A Feature Selection Method for High Impedance Fault Detection

Qiushi Cui, Khalil El-Arroudi, Yang Weng

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


It is a challenging task to detect high impedance fault (HIF) in distribution networks. On one hand, although several types of HIF models are available for HIF study, they still do not exhibit satisfactory fault waveforms. On the other hand, utilizing historical data has been a trend recently for using machine learning methods to improve HIF detection. Nonetheless, most proposed methodologies address the HIF issue starting with investigating a limited group of features, and can hardly provide a practical and implementable solution. This paper, however, proposes a systematic design of feature extraction, based on an HIF detection and classification method. For example, features are extracted according to when, how long, and what magnitude the fault events create. Complementary power expert information is also integrated into the feature pools. Subsequently, we propose a ranking procedure in the feature pool for balancing the information gain and the complexity to avoid over-fitting. For implementing the framework, we create an HIF detection logic from a practical perspective. Numerical methods show the proposed HIF detector has very high dependability and security performance under multiple fault scenarios, as compared with other traditional methods.

Original languageEnglish (US)
Article number8653353
Pages (from-to)1203-1215
Number of pages13
JournalIEEE Transactions on Power Delivery
Issue number3
StatePublished - Jun 2019


  • High impedance fault
  • data mining
  • distribution network
  • feature selection

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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