Multi-Source Data Fusion Outage Location in Distribution Systems via Probabilistic Graph Models

TL;DR

This paper introduces a probabilistic graphical model-based method using Bayesian networks and Gibbs sampling to efficiently fuse multi-source data for accurate outage location in power distribution systems, reducing computational complexity.

Contribution

It presents a novel multi-source data fusion approach with Bayesian networks tailored for outage location, accounting for complex system structure and evidence, improving efficiency over traditional methods.

Findings

Reduces computational complexity of outage inference

Accurately locates outages using multi-source evidence

Validated on real-world distribution systems

Abstract

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network's topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. A case study of several real-world distribution systems is presented to validate the proposed method.