Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution

Abstract

In the critical field of electrical grid maintenance, ensuring the integrity of power line insulators is a primary concern. This study introduces an innovative approach for monitoring the condition of insulators using aerial surveillance via drone-mounted cameras. The proposed method is a composite deep learning framework that integrates the “You Only Look Once” version 3 (YOLO3) model with deep convolutional generative adversarial networks (DCGAN) and super-resolution generative adversarial networks (SRGAN). The YOLO3 model excels in rapidly and accurately detecting insulators, a vital step in assessing their health. Its effectiveness in distinguishing insulators against complex backgrounds enables prompt detection of defects, essential for proactive maintenance. This rapid detection is enhanced by DCGAN's precise classification and SRGAN's image quality improvement, addressing challenges posed by low-resolution drone imagery. The framework's performance was evaluated using metrics such as sensitivity, specificity, accuracy, localization accuracy, damage sensitivity, and false alarm rate. Results show that the SRGAN+DCGAN+YOLO3 model significantly outperforms existing methods, with a sensitivity of 98%, specificity of 94%, an overall accuracy of 95.6%, localization accuracy of 90%, damage sensitivity of 92%, and a reduced false alarm rate of 8%. This advanced hybrid approach not only improves the detection and classification of insulator conditions but also contributes substantially to the maintenance and health of power line insulators, thus ensuring the reliability of the electrical power grid.