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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/9470
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dc.contributor.authorKumar, Rajneesh-
dc.date.accessioned2023-03-03T09:22:19Z-
dc.date.available2023-03-03T09:22:19Z-
dc.date.issued2016-08-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0038092X16300263-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9470-
dc.description.abstractThe performance of a neural network model depends on the data used to develop the model and nature of data preprocessing used in its training algorithm. This paper proposes a hybrid clustering algorithm to preprocess the data that is used for neural network training as well as data division technique to predict the power output of a soiled solar panel. Deterministic characteristics of soil on the panel namely particle size composition, X-ray diffraction (XRD) analysis and Fourier transform infrared spectroscopy (FTIR) analysis are used to model the soil. Artificial soiling experiment is conducted with each soil sample on the panel at irradiance level in the range of 200–1200 W/m2 for a set of 18 tilt angles to collect data of short circuit current (Isc) and open circuit voltage (Voc) leading to power output. NNR (Neural Network random) with random data division, NNF (Neural network fuzzy) with Fuzzy C Means clustering before training, NNK (Neural network k-means) with K-means data clustering and NNH (Neural network hybrid) with hybrid data clustering and division techniques are developed using these data. The performance of these networks for a known and unknown soil samples is compared. For a known soil sample, NNH performed better (Maximum percentage error of 2%) as compared to NNR (−6.3%), NNF (−6.7%) and NNK (13%). In case of unknown soil sample NNH outperforms other models with a maximum error margin as low as (−10%) as compared to NNR (50%), NNF (−81%) and NNK (124%).en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEEEen_US
dc.subjectClusteringen_US
dc.subjectData divisionen_US
dc.subjectNeural networksen_US
dc.subjectPower predictionen_US
dc.titlePower prediction of soiled PV module with neural networks using hybrid data clustering and division techniquesen_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

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