This research compares the performance of the models to estimate monthly mean diffuse solar radiation (DSR) on a horizontal surface for composite climatic region of India. The goal is to identify the most accurate model using deterministic and probabilistic analysis through two levels of uncertainty in daily sunshine period to examine the accuracy of the model that will be used to estimate DSR in the region under consideration. The meteorological data were collected from Indian Meteorological Department for the city of New Delhi (28 34 0 N, 77 12 0 E) which comes under composite climatic region prescribed by the Energy Conservation Building Code for India.From the literature, 150 typical models were chosen and categorized into three categories correlating diffuse fraction to sky-clearness index and relative sunshine period.The models are statistically evaluated using well-known statistical indicators in a unique way. In addition, the global performance indicator (GPI) is calculated using scaled values of indicators. The GPI of the chosen models ranged from À2.2912 to 0.2584, with the greatest value indicating the best model. Following that, the models are then ranked in decreasing order of GPI. Finally, the performances of the models are also checked for different locations having similar climatic conditions. Thus, results of this work are useful for impoverished countries as well as remote areas having similar environment conditions.
The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.
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