Application of Radial Basis Neural Network in MPPT Technique for Stand-Alone PV System Under Partial Shading Conditions

Abstract

This paper proposes a highly sophisticated controller to track the maximum power point (MPP) of Photovoltaic (PV) systems. This method is based on the Artificial Neural Network (ANN) algorithm, which uses Radial Basis Neural Network (RBNN) to estimate the optimum voltage for the considered PV system, which helps to extract Global MPP. The critical methodology lies in the RBNN block generation, which considers one-year real-time data of the Panaji, Goa (India) region for the training process to drive this extensive PV system with resistive load. Nearly 1500 samples of one-year real-time Irradiation (G) and Temperature (T) are given as input to RBNN. The proposed intelligent technique only consists of single-stage ANN, thereby reducing the processing time and memory allocations for generating the corresponding Vmpp value for each G and T. A comparative study has been done using conventional techniques like Perturb and Observe (P & O) and Incremental Conductance (InC) methodologies. It was found that RBNN MPPT is best to use with PV modules affected by partial shading; on average, the tracking accuracy ranging from 94.6% to 97.4%. The response time of the RBNN method to reach MPP is 0.007 s which is much faster than the P & O method (0.15 s) and InC method (0.268 s). Also, very few oscillations are observed with the RBNN method than the other two in the transient tracking period. Obtained results indicate that the proposed inherent learning-based controller, with its enhanced efficiency conversion and faster tracking speeds, ensures better reliability for the complete PV system.

Keywords:

• Artificial neural network (ANN)
• Incremental conductance (InC)
• MPPT techniques
• Partial shading conditions (PSCs)
• Perturb and observe (P & O)
• Photovoltaic (PV)
• Radial basis neural network (RBNN)

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Science and Engineering Research Board [EEQ/2021/000294].

Notes on contributors

Ratnakar Babu Bollipo

Ratnakar Babu Bollipo was born in Chagallu, Andhra Pradesh, India, in 1995. He graduated in electrical and electronics engineering from Sir C R Reddy College of Engineering, affiliated to Andhra University, Eluru, India, in 2016. He received his post-graduation degree in power electronics and power systems specialization from the National Institute of Technology Goa, India, in 2020. His areas of interest are grid integrated renewable energy sources, distribution generation, microgrids, and artificial intelligence based applications to the power system. He did his research on AI-based MPPT techniques for grid-connected PV systems. Email: [email protected]

Suresh Mikkili

Suresh Mikkili (M'16, SM'19) was born in Gudipudi, Bapatla, Andhra Pradesh, India, in 1985. He received the BTech degree in electrical and electronics engineering from SITE, TP Gudem, India, in 2006. He received the MTech and PhD degrees in electrical engineering from the National Institute of Technology, Rourkela, India, in 2008 and 2013, respectively. He is currently working as an associate professor in the EEE Department at the National Institute of Technology Goa (NIT Goa), India. He has been head of the EEE Department at NIT Goa from June 2014 to November 2015. Since september 2015, he has been the dean, Student Welfare at NIT Goa.

His research interests include power quality improvement issues, active filters, power electronics applications to power systems, applications of soft computing techniques and renewable energy sources. He has authored a book entitled Power Quality Issues: Current Harmonics, published in CRC Press, Taylor & Francis Group, August 2015, ISBN 9781498729628. He reported results of his research (90+ articles) in reputed international journals (SCI/SCI-E) and conferences (annual/bi-Annual/bi-ennial). ORCID ID https://orcid.org/0000-0002-5802-3390. Corresponding author. Email: [email protected]

Praveen Kumar Bonthagorla

Praveen Kumar Bonthagorla (M'19) was born in Cherukuru, Andhra Pradesh, India, in 1991. He received the BTech degree in electrical and electronics engineering from Bapatla Engineering College Guntur, India, in 2012 and the MTech degree in electrical and electronics engineering from the Annamacharya Institute of Technology and Sciences, Hyderabad, India, in 2015. He is currently working towards a PhD degree in electrical and electronics engineering at the National Institute of Technology Goa, India.

His research interests include applications of renewable energy sources, DC-DC converters, soft computing techniques. Email: [email protected]