respectively.
AB - The outdoor high voltage insulators are some of the essential parts for electrical and mechanical assistance of transmission lines. Monitoring the insulators' health frequently on a regular basis is an indispensable routine for ensuring uninterrupted power transmission. Traditionally, the monitoring is accomplished manually by linemen, which is time-consuming and implies additional logistics costs due to the long distances and various terrains associated with transmission lines. One of the most economic solutions can be condition monitoring by analysis of insulators' images using Unmanned Aerial Vehicles (UAVs). In this regard, this study focuses on outdoor high-voltage insulators' health monitoring by deep learning-based classification of hazardous surface conditions. To this end, Convolutional Neural Networks (CNNs) with hyperparameter optimization as well as fine-tuning pretrained deep learning models are employed to classify insulator surface conditions into one of the following four categories: clean, or covered either with snow, ice, or dust. Applying cross-validation external to the CNN hyperparameter optimization and the fine-tuning process of pretrained models yielded remarkable accuracies of 92.07% and 97.80%, respectively.
KW - convolutional neural networks
KW - deep learning
KW - High-voltage insulator
KW - insulator surface condition
KW - transfer learning
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U2 - 10.1109/EEEIC/ICPSEurope51590.2021.9584666
DO - 10.1109/EEEIC/ICPSEurope51590.2021.9584666
M3 - Conference contribution
AN - SCOPUS:85126461267
T3 - 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
BT - 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
A2 - Leonowicz, Zbigniew M.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 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
Y2 - 7 September 2021 through 10 September 2021
ER -