Resilient Identification of Distribution Network Topology

TL;DR

This paper presents a resilient, real-time network topology identification method for distribution networks using discriminant analysis, quadratic programming for data recovery, and Bayesian techniques to detect anomalies and cyber-attacks.

Contribution

It introduces a novel, low-complexity approach combining discriminant analysis, quadratic programming, and Bayesian methods for resilient topology identification in distribution networks.

Findings

Method accurately identifies network configuration and device status.

Approach is resilient to communication failures and cyber-attacks.

Suitable for real-time implementation with low computational cost.

Abstract

Network topology identification (TI) is an essential function for distributed energy resources management systems (DERMS) to organize and operate widespread distributed energy resources (DERs). In this paper, discriminant analysis (DA) is deployed to develop a network TI function that relies only on the measurements available to DERMS. The propounded method is able to identify the network switching configuration, as well as the status of protective devices. Following, to improve the TI resiliency against the interruption of communication channels, a quadratic programming optimization approach is proposed to recover the missing signals. By deploying the propounded data recovery approach and Bayes' theorem together, a benchmark is developed afterward to identify anomalous measurements. This benchmark can make the TI function resilient against cyber-attacks. Having a low computational burden, this approach is fast-track and can be applied in real-time applications. Sensitivity analysis is performed to assess the contribution of different measurements and the impact of the system load type and loading level on the performance of the proposed approach.