Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading

•This paper proposes a few novel MPPT techniques, which include Adaptive Cuckoo Search Optimization Algorithm (ACOA), General Regression Neural Network GRNN) with Fruit fly Optimization algorithm (FFOA), and Dragonfly Optimization Algorithm (DFO) to track the MPP under various weather condition.•The proposed techniques enhance the performance of the PV system, save the computational time and greatly reduce the oscillation around the global maximum power point.•For the validation of the proposed techniques, comparative analysis of their results with the Bio-inspired Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Artificial Bee Colony Algorithm (ABC) and PSO Gravitational search Optimization (PSOGS) is presented.•The comparison shows that proposed techniques are better in term of quick power tracking, stability, and high efficiency under various weather conditions, and also demonstrates that the proposed techniques can efficiently locate the GM (global maxima) under the PS and Dynamic Partial Shading (DPS) conditions. In PV systems, the non-uniform irradiance and diversified unpredictable weather conditions fall into the category of Partial Shading (PS). Under PS, it is challenging for PV systems to obtain the maximum output through Maximum Power Point Tracking (MPPT), i.e., the parameters of the controller are adjusted online to yield the maximum power. In the literature, various techniques have been proposed to track the MPP (Maximum Power Point) under the uniform irradiance. On the contrary, few techniques have been proposed to efficiently track MPP under PS. In this paper, a few novel MPPT techniques have been proposed, which include Adaptive Cuckoo Search Optimization Algorithm (ACOA), General Regression Neural Network GRNN) with Fruit fly Optimization algorithm (FFOA), and Dragonfly Optimization Algorithm (DFO) to track the MPP under various weather condition. The proposed techniques enhance the performance of the PV system, save the computational time and greatly reduce the oscillation around the global maximum power point. For the validation of the proposed techniques, comparative analysis of their results with the Bio-inspired Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Artificial Bee Colony Algorithm (ABC) and PSO Gravitational search Optimization (PSOGS) is presented. The comparison shows that the proposed techniques are better in term of quick power tracking, stability, and high efficiency under variou