Most of the partial shading maximum power point tracking methods have been designed for the static shading pattern of the partial shading conditions, however, the irradiance pattern may change further when in partial shading mode. Therefore, to cover this research gap, a global maximum power point control under varying irradiance (GCVI) algorithm is proposed in this paper. The algorithm does not use any sensors to detect the change in the irradiance, instead, the change in the current values of the modules are continuously monitored to detect the change. The reference voltages across which the peaks on the power curve are scanned are obtained from the reference voltage generation process, the consideration of these reference points avoids the excessive power losses in the system. The verification of the working of the proposed algorithm is carried out by simulating the photovoltaic system model on SIMULINK in MATLAB software. Simulations are carried out in various scenarios to show the effectiveness of the control. The simulation results illustrate that with the change in the global maximum under partial shading, the system successfully retunes to the new maximum point; the maximum point retunes from 10 kW to 9.2 kW and from 13.8 kW to 11.5 kW for two different case scenarios. Further, the comparisons are also carried out with the previously reported methods.

An accurate forecast of wheat yield prior to harvest is of great importance to ensure the sustainability of food production in Iran. The primary objective of this study is to determine the best remote sensing features and regression model for wheat yield prediction in Hamedan, Iran. In addition, the effects of various time windows on different regression models are verified. For this purpose, several vegetation indices (VIs) and reflectance values obtained from Sentinel-2, as input to regression models, are used in different time windows. As a result, Gaussian process regression (GPR) and random forest (RF) represented the top two best methods, and the best results were achieved for the GPR model with the SAVI, NDVI, EVI2, WDRVI, SR, GNDVI and GCVI indices corresponding to the image captured at the end of May. The best model yielded a root mean square error (RMSE) of 0.228 t/ha and coefficient of determination R^2 = 0.73. Moreover, different regression methods regarding the number of training data are compared. The neural network and linear regression were the most and stepwise regression was the model affected the least by the number of training samples. Experimental results provide a technical reference for estimating large scale wheat yield.