Enhancing busbar safety: a smart thermal imaging pipeline for real-time load monitoring

Busbars are essential components in electrical power distribution but are susceptible to overheating under high loads, potentially leading to system failures and equipment damage. Traditional overheating detection methods are often labour-intensive and require system shutdowns. To address these issues, we developed a smart, non-destructive system utilising thermal imaging for continuous monitoring of busbars. Thermal imaging offers non-invasive detection of temperature anomalies without interrupting operations, enabling early identification of overheating and supporting preventative maintenance. Our system combines the Hjorth thermal pattern extraction method with a novel one-dimensional convolutional neural network, BusThermNet. A new dataset of thermal images capturing busbar conditions at low and high loads has been created for this study. The Hjorth method analyzes busbar dynamics by evaluating activity, mobility, and complexity, while BusThermNet classifies the data into low- and high-load categories. Upon validation with the new dataset, our approach achieved an accuracy of 82.47%, with a precision of 0.89, recall of 0.74, and an F1 score of 0.81, outperforming existing techniques. These results highlight the potential of our system as a valuable tool for electrical regulatory authorities. Its capabilities for early issue detection, real-time monitoring, and reduced system disruptions make it an effective proactive solution for busbar management, ideal for adoption by regulatory bodies and industry stakeholders.