Real-Time Topology Detection and State Estimation in Distribution Systems Using Micro-PMU and Smart Meter Data

TL;DR

This paper introduces MIQP-based models using micro-PMU and smart meter data for real-time distribution network topology detection and state estimation, capable of handling multiple switching actions and various network configurations.

Contribution

It presents two novel AC optimal power flow formulations, PPV and RIV, for simultaneous topology detection and state estimation using limited sensor data.

Findings

Both models accurately identify topology and states in real-time.

RIV model outperforms PPV in accuracy.

Models handle multiple switching actions and different network types.

Abstract

Distribution network topology detection and state estimation in real-time are critical for modern distribution systems management and control. However, number of sensors in distribution networks are limited and communication links between switch devices and distribution management system are not well-established. In this regard, this paper proposes mixed-integer quadratic programming (MIQP) formulations to determine the topology of distribution network and estimate distribution system states simultaneously using micro-phasor measurement units (micro-PMUs) and smart meter data. Two approaches based on AC optimal power flow are proposed and analyzed: (i) polar power-voltage (PPV) formulation, and (ii) rectangular current-voltage (RIV) formulation. The proposed models include convex objective function while constraints are linearized using first-order approximation of Taylor series and Big M method. The proposed models can identify multiple simultaneous switching actions at each time instant and different topology configurations including radial and meshed networks. Only measurement data at each time interval is needed to identify topology and states of the system correctly. The proposed models are tested on a modified IEEE-33 bus system with realistic load data from Pecan Street Inc. database. The results confirm that both models can identify system topology and states with remarkable accuracy in real-time, while RIV model outperforms PPV model.