An enhanced arithmetic optimization algorithm for global maximum power point tracking of photovoltaic systems under dynamic irradiance patterns

ABSTRACT

The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation.

KEYWORDS:

• Arithmetic optimization algorithm
• Levy flight
• GMPPT
• photovoltaic power system
• dynamic irradiance
• real-time digital simulator

Disclosure statement

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

Additional information

Notes on contributors

Rajasekar Thota

Rajasekar Thota received the B.Tech. degree in Electrical & Electronics Engineering from Sri Vidyanikethan Engineering College Tirupati, in 2010, and the M.Tech. degree in Electrical Power Systems from the Quba College of Engineering and Technology Nellore, in 2015. Currently, he is pursuing a Ph.D. degree in the Department of Electrical Engineering at the National Institute of Technology Silchar, Assam, India. His research interests include renewable energy sources in the building sector, the Application of Soft computing techniques in the green energy building sector, Load forecasting, Energy Efficiency, and Energy management.

Nidul Sinha

Nidul Sinha was born in Tripura, India, in 1962. He received his B.E. degree in electrical engineering from Calcutta University, Kolkata, India, in 1984, the M.Tech. degree in power apparatus and systems from IIT Delhi, New Delhi, in 1989, and the Ph.D. degree in electrical engineering from Jadavpur University, Kolkata. His Ph.D. thesis was on the application of intelligent techniques in the optimal operation of a power system. Since then, he has been engaged in active research in different areas like automatic generation control, optimal operation of the power system under conventional and non-conventional environments, control of renewable energy sources and micro-grid, image denoising, and video motion estimation, EEG-based emotion detection, and silent speech reading. He has more than 90 national and international publications in diverse fields. He has completed four sponsored Research and Development Projects. He has also been a reviewer of several international journals, such as IEEE, IET, Elsevier, Taylor and Francis, and Springer.