Artificial intelligence approaches for accurate assessment of insulator cleanliness in high-voltage electrical systems

Abstract

String insulators play a critical role in electrical grids by isolating high voltage and preventing energy dispersion through the tower structure. Maintaining the cleanliness of these insulators is essential to ensure optimum performance and avoid malfunctions. Traditionally, human visual inspection has been used to assess cleaning needs, which can be error prone and pose a safety risk to personnel working near electrical equipment. Accurate detection of insulator condition is essential to prevent equipment failure. In this study, we used a comprehensive dataset of insulator images generated in Brazil using computer-aided design software and a game engine. The dataset consists of 14,424 images, categorized into those affected by salt, soot, and other contaminants, and clean insulators. We extracted key features from these images using VggNet and GoogleNet and classified them using a random forest algorithm, achieving a classification accuracy of 98.99%. This represents a 0.99% improvement over previous studies using the same dataset. Our research makes a significant contribution to the field by providing a more effective method for isolator management. By using advanced artificial intelligence models for accurate classification and real-time analysis, our approach improves the efficiency and reliability of insulator condition monitoring. This advance not only improves the detection of various insulator conditions but also reduces the reliance on manual inspections, which are often inaccurate and inefficient.