Prediction of output power has become important with the expansion of photovoltaic systems in recent years. Power prediction can be used for economic analysis, loss calculation and optimal designs. In this paper, a new approach for estimating the maximum output power of a photovoltaic power plant is proposed using the meteorological data under cloudy conditions. Basic concepts for understanding the behavior of modules in partial shading condition (PSC) are presented. To find the global maximum power point (GMPP), the shading index is introduced which can be used to find the maximum power point using the climate data. Then, analytical approach has been employed to find voltage and current of arrays at GMPP for both series and series-parallel configurations. The analytical approach is verified using MATLAB-SIMULINK software. Various experimental tests are made to confirm the aforementioned method validity.

Despite several decades of research in the field of photovoltaic (PV) systems, shading tolerance has still not been properly addressed. PV modules are influenced by shading concerning many factors, such as number and configuration of cells in the module, electrical and thermal characteristics of the cells, number and type of bypass circuits, electrical characteristics of bypass elements, and shading profile features. Along with the random nature of shading profile over the lifetime of a PV system, it is difficult to choose the best module for a location which is most of the time sunny, partly cloudy, or cloudy. This paper suggests a measurable parameter, the so-called shading tolerability (ST), to classify PV modules regarding the ability to oppose shading effects. Based on mathematical and probability analysis, the ST parameter is extracted and then measured using a large area steady state solar simulator. Finally, the results of on-field experiments are presented as a proof for the shading quantification method and its significant contribution to performance ratio improvement.