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All Journal International Journal of Applied Power Engineering (IJAPE) Indonesian Journal of Electrical Engineering and Computer Science
Durairaj Chandrakala
Easwari Engineering College
Electrical & Electronics Engineering

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Performance improvement in photovoltaic-grid system using genetic algorithm Rangasamy Sankar; Durairaj Chandrakala; Rengaraj Hema; Dakshnamurthy Padmapriya
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 3: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i3.pp1327-1336

Abstract

In recent, photov oltaic (PV) power generation has increased in importance. The growing significance of PV power production has generated the demand for enhancing energy efficiency via continuous operation at the maximum power point (MPP). To enable effective MPP trac king, the suggested system integrates a proportional - integral (PI) controller with the p erturb and observe (P&O) technique. In order to improve performance in a PV grid system, this work provides a unique method using a proportional - integral - derivative (PI D) controller optimized using a genetic algorithm (GA). The proposed controller architecture integrates the GA algorithm with a PID controller in the voltage source inverter (VSI) of the PV system. To enable effective grid integration, the GA is used to co ntinually optimize the PID controller settings. The converter’ s design criteria and computations are discussed, and MATLAB simulations are used to assess the system’ s performance. Compared to traditional PID controllers, the observed findings show increas ed efficiency, cheaper cost, and enhanced controllability. The suggested GA - PID controller offers opportunities for more study and development in this area while showing potential for improving PV grid system performance.
A deep learning approach for electric vehicle battery charging duration estimation using IoT Tumuluri Kanthimathi; Adhimoolam Sairam; Durairaj Chandrakala; Moorthy Radhika; Bichagal Shadaksharappa; Pitchai John Britto; Minakshi Sanadhya; Balasubramanian Suganya; Chelliah Srinivasan
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.pp1375-1385

Abstract

The rapid development of electric vehicles (EVs) has increased the desire for precise charging duration estimate to enhance charging station administration and elevate customer experience. This research presents a deep learning (DL) architecture that uses internet of things (IoT)-enabled data to categorise charging duration into short, medium, and long classifications. The assessment dataset comprises many numerical and categorical variables affecting battery performance and charging behaviour, providing a thorough foundation for prediction. The system utilises a deep neural network (DNN) architecture with nonlinear transformations and regularisation techniques, trained with adaptive optimisation to provide durable convergence. Extensive experiments indicate that the proposed model achieves an overall accuracy of 99.26%, markedly improving traditional machine learning (ML) techniques. These results highlight the potential of DL to adeptly discern complex linkages in charging dynamics, providing dependable predictions for duration classification. The framework provides an advanced basis for implementation in smart charging infrastructures, facilitating effective scheduling, minimizing waiting times, and endorsing predictive maintenance measures. It enhances the reliability and efficiency of EV charging ecosystems with data-driven, IoT-enabled DL technologies.
Co-Authors Adhimoolam Sairam Balasubramanian Suganya Bichagal Shadaksharappa Chelliah Srinivasan Dakshnamurthy Padmapriya Minakshi Sanadhya Moorthy Radhika Pitchai John Britto Rangasamy Sankar Rengaraj Hema Tumuluri Kanthimathi