Classification of Insulation Defects in High Voltage Equipment Using Computer Vision

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Partial Discharge (PD) analysis is an electrical method for detecting incipient faults in electrical insulation. PD activity causes progressive degradation (ageing) of dielectric materials that are subjected to high electrical stress and can eventually lead to failure. By mapping the apparent charge, phase angle and repetition rate of the discharges with relation to the power frequency sinusoid, phase-resolved partial discharge (PRPD) patterns can be produced which can be used to identify the type of defect present in high voltage equipment. Presently, visual interpretation of PRPD patterns can only be performed by engineers with specialist knowledge. It is very time consuming and expensive to implement due to the large volume of data that needs to be continuously collected and analysed. The project proposes the development of a system that can automate the detection and classification of insulation defects by using computer vision to analyse the PRPD patterns. Unlike other artificial intelligence techniques, computer vision is much more akin to the human approach of interpreting PRPD patterns, hence retaining the fundamental principles of PRPD analysis which relate to the extraction of information directly from visual images. The project will involve experimental work to produce standardised PRPD pattern images from a variety of insulation defects, the development of instrumentation for their processing, and the design, training and validation of computer vision and machine learning models for automated defect detection, classification, and severity reporting. Automation of PRPD analysis can make condition diagnosis of high voltage equipment faster and more accurate. Furthermore, it will make the technique easier and more cost-effective to implement, which in turn will enable the monitoring of a wider range of high voltage assets. Timely and reliable diagnosis can inform maintenance or replacement decisions while providing additional benefits including prevention of catastrophic failure of equipment, increased safety for personnel, and minimisation of losses due to unplanned downtime. The project outputs can be used to modernise industry practices and asset management methodologies leading to more sustainable energy infrastructure.