Partial discharge (PD) is a phenomenon that occurs in electrical assets whereby localised energy discharges do not bridge the insulation gap between conductors.  It is a sign that an asset has a defect of some kind, either from manufacture or deterioration.  Initially PD may occur infrequently, occurring more often as the defect worsens until eventually the PD frequency and magnitude becomes sufficiently concerning that the asset needs to be replaced or the discharge bridges the gap causing arcing and failure.  PD monitoring is carried out routinely every three months along with thermovision checks at each substation; it is time consuming, and the monitoring equipment is relatively heavy. More frequent PD checks would increase the probability of getting early warnings of asset deterioration but other than more frequent personnel checks the alternative is continuous monitoring systems that have limitations and are expensive. In the event of a dielectric failure of an asset it is sometimes necessary to establish risk management hazard zones, PD monitoring could be used to mitigate the risks and allow site work to continue. 

This project will address the problem in a number of different ways to improve our ability to identify, locate and diagnose PD related defects as early as possible.  The project will involve a number of workstreams: 

• Use of machine learning techniques to identify the optimal locations for PD monitoring systems
• Improved diagnostic capability for understanding PD patterns taking into account influencing environmental factors using AI
• Demonstration of robot- and drone-mounted PD monitoring – assessing the capabilities and relative merits of both solutions 

Benefits

The project will deliver improved monitoring and can prevent failure, and identify causes earlier thereby reducing outages or allowing them to be aligned with construction activity​
Robots and drones could provide mitigation after a failure as they can enter risk management hazard zones. This could enable work to continue short term, continuous monitoring can allow long term-working.