Enhance High Impedance Fault Detection and Location Accuracy via \mu -PMUs

Qiushi Cui, Yang Weng

Research output: Contribution to journalArticle

Abstract

The high impedance fault (HIF) has random, irregular, and unsymmetrical characteristics, making such a fault difficult to detect in distribution grids via conventional relay measurements with relatively low resolution and accuracy. This paper proposes a stochastic HIF monitoring and location scheme using high-resolution time-synchronized data in \mu -PMUs for distribution network protection. Specifically, we systematically design a process based on feature selections, semi-supervised learning (SSL), and probabilistic learning for fault detection and location. For example, a wrapper method is proposed to leverage output data in feature selection to avoid overfitting and reduce communication demand. To utilize unlabeled data and quantify uncertainties, an SSL-based method is proposed using the information theory for fault detection. For location, a probabilistic analysis is proposed via moving window total least square based on the probability distribution of the fault impedance. For numerical validation, we set up an experiment platform based on the real-time simulator, so that the real-time property of \mu -PMU can be examined. Such experiment shows enhanced HIF detection and location, when compared to the traditional methods.

Original languageEnglish (US)
Article number8755317
Pages (from-to)797-809
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume11
Issue number1
DOIs
StatePublished - Jan 2020

Keywords

  • fault location
  • High impedance fault
  • semi-supervised learning
  • μ-PMUs

ASJC Scopus subject areas

  • Computer Science(all)

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