Infrared Thermography-Based Insulator Fault Classification via Unsupervised Clustering and Semi-Supervised Learning

Power substations play a crucial role in ensuring the reliable transmission of electricity to residential and commercial establishments. This paper addresses the critical issue of insulator fault detection in electric substations, emphasizing the importance of timely identification to prevent accidents. Conventional inspection methods relying on manual intervention pose high risk, especially in high-voltage substations. To overcome this, infra-red (IR) thermal cameras are employed, but human analysis of IR thermal images is subjective and time-consuming. The current paper develops a computer vision system uses advanced segmentation algorithms to automatically detect and categorize defects in insulators based on Infrared Thermography (IRT) images. The proposed system combines unsupervised clustering for efficient training with supervised learning for accurate classification. In particular, it first employs clustering techniques for unsupervised learning to reduce human labeling needs. Then, in the supervised learning phase, a Gaussian kernel support vector machine (SVM) algorithm classifies various insulator defects using extracted features. The trained algorithm utilizes the extracted characteristics to effectively identify and classify several fault types in thermal images of insulators. The system’s efficiency in quickly identifying faults enables timely intervention, preventing potential power outages and safety hazards.