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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.

Solar energy is a sustainable and inexhaustible renewable energy. Solar-powered PV systems contribute to the cleanest and the most cost-effective electrical energy. However, PV systems are confronted with a nonlinear maximum power point tracking (MPPT) problem caused by partial shading (PS). PS causes the loss of available power. This paper presents a novel Harris hawk optimization (HHO) based MPPT controller to effectively track maximum power under all weather conditions. The effectiveness of the proposed HHO based MPPT is supplemented by a comparative study with perturb and observe algorithm (P&O), dragonfly optimization algorithm (DFOA), particle swarm optimization (PSO), cuckoo search (CS) and grey wolf optimization (GWO). The analytical and statistical analysis is made based on 4 different cases, including fast varying irradiance, PS, complex-PS (CPS), and field atmospheric data of Hefei city of China, to solidify the effectiveness of HHO based MPPT for PV systems in real-world applications. The proposed HHO shows superior performance in tracking maximum power point and faster convergence at the global maximum power point. The improvement of 10–30% in tracking time and more than 90% in random oscillations are effectively achieved by the proposed MPPT technique. Faster maximum power point (MPP) tracking, lower computational burden and higher efficiency are the key contributions of the HHO based MPPT technique.