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MRI India Journals Vol. 9 No. 1s (2026): Special Issue

Hybrid AI Models for Enhanced Harmonic Source Localization in Renewable-Dominated Microgrids

Authors

  • Harshwardhan Sachin Pande Department of Electrical Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune, Maharashtra, India
  • Rupalee S. Ambekar Department of Electrical Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune, Maharashtra, India
  • Rajesh M. Holmukhe Department of Electrical Engineering, Bharati Vidyapeeth (Deemed to be University) College of Engineering Pune, Maharashtra, India

DOI:

https://doi.org/10.65521/oaijse.v9i1s.3693

Keywords:

Graph Neural Networks Transformer Architecture Harmonic Source Localization Renewable-Dominated Microgrids Power Quality Analysis Artificial Intelligence

Abstract

This paper proposes a hybrid artificial intelligence framework that combines Graph Neural Networks (GNNs) and Transformer architectures for harmonic source localization in renewable-dominated microgrids. The proposed method utilizes spatial information derived from network topology along with temporal harmonic measurements to improve detection accuracy. Simulation scenarios based on a modified IEEE 34-bus microgrid demonstrate the effectiveness of the proposed approach under different operating conditions including grid-connected and islanded modes.

 

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Published

2026-06-25

How to Cite

Pande, H. S., Ambekar, R. S., & Holmukhe, R. M. (2026). Hybrid AI Models for Enhanced Harmonic Source Localization in Renewable-Dominated Microgrids. Open Access International Journal of Science and Engineering , 9(1s), 181–184. https://doi.org/10.65521/oaijse.v9i1s.3693

Issue

Vol. 9 No. 1s (2026): Special Issue

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.