Bio-inspired optimization algorithms based maximum power point tracking technique for photovoltaic systems under partial shading and complex partial shading conditions

Abstract PV systems generate electrical energy from solar energy and are categorized as the cleanest and cost-effective form of electrical energy. Due to changes in temperature and irradiance conditions, PV systems fall into the category of partial shading (PS) which renders its output to be non-linear. Power losses occur due to PS conditions. Multiple bio-inspired MPPT techniques are pre-selected such as particle swarm optimization (PSO), grey wolf optimizer (GWO), cuckoo search (CS), and grasshopper optimization (GHO). Oscillations around the Global Maxima (GM), getting trapped in local maxima and in-efficient tracking of the GM, are the main drawbacks that are observed in the above mentioned techniques. This paper utilizes two swarm intelligence (SI) based novel MPPT techniques namely marine predator algorithm (MPA) and mayfly optimization algorithm (MFA). These techniques overcome the above-mentioned drawbacks. Three cases have been introduced in this paper namely fast varying irradiance, partial shading conditions, and complex partial shading condition. Improvements in the tacking time of up to 45% and efficiency greater than 99.9% has been observed in the proposed technique. It is observed that oscillations have been reduced to as low as 1 W along with extreme reduction in power loss as well. Rapid MPPT, low computational power, and high efficiency are the main features of the proposed techniques.