Distribution Grid Line Outage Identification with Unknown Pattern and
  Performance Guarantee
Chenhan Xiao Yizheng Liao Yang Weng
Line outage identification in distribution grids is essential for sustainable
grid operation. In this work, we propose a practical yet robust detection
approach that utilizes only readily available voltage magnitudes, eliminating
the need for costly phase angles or power flow data. Given the sensor data,
many existing detection methods based on change-point detection require prior
knowledge of outage patterns, which are unknown for real-world outage
scenarios. To remove this impractical requirement, we propose a data-driven
method to learn the parameters of the post-outage distribution through gradient
descent. However, directly using gradient descent presents feasibility issues.
To address this, we modify our approach by adding a Bregman divergence
constraint to control the trajectory of the parameter updates, which eliminates
the feasibility problems. As timely operation is the key nowadays, we prove
that the optimal parameters can be learned with convergence guarantees via
leveraging the statistical and physical properties of voltage data. We evaluate
our approach using many representative distribution grids and real load
profiles with 17 outage configurations. The results show that we can detect and
localize the outage in a timely manner with only voltage magnitudes and without
assuming a prior knowledge of outage patterns.