Nowadays a hot topic among the research community is the harnessing energy from the free sunlight which is abundant and pollution-free. The availability of cheap solar photovoltaic (PV) modules has to harvest solar energy with better efficiency. The nature of solar modules is nonlinear and therefore the proper impedance matching is essential. The proper impedance matching ensures the extraction of the maximum power from solar PV module. Maximum power point tracking (MPPT) algorithm is acting as a significant part in solar power generating system because it varies in the output power from a PV generating set for various climatic conditions. This paper suggested a new improved work for MPPT of PV energy system by using the optimized novel improved fractional order variable step size (FOVSS) incremental conductance (Inc-Cond) algorithm. The new proposed controller combines the merits of both improved fractional order (FO) and variable step size (VSS) Inc-Cond which is well suitable for design control and execution. The suggested controller results in attaining the desired transient reaction under changing operating points. MATLAB simulation effort shows MPPT controller and a DC to DC Luo converter feeding a battery load is achieved. The laboratory experimental results demonstrate that the new proposed MPPT controller in the photovoltaic generating system is valid.
This paper presents the optimized Hopfield Neural Network (HFNN) based Fuzzy Logic Control (FLC) Maximum Power Tracking structure for a renewable Photovoltaic (PV) system under changing climatic conditions. Changing climatic condition of photovoltaic panel yield multiple local and global maximum power points, which creates tracing of the extreme power which is a problematic task. Most of existing traditional techniques fail to operate accurately under this changing weather condition. This paper advances the technique by considering wide search and changing climate so that the designed HFNN trace the maximum power for the entire situation. It is verified for dissimilar weather condition through simulation and proved experimentally. In this paper, the major merit of using optimized new FLC is also presented. HFNN neurons are optimized using new FLC. Comparative tests have been conducted for conventional Perturb and Observe and Incremental Conductance methods. From the outcomes of the simulation results, it is measured that the HFNN technique decreases error and it contributes quick reaction to climatic variations. Moreover, it does not need any external fine-tuning of the structure, unlike existing traditional FLC technique, wherein the regulator gain components want to be altered when solar illumination varies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.