Abstract
Module temperature is an important parameter of photovoltaic energy systems since their performance is affected by its variation. Several cooling controllers require a precise estimation of module temperature to reduce excessive heating and power losses. In this work, an adaptive neuro fuzzy inference system technique is developed for temperature estimation of photovoltaic systems. For the learning process, experimental measurements comprising six environmental variables (temperature, irradiance, wind velocity, wind direction, relative humidity, and atmospheric pressure) and one operational variable (photovoltaic power output) were used as training parameters. The proposed predictive model comprises a zero-order Sugeno neuro fuzzy system with two generalized bell-shaped membership functions per input and 128 fuzzy rules. The model is validated with experimental information from an instrumented photovoltaic system with a fitness correlation parameter of R = 95%. The obtained results indicate that the proposed methodology provides a reliable tool for estimation of modules temperature based on environmental variables. The developed algorithm can be implemented as part of a cooling control system of photovoltaic modules to reduce the efficiency losses.
Highlights
Solar energy has become one of the most attractive renewable energy sources since it is abundant, environmentally friendly, and safe
A high module temperature produces a reduction of PV efficiency due to the consequent low values of open circuit voltage, fill factor, and power output [8]
Among environmental variables that affect the performance of PV module are: temperature (Ta), solar radiation (G), wind velocity (Wv), wind direction (Wd), relative humidity (RH), and atmospheric pressure (Pat)
Summary
Solar energy has become one of the most attractive renewable energy sources since it is abundant, environmentally friendly, and safe These characteristics have led many countries to pay attention on the design policies, strategies, and technology for its use [1,2,3,4]. In this context, photovoltaic (PV) systems are one of the most mature solar technologies, and it is constantly increasing its competitiveness and decreasing its production costs [5]. A high module temperature produces a reduction of PV efficiency due to the consequent low values of open circuit voltage, fill factor, and power output [8]
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