Distribution System Outage Detection using Consumer Load and Line Flow Measurements
This work addresses the problem of outage detection for power distribution system operators, offering a method to reduce sensor deployment costs while maintaining reliability.
The paper proposes a framework for detecting outages in power distribution networks by combining real-time power flow measurements with load forecasts, using a maximum a posteriori detector. Results show that a 10% loss in detection reliability reduces required sensor density by 60%.
An outage detection framework for power distribution networks is proposed. Given the tree structure of the distribution system, a method is developed combining the use of real-time power flow measurements on edges of the tree with load forecasts at the nodes of the tree. A maximum a posteriori detector {\color{black} (MAP)} is formulated for arbitrary number and location of outages on trees which is shown to have an efficient detector. A framework relying on the maximum missed detection probability is used for optimal sensor placement and is solved for tree networks. Finally, a set of case studies is considered using feeder data from the Pacific Northwest National Laboratories. We show that a 10\% loss in mean detection reliability network wide reduces the required sensor density by 60 \% for a typical feeder if efficient use of measurements is performed.