Distribution System Outage Detection using Consumer Load and Line Flow Measurements

TL;DR

This paper presents a novel outage detection framework for power distribution networks that combines real-time measurements and load forecasts, optimizing sensor placement to improve detection reliability.

Contribution

It introduces a MAP-based outage detection method for tree-structured networks and an optimal sensor placement framework based on missed detection probability.

Findings

A 10% reduction in detection reliability decreases sensor density by 60%.

The proposed method effectively detects outages using real-time data and load forecasts.

Case studies demonstrate improved detection efficiency in real distribution feeders.

Abstract

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.