Nagendra Singh
Department of Electrical Engineering, Trinity College of Engineering & Technology, Karimnagar, Telangana, India.

Andrei Dzeikalo
Research & Development in Engineering, 13475 Rincon Drive, apt 1418, Houston, TX, 77077, USA.

Bhagyanagar Rajagopal
Department of Applied Science, Trinity College of Engineering & Technology, Karimnagar, Telangana, India.

Harsh Pratap Singh
Department of Computer Science and Engineering, Medicaps University, Indore, Madhya Pradesh, India.

Sandeep Kumar Mathariya
Department of Computer Science and Engineering, Medicaps University, Indore, Madhya Pradesh, India.

DOI https://doi.org/10.33889/IJMEMS.2026.11.4.067

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Abstract

Demand for solar-based microgrid systems is increasing in remote and rural areas due to their simple installation and operation flexibility. Power quality issues arise in the solar supply system because of the switching of inverters, nonlinear loads, and fluctuation in solar irradiance. This study proposes an integrated artificial intelligence and Internet of Things-based smart power quality management system for a solar microgrid. A MATLAB simulation model of a 20 kW solar microgrid is developed, incorporating a DC–DC boost converter and a maximum power point tracking system for improving the power conversion efficiency. An IoT monitoring system continuously monitors real-time data of supply voltage, current, power factor, and total harmonic distortion. These measures are processed using an artificial neural network- long short-term memory-based intelligent controller to detect power quality disturbances by analyzing the sensor data and making quick control decisions. The proposed MATLAB Simulink microgrid model is validated through hardware-in-the-loop testing on the Typhoon hardware-in-the-loop platform. The experimental results of hardware-in-the-loop demonstrate a 70% total harmonic distortion reduction (to 3%), power factor (0.99), and settling time less than 50 ms that is under irradiance/load dynamics, outperforming baselines and meeting IEEE 519/1547 standards. Hardware-in-the-loop results show less than 5% deviation, demonstrating suitability for deployment.

Keywords- Solar microgrid, Power quality management, Artificial intelligence and Internet of Things, Artificial neural network- long short-term memory, Hardware-in-the-loop validation, Total harmonic distortion.

Citation

Singh, N., Dzeikalo, A., Rajagopal, B., Singh, H. P. & Mathariya, S. K (2026). Power Quality Management for Solar-Based Microgrids Using Integrated Artificial Intelligence and Internet of Things Technologies. International Journal of Mathematical, Engineering and Management Sciences, 11(4), 1639-1662. https://doi.org/10.33889/IJMEMS.2026.11.4.067.