Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks
This addresses the challenge of fault localization for power system operators, offering an incremental improvement over existing data-driven techniques.
The paper tackles real-time fault location in power grids by proposing a CNN-based classifier using bus voltages with physically interpretable features, achieving superior accuracy compared to other machine learning methods and maintaining performance under low observability (7% of buses).
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.