# Real-time Faulted Line Localization and PMU Placement in Power Systems through Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1810.05247 · HTML: https://mlsanity.com/paper/1810.05247/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 14 of 14)

- [Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems](https://mlsanity.com/paper/2102.06349/) (2021)
- [Embedding Power Flow into Machine Learning for Parameter and State Estimation](https://mlsanity.com/paper/2103.14251/) (2021)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [PPGN: Physics-Preserved Graph Networks for Real-Time Fault Location in Distribution Systems with Limited Observation and Labels](https://mlsanity.com/paper/2107.02275/) (2021)
- [A Heterogeneous Graph-Based Multi-Task Learning for Fault Event Diagnosis in Smart Grid](https://mlsanity.com/paper/2309.09921/) (2023)
- [Physics-Constrained Backdoor Attacks on Power System Fault Localization](https://mlsanity.com/paper/2211.04445/) (2022)
- [Which Neural Network to Choose for Post-Fault Localization, Dynamic State Estimation and Optimal Measurement Placement in Power Systems?](https://mlsanity.com/paper/2104.03115/) (2021)
- [Large-Scale Sequential Learning for Recommender and Engineering Systems](https://mlsanity.com/paper/2205.06893/) (2022)
- [Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems](https://mlsanity.com/paper/2206.06789/) (2022)
- [Ranking-Based Physics-Informed Line Failure Detection in Power Grids](https://mlsanity.com/paper/2209.01021/) (2022)
- [1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems](https://mlsanity.com/paper/2211.02930/) (2022)
- [Fault Diagnosis in Power Grids with Large Language Model](https://mlsanity.com/paper/2407.08836/) (2024)
- [Selection of Optimal Number and Location of PMUs for CNN Based Fault Location and Identification](https://mlsanity.com/paper/2509.02192/) (2025)
- [A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction](https://mlsanity.com/paper/2512.24542/) (2025)

## Abstract (arXiv)

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.