Enhanced FOPID Controller Based Cascaded Inverter for Grid-Connected Photovoltaic System

Abstract

An enhanced controller is proposed to investigate grid-connected photovoltaic systems employing cascaded two-level inverters. The primary focus is on optimizing power output, achieved through the development, modeling, and testing of photovoltaic systems using the suggested upgraded controller. This advanced controller, specifically a Fractional Order PID (FOPID) controller, incorporates the Ant-Lion Optimizer (ALO) algorithm for enhanced performance compared to conventional controllers. The FOPID controller’s reliability is emphasized, and further improvements are pursued by optimizing gain parameters through the prescribed method. Power supply under both schemes is examined, and the controller’s performance in extracting maximum power under specified operating conditions is deemed satisfactory. To validate the proposed approach, practical implementation is conducted using the MATLAB/Simulink platform. The effectiveness of proposed and conventional methodologies for analyzing the i_d and i_q currents is assessed. A comparison is drawn between the suggested approach and current methods such as the base controller, GSA, and ABC methodologies. In comparison to the existing methods, the suggested FOPID controller demonstrates superior performance in maintaining the DC link voltage, achieving an efficiency of 94%, while the GSA method reaches 91.5%, the ABC method achieves 91%, and the base controller achieves 90%.

Keywords:

• PV
• cascaded two level inverter
• FOPID
• ALO algorithm
• GSA
• PSO
• dc link voltage and current

ACKNOWLEDGMENTS

The authors are highly grateful to the Vice-chancellor of S.V. University, Tirupati for providing excellent infrastructure facilities and encouragement that have made this research work possible.

AUTHOR CONTRIBUTIONS

W.V. Jahnavi—Investigation; Writing—original draft. J.N. Chandra Sekhar—Conceptualization, Methodology, Formal analysis, Supervisor. All authors have read and agreed to the published version of the manuscript.

DISCLOSURE STATEMENT

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

Additional information

Notes on contributors

W.V. Jahnavi

W.V. Jahnavi is a research scholar in EEE department at SVU College of engineering, Tirupati. She completed her bachelor’s degree in the year of 2004 and master’s degree in the year of 2006. Her areas of research interests are renewable energy sources and artificial intelligence.

J.N. Chandra Sekhar

J.N. Chandra Sekhar obtained his Bachelor’s degree in Electrical and Electronics Engineering in 2004 from JNT University, Hyderabad. Then he obtained his Master’s degree in 2006 in Power Electronics and Industrial Drives from Satyabama University, Chennai. He had been awarded Doctoral degree in 2019 from Sri Venkateswara University, Tirupati. Currently working as Associate Professor in the department of Electrical and Electronics Engineering at Sri Venkateswara University College of Engineering, Tirupati, India. His research interests are Power Semiconductor Drives, Electric Vehicles, Machine learning and Deep learning Algorithms, Energy Storage Systems.