Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Abstract

Due to the extremely poor efficiency of solar energy, researchers created various maximum power point tracking (MPPT) techniques with the aim of enhancing the effectiveness of photovoltaic (PV) systems. Because of their propensity to address complex problems and their non-linear characteristics, Artificial Neural Network (ANN) algorithms are the most frequently used among these MPPT techniques. Nevertheless, the performance of the ANN-based MPP tracking algorithms is contingent upon various factors, including the choice of activation function, the quantity of hidden neurons, and the training algorithm employed. Shannon’s Information Criteria (SIC) is used to determine the optimal number of hidden neurons within a single hidden layer for the neuro-controller application. In this regard, a two-layer Feed-Forward Neural Network (FFNN) was trained using MATLAB/Simulink software, incorporating varying numbers of hidden neurons. The results indicate that the two-layer FFNN with five hidden neurons has the highest performance, as demonstrated by the lowest Mean Squared Error (MSE) of 4.03 × 10^-9, which is statistically significant. The successful incorporation of Gaussian noise in the simulation of an 85 kW PV system demonstrates that the ANN-based MPPT algorithm is both theoretically robust and practically viable and reliable for enhancing the efficiency of solar PV systems in real-world scenarios.

Keywords:

• solar energy
• neuro-controller
• maximum power point tracking (MPPT)
• photovoltaic systems
• Shannon’s information criteria
• Gaussian noise

ACKNOWLEDGMENTS

The authors are thankful to the “The University of Djibouti” for their financial support to Oubah in her Ph.D. study at the Istanbul Technical University.

Author Contributions

Oubah Isman Okieh: Conceptualization, methodology, software, formal analysis, writing—original draft, visualization. Serhat Seker: Resources, writing—review and editing, supervision. Tahir Cetin Akinci: Resources, writing—review and editing. Abdoulkader Ibrahim Idriss: Resources, writing—review and editing.

DISCLOSURE STATEMENT

The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

Data available on request.

Additional information

Funding

This research received no external funding.

Notes on contributors

Oubah Isman Okieh

Oubah Isman Okieh is a PhD candidate in the Electrical Engineering Department at Istanbul Technical University. Her academic journey commenced at the University of Djibouti, where she earned both her bachelor’s and master’s degrees from the Electrical and Energy Engineering Department, Faculty of Engineering. Oubah’s research is primarily focused on the renewable energy sector, with a particular emphasis on solar energy. She is deeply committed to the advancement of Photovoltaic (PV) system efficiency, exploring innovative approaches to harness solar power more effectively.

Serhat Seker

Serhat Seker received the degree from the Electrical Engineering Department, Istanbul Technical University (ITU), and the master degree from Nuclear Engineering Department and Ph.D. degree from the Electrical Engineering Division, Science and Technology Institute, ITU. He studied the Ph.D. thesis with the Energy Research Centre of the Netherlands (ECN) and worked on signal analysis techniques. He was an Assistant Professor and an Associate Professor with ITU, in 1995 and 1996. He worked in industrial signal processing with the Maintenance and Reliability Centre, The University of Tennessee, Knoxville, TN, USA, in 1997. He was the Vice Dean with the Electrical and Electronic Engineering Faculty, from 2001 to 2004, and the Department Head of the Electrical Engineering, from 2004 to 2007. He was also the Dean of the Faculty of Electrical and Electronics, from 2014 to 2020.

Tahir Cetin Akinci

Tahir Cetin Akinci pursued his Bachelor’s degree in Electrical Engineering in 2000, followed by his Master’s and Ph.D. degrees in 2005 and 2010, respectively. From 2003 to 2010, he worked as a Research Assistant at Marmara University in Istanbul, Turkey. Dr. Akinci has been a professor in the Electrical Engineering Department at Istanbul Technical University (ITU) since 2020. Dr. Akinci assumed the role of a visiting scholar at the University of California Riverside (UCR). His research interests include artificial neural networks, deep learning, machine learning, cognitive systems, signal processing, and data analysis. In 2022, Dr. Akinci was honored with the International Young Scientist Excellence Award as well as the Best Researcher Award for his exceptional research achievements.

Abdoulkader Ibrahim Idriss is Assistant Professor of the Faculty of Engineering, Université de Djibouti, Djibouti. He holds a PhD degree in Photonic Engineering in France (Université de Franche-Comté, Besançon) with specialization in Optical nano-antennas for the inspection of photonic structures. His research areas are materials, photonic, nanomaterials for renewable energy. He is Dean of the Faculty of Engineering. He was also the Director of the Logistic and Transport Centre (Centre of Excellence), financed by the World Bank, from 2019 to December 2021. He is currently a professor with the department of Electrical engineering. He is also Guest Editor of the Special Issue Big Data in Renewable Energy for Renewable and Sustainable Energy Reviews published by Elsevier Ltd.