Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions

Arailym Serikbay, Mehdi Bagheri (), Amin Zollanvari and B. T. Phung
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Arailym Serikbay: Department of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
Mehdi Bagheri: Department of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
Amin Zollanvari: Department of Electrical and Computer Engineering, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana 010000, Kazakhstan
B. T. Phung: School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia

Energies, 2024, vol. 17, issue 22, 1-17

Abstract: Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures.

Keywords: deep learning; ensemble learning; condition assessment; high-voltage insulators; contamination classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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