  Diverse fault types, fast re-closures, and complicated transient states after
a fault event make real-time fault location in power grids challenging.
Existing localization techniques in this area rely on simplistic assumptions,
such as static loads, or require much higher sampling rates or total
measurement availability. This paper proposes a faulted line localization
method based on a Convolutional Neural Network (CNN) classifier using bus
voltages. Unlike prior data-driven methods, the proposed classifier is based on
features with physical interpretations that improve the robustness of the
location performance. The accuracy of our CNN based localization tool is
demonstrably superior to other machine learning classifiers in the literature.
To further improve the location performance, a joint phasor measurement units
(PMU) placement strategy is proposed and validated against other methods. A
significant aspect of our methodology is that under very low observability (7%
of buses), the algorithm is still able to localize the faulted line to a small
neighborhood with high probability. The performance of our scheme is validated
through simulations of faults of various types in the IEEE 39-bus and 68-bus
power systems under varying uncertain conditions, system observability, and
measurement quality.
