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Genetic k-means algorithm based RBF network for photovoltaic MPP prediction

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  • Liao, Chiung-Chou

Abstract

By operating PV systems more close to the maximum power point (MPP), the output efficiency of PV panels can be improved. Traditionally, the k-means algorithm (KMA) is one of the most popular methods to classify the input patterns of the radial basis function (RBF) network. Although the KMA has an ability to cluster the training patterns rapidly, it usually converges to a local minimum and can be oversensitive to randomly initial partitions. To solve these significant problems, a hybrid skill called Genetic k-Means Algorithm (GKA) is proposed to improve the effectiveness of maximum power point track. Besides, the proposed GKA based clustering approach can overcome the problem of oversensitivity to randomly initial partitions in the existing KMA. In order to determine a suitable number of centers in RBF from the input data, the orthogonal least squares (OLS) learning algorithm was used in this paper. By precisely clustering of the training patterns, the objective to accurately and rapidly approximate the MPP of PV system can be achieved with the least squares criterion in RBF network. Also, this paper employed the actual data obtained from the practical PV systems and with which the developed MPP tracker method was proven to be effective.

Suggested Citation

  • Liao, Chiung-Chou, 2010. "Genetic k-means algorithm based RBF network for photovoltaic MPP prediction," Energy, Elsevier, vol. 35(2), pages 529-536.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:2:p:529-536
    DOI: 10.1016/j.energy.2009.10.021
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    Cited by:

    1. Shi, Xing, 2011. "Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm," Energy, Elsevier, vol. 36(3), pages 1659-1667.
    2. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    3. Jiménez-Fernández, S. & Salcedo-Sanz, S. & Gallo-Marazuela, D. & Gómez-Prada, G. & Maellas, J. & Portilla-Figueras, A., 2014. "Sizing and maintenance visits optimization of a hybrid photovoltaic-hydrogen stand-alone facility using evolutionary algorithms," Renewable Energy, Elsevier, vol. 66(C), pages 402-413.
    4. Mellit, Adel & Kalogirou, Soteris A., 2014. "MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives," Energy, Elsevier, vol. 70(C), pages 1-21.
    5. Kadri, Riad & Andrei, Horia & Gaubert, Jean-Paul & Ivanovici, Traian & Champenois, Gérard & Andrei, Paul, 2012. "Modeling of the photovoltaic cell circuit parameters for optimum connection model and real-time emulator with partial shadow conditions," Energy, Elsevier, vol. 42(1), pages 57-67.
    6. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    7. Kirchner-Bossi, N. & Prieto, L. & García-Herrera, R. & Carro-Calvo, L. & Salcedo-Sanz, S., 2013. "Multi-decadal variability in a centennial reconstruction of daily wind," Applied Energy, Elsevier, vol. 105(C), pages 30-46.
    8. Chen, Cheng-Chuan & Chang, Hong-Chan & Kuo, Cheng-Chien & Lin, Chien-Chin, 2013. "Programmable energy source emulator for photovoltaic panels considering partial shadow effect," Energy, Elsevier, vol. 54(C), pages 174-183.
    9. Dadouche, F. & Béthoux, O. & Kleider, J.-P., 2011. "New silicon thin-film technology associated with original DC–DC converter: An economic alternative way to improve photovoltaic systems efficiencies," Energy, Elsevier, vol. 36(3), pages 1749-1757.
    10. Bizon, Nicu, 2016. "Global Maximum Power Point Tracking (GMPPT) of Photovoltaic array using the Extremum Seeking Control (ESC): A review and a new GMPPT ESC scheme," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 524-539.
    11. Bizon, Nicu, 2013. "Energy harvesting from the PV Hybrid Power Source," Energy, Elsevier, vol. 52(C), pages 297-307.
    12. Huang, Junwei & Xiao, Qingtai & Liu, Jingjing & Wang, Hua, 2019. "Modeling heat transfer properties in an ORC direct contact evaporator using RBF neural network combined with EMD," Energy, Elsevier, vol. 173(C), pages 306-316.
    13. Carro-Calvo, L. & Salcedo-Sanz, S. & Kirchner-Bossi, N. & Portilla-Figueras, A. & Prieto, L. & Garcia-Herrera, R. & Hernández-Martín, E., 2011. "Extraction of synoptic pressure patterns for long-term wind speed estimation in wind farms using evolutionary computing," Energy, Elsevier, vol. 36(3), pages 1571-1581.
    14. Jiang, Joe-Air & Wang, Jen-Cheng & Kuo, Kun-Chang & Su, Yu-Li & Shieh, Jyh-Cherng & Chou, Jui-Jen, 2012. "Analysis of the junction temperature and thermal characteristics of photovoltaic modules under various operation conditions," Energy, Elsevier, vol. 44(1), pages 292-301.

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