  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.
