Abstract
PV fouling detecting system based on neural network and fuzzy logic is proposed. Comparing with traditional methods, the proposed method is rapid adaptive and universal to all PV power station. Neural network is used to predict the maximum power point (MPP) of a PV module under any lighting conditions. Then fuzzy logic rule is used to identify the fouling condition according to the result from neural network prediction. The experiment shows that the neural network can precisely predict the MPP under any lighting environment and the fuzzy logic rules can precisely identify the fouling condition of PV modules.
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Chen, X., Wu, C., Li, H., Feng, X., Li, Z. (2014). PV Fouling Detecting System Based on Neural Network and Fuzzy Logic. In: Li, K., Xue, Y., Cui, S., Niu, Q. (eds) Intelligent Computing in Smart Grid and Electrical Vehicles. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45286-8_39
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DOI: https://doi.org/10.1007/978-3-662-45286-8_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45285-1
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