Support Vector Machine Based Fault Location Identification in Microgrids Using Interharmonic Injection

Alireza Forouzesh, Mohammad S. Golsorkhi, Mehdi Savaghebi and Mehdi Baharizadeh
Additional contact information
Alireza Forouzesh: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran
Mohammad S. Golsorkhi: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 8415683111, Iran
Mehdi Savaghebi: Electrical Engineering Section, Department of Mechanical and Electrical Engineering, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
Mehdi Baharizadeh: Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan 8418148499, Iran

Energies, 2021, vol. 14, issue 8, 1-14

Abstract: This paper proposes an algorithm for detection and identification of the location of short circuit faults in islanded AC microgrids (MGs) with meshed topology. Considering the low level of fault current and dependency of the current angle on the control strategies, the legacy overcurrent protection schemes are not effective in in islanded MGs. To overcome this issue, the proposed algorithm detects faults based on the rms voltages of the distributed energy resources (DERs) by means of support vector machine classifiers. Upon detection of a fault, the DER which is electrically closest to the fault injects three interharmonic currents. The faulty zone is identified by comparing the magnitude of the interharmonic currents flowing through each zone. Then, the second DER connected to the faulty zone injects distinctive interharmonic currents and the resulting interharmonic voltages are measured at the terminal of each of these DERs. Using the interharmonic voltages as its features, a multi-class support vector machine identifies the fault location within the faulty zone. Simulations are conducted on a test MG to obtain a dataset comprising scenarios with different fault locations, varying fault impedances, and changing loads. The test results show that the proposed algorithm reliably detects the faults and the precision of fault location identification is above 90%.

Keywords: fault location; harmonics; machine learning; microgrid; power electronics; protection (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/8/2317/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/8/2317/ (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:14:y:2021:i:8:p:2317-:d:539568

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 ().