Autonomous Health Monitoring of Energy Distribution Systems

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An energy distribution network has thousands of points to monitor. While real-time gathering of such data is quite possible with**current technology, it is seldom practiced. This is because the amount of data overwhelms manual attempts to monitor, and**without proper interpretation, the value of data is lost. Thus, utility companies rely on preventative maintenance schedules, which**comprise of extensive infrastructure audits. As a result, any sudden degradation of network health remains undetected, yielding**preventable energy losses and service outages. According to Blackout Tracker, Canada Annual Report 2016, Ontario had the**highest number of power outages among Canadian provinces and during 2016, total number of 162 outages with the average of**52 minutes per outage, affected the life of 641,364 people in Ontario. In this project, we will develop a major enabling technology**towards monitoring the health of a distribution network: algorithms for fully autonomous monitoring and diagnosis enabled by the**real-time data collected from strategic locations within the distribution network. This will allow electricity providers to have full**knowledge of their infrastructures operations and health so as to (1) adaptively streamline its operations, and (2) predict and**trigger maintenance efforts to minimize service interruptions.