Support vector machine based fault location identification in microgrids using interharmonic injection

Alireza Forouzesh, Mohammad S. Golsorkhi*, Mehdi Savaghebi*, Mehdi Baharizadeh

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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%.

Original languageEnglish
Article number2317
Issue number8
Number of pages14
Publication statusPublished - 20. Apr 2021


  • Fault location
  • Harmonics
  • Machine learning
  • Microgrid
  • Power electronics
  • Protection


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