Abstract
Quantitative information of maximum power point (MPP) is crucial for controlling and optimizing the output power of photovoltaic (PV) modules. However, it is difficult to obtain the voltage at MPP through direct measurements. A novel approach of radial basis function neural network (RBFNN) is proposed to achieve maximum power point estimation in this study. The proposed method has the capability of determining the MPP of PV arrays directly from the measured current–voltage data of PV modules, and takes advantages of no need of internal parameters of PV model. The experimental results show that the proposed approach can obtain the optimal power output in high accuracy.
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Acknowedgments
The authors would like to thank the anonymous reviewers for their constructive comments and suggestions to improve this paper. This research is supported by the Natural Science Research Project of Higher Education of Jiangsu (Grant No. 15KJB480002), the National Natural Science Foundation of China (Grant No. 51477109) and the Science and Technology Project of Ministry of Housing and Urban-Rural Development (Grant No. 2016-K1-19, 2014-K1-040).
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Ma, J. et al. (2016). Maximum Power Point Estimation for Photovoltaic Modules via RBFNN. In: Park, J., Jin, H., Jeong, YS., Khan, M. (eds) Advanced Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 393. Springer, Singapore. https://doi.org/10.1007/978-981-10-1536-6_52
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DOI: https://doi.org/10.1007/978-981-10-1536-6_52
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