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PV-Grid Performance Improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking

Abhishek Kumar Sahu*, Durga Sharma**, Anup Mishra***

* Department of Electrical and Electronics Engineering, Bhilai Institute of Technology, Raipur, Chhattisgarh, India.

** Department of Electrical and Electronics Engineering, Dr C. V. Raman University, Bilaspur, Chhattisgarh, India.

*** Bhilai Institute of Technology Durg, Chattisgarh, India.

Periodicity:July - September'2024

Abstract

This paper presents an optimized model that combines the Intelligent Water Drop (IWD) optimization algorithm and a neural network (NN) for maximum power point tracking (MPPT) in photovoltaic (PV) applications. The proposed approach demonstrates superior performance compared to conventional methods, including Fuzzy Logic Control, Perturb and Observe (P&O), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Incremental Conductance (INC) control. The enhanced model improves adaptability and convergence due to the optimization capabilities of the IWD algorithm and leverages the predictive characteristics of the NN for faster and more accurate tracking. The results indicate that this model offers significant potential for future-generation PV systems, particularly in solar energy applications.

Keywords

Photovoltaic System, MPPT, IWD Optimization, NN, Simulink Simulation, Renewable Energy, Solar Power, Optimization Algorithm.

How to Cite this Article?

Sahu, A. K., Sharma, D., and Mishra, A. (2024). PV-Grid Performance Improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking. i-manager’s Journal on Electrical Engineering, 18(1), 1-11.

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