Temperature Estimation for Photovoltaic Array Using an Adaptive Neuro Fuzzy Inference System

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 inferen...

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Bibliographic Details
Published inSustainability Vol. 9; no. 8; p. 1399
Main Authors Bassam, A., May Tzuc, O., Escalante Soberanis, M., Ricalde, L., Cruz, B.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2017
Subjects
Adaptive systems
Atmospheric pressure
Cooling
Fuzzy logic
Fuzzy systems
Inference
Irradiance
Photovoltaic cells
Photovoltaics
Relative humidity
Sustainability
Temperature effects
Temperature requirements
Velocity
Wind direction
Wind speed
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su9081399