High Impedance Fault Location Methods: Review and Harmonic Selection-based Analysis

• Published in: IEEE open access journal of power and energy Vol. 10; p. 1
• Main Authors: Lopes, Gabriela N., Menezes, Thiago S., Gomes, Douglas P. S., Vieira, Jose Carlos M.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-01-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Conductors; Distribution system; Electric contacts; Fault currents; Fault location; Frequency measurement; High impedance; high impedance fault location; Impedance; Machine learning; Mathematical models; Measurement; random forest; Stockwell transform; Supervised learning; Wildfires
• ISSN: 2687-7910; 2687-7910; 2644-1314
• DOI: 10.1109/OAJPE.2023.3244341

High Impedance Faults (HIFs) are recurring events in electrical Distribution Systems (DSs) and occur by the contact between energized conductors and high impedance surfaces. HIFs may pose hazards to living beings and cause bushfires. However, the HIF protection has not been completely solved due to the small fault current and varying impedance, inhibiting traditional protection techniques from functioning correctly. In the literature, researchers have mainly focused on detection techniques. Thus, the development of HIF Location Methods (HIFLMs) is recent, and evidences for conclusive solutions are still lacking. Moreover, to this date, no existing study reviews the main challenges concerning HIFLMs in DSs. This paper proposes a systematic analysis of the common stages to design the main existing HIFLMs. The strategy is evaluating the similar characteristics that pose a common research path regarding challenges faced in real-world conditions. Additionally, this paper proposes a case study to assess the best input signals, metrics, and machine learning-based decision algorithms of a new HIFLM. The results are promising, with high identification rates, even in noisy conditions. The methodology can help to select the datasets for supervised learning-based HIFLM. Highlighting the state-of-art of current methods and support development of HIFLMs are this paper's main contributions.