AI in arcing‐HIF detection: a brief review. Issue 4 (4th June 2020)

• Record Type: Journal Article
• Authors: Hao, Bai
• Is Part Of: IET smart grid. Volume 3:Issue 4(2020)
• Page Start: 435
• Page End: 444
• Publication Date: 2020-06-04
• Subjects: artificial intelligence -- power distribution faults -- fault diagnosis -- arcs (electric) -- power distribution reliability -- power engineering computing; arcing‐high‐impedance fault detection problems -- arcing‐HIF database construction method -- arcing‐HIF detection‐based AI algorithm -- distribution network‐based AI algorithm -- grounded distribution network -- artificial intelligence method -- data acquisition -- feature extraction -- classifier selection -- reliability; B0170N Reliability -- B8120J Distribution networks -- C6170 Expert systems and other AI software and techniques -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0091
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : In the past few decades, the arcing‐high‐impedance fault (arcing‐HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing‐HIF detection in distribution network‐based AI. First, characteristics and models of arcing‐HIF are analysed, the arcing‐HIF database construction method is also explained; this part is a foundation work for arcing‐HIF detection. Next, arcing‐HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing‐HIF detection algorithm. Finally, the future trends and challenges to arcing‐HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing‐HIF detection‐based AI.