A Predictive Model for Solar Photovoltaic Power based on Computational Intelligence Technique
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This paper introduces a novel method for representing the photovoltaic (PV) characteristics using Takagi–Sugeno type neuro-fuzzy network (NF). The proposed NF uses four layers with sixty-four fuzzy rules. Moreover, an improved self-tuning method is developed based on the PV system and its high-performance requirements, to adjust the parameters of the fuzzy logic in order to minimize the square of the error between actual and reference outputs. The developed PV model has a compact structure, an interpretable set of rules and ultimately is accurate in predicting the output values for given input samples. The NF-PV model has been applied for reconstructing a set of practical current–voltage characteristics, and it has been shown to compare well with the measured values. The proposed approach can also be used to predict and extract the maximum power points of individual PV modules in real time. Numerical and experimental data have confirmed its accuracy.
KeywordsNeuro-fuzzy network Photovoltaic system Prediction
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- 5.Jawarneh, M.; Domijan, A.; Luo, W.: The performance of maximum power point tracking (MPPT) algorithms for photovoltaic systems. Int. J. Power Energy Syst. 35(4), 141–148 (2015). https://doi.org/10.2316/Journal.203.2015.4.203-6153 Google Scholar
- 8.Mohanty, P.; Bhuvaneswari, G.; Balasubramanian, R.; Dhaliwal, N.K.: MATLAB based modeling to study the performance of different MPPT techniques used for solar PV system under various operating conditions. Renew. Sustain. Energy Rev. 38, 581–593 (2014). https://doi.org/10.1016/j.rser.2014.06.001 CrossRefGoogle Scholar
- 13.Chen, W.; Song, X.; Huang, H.; Yang, X.: Numerical and experimental investigation of parasitic edge capacitance for photovoltaic panel. Paper presented at the 2014 international power electronics conference, IPEC-Hiroshima-ECCE Asia 2014. 2967–2971, 2014. https://doi.org/10.1109/IPEC.2014.6870105
- 14.Shadmand, M.B.; Balog, R.S.: A finite-element analysis approach to determine the parasitic capacitances of high-frequency multiwinding transformers for photovoltaic inverters. Paper presented at the 2013 IEEE power and energy conference at Illinois, PECI 2013. 114–119, 2013. https://doi.org/10.1109/PECI.2013.6506044
- 25.Khaehintung, N.; Kunakorn, A.; Sirisuk, P.: A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control. Int. J. Control Autom. Syst. 8(2), 289–300 (2010). https://doi.org/10.1007/s12555-010-0215-7 CrossRefGoogle Scholar
- 27.Goetzberger, A.; Hoffmann, V.U.: Photovoltaic Solar Energy Generation. Springer Series in Optical Sciences, Berlin (2005)Google Scholar
- 32.Chang, C.; Tao, C.: A novel approach to implement Takagi–Sugeno fuzzy models. IEEE Trans. Cybern. Article in Press (2017). https://doi.org/10.1109/TCYB.2017.2701900
- 33.Lewis, C.D.: International Business Forecasting Methods. Butter-worths, London (1982)Google Scholar