Performance Enhancement of Solar Photovoltaic-Maximum Power Point Tracking Using Hybrid Adaptive Neuro-Fuzzy Inference System–Honey Badger Algorithm

Abstract

The work evaluates the effectiveness of three maximum power point tracking (MPPT) techniques: pulse width modulation (PWM)-based, adaptive neuro-fuzzy inference system (ANFIS)-based, and a proposed hybrid ANFIS–honey badger algorithm (HBA) model that combines ANFIS with the HBA. Experiments and simulations were conducted to assess the performances of these techniques in terms of output current, output voltage, simulation output power, experimental output power, and efficiency. The experimental data are collected under a solar irradiance of 1000 W/m² and a 25 °C temperature. The outcomes demonstrate the efficacy of the hybrid model-based approach MPPT technique outperforms both the PWM-based and ANFIS-based techniques, achieving an output voltage of 100 V, output current of 5 A, simulation output power of 500 W, experimental output power of 413.21 W, and an efficiency of 98.74%. The hybridization of ANFIS with the HBA demonstrates superior performance by combining adaptive learning and evolutionary optimization techniques. These findings highlight the potential of the proposed ANFIS–HBA-based MPPT technique in enhancing power extraction efficiency and output performance in solar photovoltaic (PV) modules. The outcomes of this research provide valuable insights for developing and optimizing MPPT techniques in solar PV systems and aid in the increased use of energy from renewable sources.

Keywords:

• solar photovoltaic
• maximum power point tracking
• performance enhancement
• hybrid adaptive neuro-fuzzy inference system–honey badger algorithm
• boost converter
• renewable energy source

Acknowledgment

There is no acknowledgement involved in this work.

Ethical approval and patient consent statements

No participation of humans takes place in this implementation process.

Author contributions

All authors are contributed equally to this work.

Human and animal rights

No violation of human and animal rights is involved.

Disclosure statement

Conflict of Interest is not applicable in this work.

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Additional information

Funding

No funding is involved in this work.

Notes on contributors

R. R. Rubia Gandhi

R. R. Rubia Gandhi is working as an Assistant Professor in the department of Electrical and Electronics Engineering at Sri Ramakrishna Engineering College, Coimbatore. She has two years of industrial experience. She graduated her B.E., Electrical & Electronics Engineering with first class with distinction from Karpagam College of Engineering, Coimbatore, India in 2011. She graduated her M.E., Power Electronics and Drives with first class with distinction from Sri Krishna College of Engineering & Technology, Coimbatore, India in 2015. She is currently pursuing her research in Electrical Engineering as a part time research scholar in Anna University, Chennai. She has attended many national and international conferences and has published many papers in international and Scopus indexed journals. She is BEC Certified professional and lifetime member of ISTE. Her field of interest is Power Electronics and Renewable Energy.

C. Kathirvel

C. Kathirvel, is currently working as Professor in Sri Sai Ranganathan Engineering College, Coimbatore. He completed his Ph.D in Electrical Engineering from Anna University, Chennai in the year 2014. He has completed his M.E., Applied Electronics degree from Coimbatore Institute of technology, in the year 2004 and has received his B.E., (Electrical and Electronics Engineering) degree from Bharathiyar University in the year 2000. He has around 25 years of teaching and industrial experience. He is a life member in ISTE and a certified ISO internal auditor. His area of interests includes Renewable Energy systems, Soft computing and Controllers for Hybrid energy systems.