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This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Mohd Riduan Mohd Shariff
Malaysian Refining Company Sdn. Bhd.
Control & Systems Engineering Electrical & Electronics Engineering

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Identification of harmonic source location in power distribution network Mohd Hatta Jopri; Aleksandr Skamyin; Mustafa Manap; Tole Sutikno; Mohd Riduan Mohd Shariff; Aleksey Belsky
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i2.pp938-949

Abstract

This paper presents the experimental set-up of identification of harmonic source location in the power distribution network using time-frequency analysis, known as S-transform (ST) at the point of common coupling (PCC). S-transform offers high frequency resolution in analyzing the low frequency component and able to represent signal parameters in time-frequency representation (TFR) such as TFR impedance (ZTFR). The proposed method is based on IEEE Std. 1459-2010, ST, and the significant relationship of spectral impedances components (ZS) that been extracted from the ZTFR, consist of the fundamental impedance (Z1) and harmonic impedance (Zh). This experiment was conducted out on an IEEE 4-bus test feeder with a harmonic producing load in numerous different scenarios. The experimental was tested and verified for three consecutive months. The findings of this study reveal that the proposed method provides 100 percent correct identification of harmonic source location.
Explainable AI for harmonic fingerprinting and voltage sag diagnosis in decentralized power grids: trends, challenges and future directions Mohd Hatta Jopri; Tole Sutikno; Yacine Djeghader; Mohd Riduan Mohd Shariff; Wan Azlan Wan Zainal Abidin
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1484-1498

Abstract

The evolution of decentralized power grids has increased the complexity of power-quality monitoring, particularly harmonic fingerprinting and voltage sag diagnosis. Artificial intelligence improves disturbance detection and classification, yet black-box models limit transparency, engineering validation, and operator trust. This review synthesizes 108 selected studies on explainable artificial intelligence (XAI) for power-system diagnostics, focusing on SHapley Additive exPlanations (SHAP), local interpretable model-agnostic explanations (LIME), attention-based interpretability, visual analytics, and physics-informed learning. The review integrates harmonic fingerprinting with voltage sag diagnosis through their shared requirements for source attribution, temporal interpretation, physical consistency, and operator-oriented explanation. Four major deployment gaps are identified: data quality, computational latency, physical grounding, and trustworthiness. Future priorities include real-time embedded XAI, physics-informed neural networks, federated learning, standardized trustworthiness metrics, and adaptive model lifecycle management. The findings indicate that reliable autonomous diagnosis requires explainability to be integrated with predictive performance, electrical-system physics, computational efficiency, and field validation. This integration provides a stronger foundation for transparent, resilient, and trustworthy diagnostic systems in decentralized power grids.
Co-Authors Aleksandr Skamyin Aleksey Belsky Mohd Hatta Jopri Mustafa Manap Tole Sutikno Wan Azlan Wan Zainal Abidin Yacine Djeghader