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Research Papers

Prediction of Solar Photovoltaic/Thermal Collector Power Output Using Fuzzy Logic

[+] Author and Article Information
M. Sridharan

Mechanical,
Saranathan College of Engineering,
Tiruchirappalli 620012, Tamil Nadu, India
e-mail: sridhudhiya@gmail.com

G. Jayaprakash

Professor
Mechanical,
Saranathan College of Engineering,
Tiruchirappalli 620012, Tamil Nadu, India
e-mail: jayaprakashcad@gmail.com

M. Chandrasekar

Mechanical,
Anna University of Technology,
Tiruchirappalli 620024, Tamil Nadu, India
e-mail: shekarpunchu@yahoo.com

P. Vigneshwar

Mechanical,
Saranathan College of Engineering,
Tiruchirappalli 620012, Tamil Nadu, India
e-mail: Vigneshwar-mech@saranathan.ac.in

S. Paramaguru

Mechanical,
Saranathan College of Engineering,
Tiruchirappalli 620012, Tamil Nadu, India
e-mail: paramaguru-mech@saranathan.ac.in

K. Amarnath

Mechanical,
Saranathan College of Engineering,
Tiruchirappalli 620012, Tamil Nadu, India
e-mail: amar-mech@saranathan.ac.in

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received November 29, 2017; final manuscript received June 25, 2018; published online July 24, 2018. Assoc. Editor: Gerardo Diaz.

J. Sol. Energy Eng 140(6), 061013 (Jul 24, 2018) (6 pages) Paper No: SOL-17-1473; doi: 10.1115/1.4040757 History: Received November 29, 2017; Revised June 25, 2018

In recent years, solar PV/T water collectors have been identified as one of the most promising hybrid devices. It is a combination of solar photovoltaic (PV) and solar flat plate collector (FPC) systems capable of generating electrical and thermal power simultaneously. This study presents a model which predicts solar PV/T collector power output using fuzzy logic techniques. A fuzzy logic model was established to predict power output of PV/T with respect to changes in input process and FPC output power. Membership functions were allocated in connection with each model input. Experimental tests conducted during the month of December 2016 are compared with the developed fuzzy model to verify predicted results. The results indicate an agreement between fuzzy model and experimental results with an accuracy of 94.38% and error of 5.62%.

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References

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Figures

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Fig. 1

Architecture of fuzzy logic based PV/T predictive model

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Fig. 2

Standalone partially covered solar PV/T collectors

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Fig. 3

(a) Input variable-1 PV output power, (b) input variable-2 FPC output power, and (c) output variable-PV/T output power

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Fig. 4

PV/T power output in relations to change in PV and FPC power output

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Fig. 5

Variation in thermal and electrical efficiency with respect to time

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Fig. 6

Comparison of fuzzy logic model prediction with the measured PV/T power output

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