A Feature Selection Method for High Impedance Fault Detection

Abstract

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. Index TermsHigh impedance fault, distribution network, data mining, feature selection. arXiv:1808.04454v1 [eess.SP] 13 Aug 2018 S KF = {s 5 , s 6 , s 7 , s 8 , s 9 , s 10 } = {KF V a cos H3, KF V b cos H3, KF V c cos H3, KF V a sin H3, KF V b sin H3, KF V c sin H3}

Classification of arc fault between broken conductor and high‐impedance surface: an empirical mode decomposition and Stockwell transform‐based approach

“…Moreover, due to the inherent similarities between the obtained harmonic responses, it is difficult to classify different HIAF events. Few researchers [34] extracted different features from HIF. Therefore, in this study, different machine‐learning techniques are used to get a comparative view of the learning tools.…”

Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders

“…Detailed information regarding the line and the load data are provided in the Appendix A, and further information about this benchmark system can be found in [40]. For an accurate representation of the HIF behavior, the antiparallel diode model of Figure 8, [2,3,5,6,41] is utilized. The HIF model parameters representing seven different faulted surface types are given in Table 1 [2,42].…”

Modeling of operating modes of relay protection in electrical networks with insulated neutral

“…Currently, an in-depth question on the earthing of the network neutral arises at the stage of electrical project design during the construction of new, as well as the reconstruction of the existing medium voltage substations [1][2][3][4]. This problem consists in the fact that there are no clear instructions for solving this problem in the electrical installation rules.…”