  High impedance fault (HIF) has been a challenging task to detect in
distribution networks. On one hand, although several types of HIF models are
available for HIF study, they are still not exhibiting 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 comparing with other traditional methods.
