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Online health status monitoring of high voltage insulators using deep learning model

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Abstract

The inspection of electrical components has long been an important issue in the power distribution system. Unmanned drones are impressive surveillance systems with a powerful spatial and remote sensing capability. This paper proposes a system to monitor the health of the ceramic insulators that uses aerial images as a source of information and deep structured learning model for the data interpretation. The key drawbacks of existing monitoring systems are poor detection accuracy and lack of real-time execution, making it more complicated to obtain attributes from aerial photographs. The focus of this paper is to increase accuracy of detection while operating in real-time using You Only Look Once version 3 (YOLOv3). The novelty of the proposed system is that it combines deep learning and the Internet of Things using a single embedded device called Raspberry Pi. For the scientific investigation, we equipped Raspberry Pi with a test image as an input to detect an insulator’s health status using YOLOv3. Many aerial images are not clear due to motion blur. Excluding such low-resolution training images will affect accuracy. So we used a super-resolution CNN to reconstruct a blurred image as high-resolution image. The efficiency of the proposed system has been tested using a private data set consisting of a variety of scenes containing high-voltage power line insulators. The results show that the suggested system is quick and accurate in the identification and classification of insulators.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India

    Dipu Sarkar

  2. Department of Electronics and Instrumentation Engineering, National Institute of Technology Nagaland, Dimapur, 797103, India

    Sravan Kumar Gunturi

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  1. Dipu Sarkar
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  2. Sravan Kumar Gunturi
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Sarkar, D., Gunturi, S.K. Online health status monitoring of high voltage insulators using deep learning model. Vis Comput 38, 4457–4468 (2022). https://doi.org/10.1007/s00371-021-02308-x

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