Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)

Authors

DOI:

https://doi.org/10.4108/ew.v9i6.147

Keywords:

Neural Networks (NN), Maximum power point tracking (MPPT), Small Wind Turbine (SWT), Solar Photovoltaic (SPV), P&O (Perturb and Observe), (Fuzzy logic controller)

Abstract

INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads.

OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out.

METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool.

RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of   0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm.

CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.

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Published

22-06-2023

How to Cite

1.
R. J, Rajesh K. Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN) . EAI Endorsed Trans Energy Web [Internet]. 2023 Jun. 22 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/ew/article/view/147