Photovoltaic (PV) system has been extensively used over the last few years because it is a noise-free, clean, and environmentally friendly source of energy. Maximum Power Point (MPP) from the PV energy systems is a challenging task under modules mismatching and partial shading. Up till now, various MPP tracking algorithms have been used for solar PV energy systems. Classical algorithms are simple, fast, and useful in quick tracing the MPP, but restricted to uniform weather conditions. Moreover, these algorithms do not search the Global Maxima (GM) and get stuck on Local Maxima (LM). However, bio-inspired algorithms help find the GM but their main drawback is that they take more time to track the GM. This paper addresses the issue by using the combination of conventional Incremental Conductance (InC) with variable step size and bio-inspired Dragonfly Optimization (DFO) algorithms leading to a hybrid (InC-DFO) technique under multiple weather conditions, for instance, Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS). To check the robustness of the proposed algorithm, a comparative analysis is done with six already implemented techniques. The results indicate that the proposed technique is simple, efficient with a quicker power tracking capability. Furthermore, it reduces undesired oscillation around the MPP especially, under PS and CPS conditions. The proposed algorithm has the highest efficiencies of 99.93%, 99.88%, 99.92%, and 99.98% for UI, PS1, PS2, and CPS accordingly among all techniques. It has also reduced the settling time of 0.75 s even in the case of the CPS condition. The performance of the suggested method is also verified using real-time data from the Beijing database.

The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.

Photovoltaic (PV) solar energy is a very promising renewable energy technology, as solar PV systems are less efficient because of climate conditions, temperature, and irradiance change. So, to resolve this problem, two PV topologies are used, i.e., centralized and distributed PV systems. The centralized technique is quicker than the distributed technique in terms of convergence speed and a faster power tracking approach. In the event of uniform irradiance, the centralized system also has the benefit of supplying superior energy, but in PS scenarios, a huge amount of energy is lost. However, the distributed approach requires current and voltage measurements at each panel, resulting in a massive data set. Nevertheless, in the event of shading circumstances, the distributed technique is highly effective because a modular level power electronics (MLPE) converter is used. While in a centralized PV system, there is only a single DC-DC converter for the whole PV system. In this research work, a DFO-based DC-DC converter is designed for modular level, with an ability to perform a rapid shutdown of the module under fire hazard conditions, troubleshooting, and monitoring of a module in a very efficient way. The robustness of the proposed MPPT DFO algorithm is tested with different techniques such as Cuckoo Search (CS), Fruit Fly Optimization (FFO), Particle swarm optimization (PSO), Incremental conductance (InC), and Perturb and observe(P&O) techniques. The proposed technique shows better results in terms of MPPT efficiency, dynamic responsiveness, and harmonics. Furthermore, the result of MLPE and the centralized system is verified by using the Helioscope with different inverter companies like SMA, Tigo, Enphase, Solar edge, and Huawei. The results prove that MLPE is a better option in the case of shading region for attaining the maximum power point.