Global Maximum Power Point Tracking of Photovoltaic Systems Using Bio-inspired Algorithm-Based MPPT and One-Cycle Control

• Published in: Arabian journal for science and engineering (2011) Vol. 49; no. 5; pp. 6799 - 6814
• Main Authors: Nisha, R., Sheela, K. Gnana
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Algorithms; Engineering; Humanities and Social Sciences; Maximum power tracking; multidisciplinary; Particle swarm optimization; Performance indices; Renewable energy sources; Research Article-Electrical Engineering; Science; Global maximum power point; Partial shading; Particle swarm optimization; One-cycle control; Bio-inspired algorithm
• ISSN: 2193-567X; 1319-8025; 2191-4281
• DOI: 10.1007/s13369-023-08493-2

The clean and abundant nature of photovoltaic technology makes it eminent among other renewable energy sources and to obtain the best benefit from these sources, an efficient maximum power point tracking technique is needed that can produce the required output even under varying environmental conditions. This work deals with the development of a global maximum power point tracking technique combining bio-inspired algorithms and one-cycle control which helps in effective tracking even under partial shading conditions. This technique generates signals for the KY converter from the duty cycle obtained from bio-inspired algorithms. The voltage at the output of the photovoltaic panel is fed to the load through KY converter. The analysis of the system is carried out using resistive load under different patterns of the photovoltaic array using particle swarm optimization, flower pollination and flying squirrel search optimization algorithms through simulation and experimentation. The performance indices like tracking speed, tracking efficiency and steady-state oscillations are taken for comparison with the existing systems without one-cycle control, and the results indicate its capability in GMPP tracking with an average efficiency and tracking time of 99.1% and 0.13 s, respectively.