Optimal Sensor Placement for Topology Identification in Smart Power Grids

TL;DR

This work addresses real-time topology observability in radial smart power distribution networks under outages and potential topology attacks. It introduces a binary-cost optimization formulation that selects node sensors and line sensors to enable outage detection using linearized DistFlow measurements, without relying on load forecasts, and accounts for zero-injection nodes. A key contribution is the explicit identification of uniquely identifiable outage sets ${\cal H}_U$ and constraints that guarantee detection of all such outages while minimizing deployment cost. Numerical results on a 30-node feeder illustrate how sensor allocations vary with sensor costs and network zero-injection patterns, providing a practical, cost-effective approach to topology identification and outage detection in distribution grids.

Abstract

Accurate network topology information is critical for secure operation of smart power distribution systems. Line outages can change the operational topology of a distribution network. As a result, topology identification by detecting outages is an important task to avoid mismatch between the {topology that the operator believes is present and the actual topology}. Power distribution systems are operated as radial trees and are recently adopting the integration of sensors to monitor the network in real time. In this paper, an optimal sensor placement solution is proposed that enables outage detection through statistical tests based on sensor measurements. Using two types of sensors, node sensors and line sensors, we propose a novel formulation for the optimal sensor placement as a cost optimization problem with binary decision variables, i.e., {to place or not place a sensor at each bus/line}. The advantage of the proposed placement strategy for outage detection is that it incorporates various types of sensors, is independent of load forecast statistics and is cost effective. Numerical results illustrating the placement solution are presented.

Figures (6)

• Figure 1: Causes for a distribution network topology change
• Figure 2: A distribution network represented as a tree.
• Figure 3: Sensor placement
• Figure 4: Test feeder: Case 1
• Figure 5: Test feeder: Case 2