Online adaptive master maximum power point tracking algorithm and sensorless weather estimation

This paper presents analysis aspects for photovoltaic (PV) cells. Starting from reducing the number of needed I-V points to extract the parameters of the double diode PV model based on two different stochastic algorithms. Then, the identified model is analyzed to find informative and detailed sensitivities of the main outputs of the PV cell to cell temperature and solar irradiance. These formulations can work as a master algorithm to check, update and accelerate the maximum power point tracking (MPPT) algorithms. Besides that, model uncertainties and variations in the parameters are considered to represent a new accurate MPPT algorithm in one straightforward step. An improved online adaptation to the fractional open circuit voltage and fractional short circuit current MPPT algorithms are presented as examples of the proposed one-step online MPPT algorithm. As a result, different sensorless estimators of the temperature and the irradiance are presented to replace the actual weather sensors with high accuracy. The proposed work is done for four PV cells to show the importance of this comprehensive analysis. Experimental work is carried out using a 100W (PS-M36s) solar panel and Altera DE2-115 field programmable gate array development board in the-loop verification environment to illustrate the effectiveness of the proposed procedure.