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

Wenting Li, Deepjyoti Deka, Michael Chertkov, Meng Wang

Research output: Contribution to journalArticlepeer-review

62 Scopus citations

Abstract

Diverse fault types, fast reclosures, 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 placement strategy are 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.

Original languageEnglish (US)
Article number8718345
Pages (from-to)4640-4651
Number of pages12
JournalIEEE Transactions on Power Systems
Volume34
Issue number6
DOIs
StatePublished - Nov 2019
Externally publishedYes

Keywords

  • Fault location
  • PMU placement
  • deep learning
  • feature extraction
  • phasor measurement unit (PMU)
  • real-time

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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