Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

Muhammad Uzair, Mohsen Eskandari (), Li Li and Jianguo Zhu
Additional contact information
Muhammad Uzair: School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Mohsen Eskandari: School of Electrical Engineering and Telecommunication, University of New South Wales, Sydney, NSW 2052, Australia
Li Li: School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Jianguo Zhu: School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia

Energies, 2022, vol. 15, issue 24, 1-19

Abstract: The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.

Keywords: machine learning; AC microgrid protection; fault detection; fault type classification; faulted phase identification; feature extraction; peaks metric; max factor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/24/9397/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/24/9397/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:24:p:9397-:d:1001031

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().