ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions-Reference-Cited by-同舟云学术

ANN-based Maximum Power Point Tracking Technique for PV Power Management under Variable Conditions

Published:2024-10-01 Issue:5 Volume:9 Page:1106-1123
ISSN:2455-7749
Container-title:International Journal of Mathematical, Engineering and Management Sciences
language:en
Short-container-title:Int. j. math. eng. manag. sci.

Author:

Khan Mohammad Junaid¹,Akhtar Md Naqui²,Hasan Mashhood³,Malik Hasmat⁴,Ansari Md Fahim⁵,Afthanorhan Asyraf⁶

Affiliation:

1. Artificial Intelligence for Islamic Civilization and Sustainability, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus 21300, Terengganu, Malaysia. & Operation Research & Management Sciences, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus 21300, Terengganu, Malaysia. & Department of Electrical and Electronics Engineering, Mewat Engineering College (Waqf), Nuh, Haryana, India.

2. Department of Electrical Engineering, Government Polytechnic Sahibganj, Jharkhand, India.

3. Department of Electrical Engineering Technology, College of Applied Industrial Technology (CAIT), Jazan University, 45142, Jazan, Kingdom of Saudi Arabia.

4. Department of Electrical Power Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia. & Department of Electrical Engineering, Graphic Era (Deemed to be University), 248002, Dehradun, India. & Intelligent Prognostic Private Limited, India.

5. Department of Electrical Engineering, Graphic Era (Deemed to be University), 248002, Dehradun, India.

6. Artificial Intelligence for Islamic Civilization and Sustainability, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus 21300, Terengganu, Malaysia. & Operation Research & Management Sciences, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus 21300, Terengganu, Malaysia.

Abstract

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology.

Publisher

Ram Arti Publishers

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