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Efficient outage location is critical to enhancing the resilience of powerdistribution systems. However, accurate outage location requires combiningmassive 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 acomputationally complex task due to the high dimensionality of data indistribution grids. In this paper, we propose a multi-source data fusionapproach to locate outage events in partially observable distribution systemsusing Bayesian networks (BNs). A novel aspect of the proposed approach is thatit takes multi-source evidence and the complex structure of distributionsystems into account using a probabilistic graphical method. Our method canradically reduce the computational complexity of outage location inference inhigh-dimensional spaces. The graphical structure of the proposed BN isestablished based on the network's topology and the causal relationship betweenrandom variables, such as the states of branches/customers and evidence.Utilizing this graphical model, accurate outage locations are obtained byleveraging a Gibbs sampling (GS) method, to infer the probabilities ofde-energization for all branches. Compared with commonly-used exact inferencemethods that have exponential complexity in the size of the BN, GS quantifiesthe target conditional probability distributions in a timely manner. A casestudy of several real-world distribution systems is presented to validate theproposed method.

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