Detection and classification of faults and disturbances in future power systems

Increasing reliance on electrical energy in modern times has led to a steady increase in utilization of power grids. Also with the penetration of more intermittent renewable sources and loads, higher variability on power flow is being observed. This dynamic nature of power transfer in the grid will induce high stress on an ageing infrastructure calling for a major investments to maintain reliable power supply. In project Resident, we strive towards developing advance methods that assist in safe exploitation of existing, expensive grid infrastructures, so that it works efficiently in conjunction with further new grid development, thereby eliminating the concept of refurbishing the existing grid infrastructure. With the development of synchro-phasor based wide area monitoring systems, sophisticated  tools offering quicker and accurate monitoring solutions can be obtained. Also, tools like real-time state estimator and Dynamic thermal rating algorithms has further improved live grid observability and assessment of threshold line current capacity respectively. Acknowledging these advancements, I develop robust algorithms to classify, detect and  locate disturbances. These disturbances in the long run, if unnoticed, may lead to major faults and cascading outages. Fast acting statistical algorithms and novel artificial intelligence based tools will be explored to classify the faults and suggest preventive and/or post-contingency control measures to avoid cascading outages