Improved Incremental Conductance MPPT Technique Designed to Addressing Drift Problem in a Photovoltaic System

Abstract

Many researchers have concentrated on improving the efficiency of photovoltaic (PV) systems by optimizing control mechanisms aimed at extracting the maximum power from PV panels. The variable step size incremental conductance control (VSS-INC) technique has been the primary focus of the majority of these studies. However, this strategy faces challenges in the form of drift when confronted with swift changes in solar irradiation, temperature variations, and resistive load fluctuations. In addressing this issue, the present study proposes an innovative VSS-INC method. It is suggested to utilize a buck-boost converter as an impedance adaptor in achieving maximum power point tracking (MPPT). This involves controlling the duty cycle and aligning its input with that of the PV module. The efficiency of the suggested approach was evaluated through MATLAB software, and the outcomes were compared with traditional algorithms across various operational scenarios. Simulation results demonstrate the notably satisfactory and efficient performance of the proposed method when compared to conventional approaches.

Keywords:

• buck-boost converter
• MPPT
• PV
• vss-inc
• modified variable step size

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

Abdelkadir Belhadj Djilali

Abdelkadir Belhadj Djilali received his Engineer and M.S. degree in Electrical Engineering from the Hassiba Benbouali University of Chlef, Algeria in 2006 and 2012, respectively. He received a PhD in Automatic from Ecole Nationale Polytechnique (ENP), Algiers, Algeria 2019. He is currently an Associate Professor at the Department of Electrical Engineering at Hassiba Benbouali University of Chlef. His research activities include the study and application of MPPT control of photovoltaic system.

Adil Yahdou

Adil Yahdou received his Engineer and M.S. degrees in Electrical Engineering from the Hassiba Benbouali University of Chlef, Algeria, in 2006 and 2011, respectively. He received a PhD in Automatic from Ecole Nationale Polytechnique (ENP), Algiers, Algeria 2017. He is currently an Associate Professor at the Department of Electrical Engineering at Hassiba Benbouali University of Chlef. His research interest includes power electronics, artificial intelligence, application of robust and nonlinear control in electrical machines and wind turbine systems.

Habib Benbouhenni

Habib Benbouhenni was born in Chlef, Algeria. He received the Ph.D. degree in electrical engineering from the ENPO-MA, Oran, Algeria. He received a M.A. degree in Automatic and industrial informatics in 2017. He is currently professor with the University of Nisantasi, Turkey. He is editor of seven books and more than 200 papers in scientific fields related to electrical engineering. In recent years, Habib Benbouhenni has served as a committee member of several scientific conferences such as ECAI-2024 and ECA-2023. He is also a committee member of Symmetry and EPCS journals. His research activities include the application of robust control in wind turbine power systems.

Ilhami Colak

Ilhami Colak was born in 1962 in Turkey. He received his diploma in Electrical Engineering from Gazi University, Turkey in 1985. Then he did his MSc in Electrical Engineering in the field of Speed Control of Wound Rotor Induction Machines Using Semiconductor Devices at Gazi University in 1991. After that he did his MPhil at Birmingham University in England by preparing a thesis on High Frequency Resonant DC Link Inverters in 1991. Finally, he did his PhD at Aston University in England on Mixed Frequency Testing of Induction Machines Using Inverters in 1994. He became an assistant professor, an associate professor and a full professor in 1995, 1999, and 2005, respectively. He has published more than 108 journal papers, 239 conference papers, and 7 books in different subjects including electrical machines, drive systems, machine learning, reactive power compensation, inverter, converter, artificial neural networks, distance learning automation and alternating energy sources.