Artificial intelligence and ANFIS reduced rule for equivalent parameter estimation of PV module on various weather conditions utilized for MPPT

Authors

  • Rati Wongsathan Department of Electrical Engineering, Faculty of Engineering, North-Chiang Mai University 169 Hangdong Rd. Chiang Mai
  • Isaravuth Seedadan Department of Electrical Engineering, Faculty of Engineering, North-Chiang Mai University 169 Hangdong Rd. Chiang Mai

Keywords:

Multi-layered perceptron neural network, radial basis function neural networks, ANFIS, PV module, genetic algorithm

Abstract

      In order to facilitate the most appropriate and robust method for the overall efficiency of photovoltaic (PV), providing a more accurate optimization algorithm to extract the equivalent parameters of PV models prior to installation is necessary. In this paper, a numerical technique based on genetic algorithms (GAs)is performed to determine the equivalent parameters of PV solar module based on single diode model including with the photo current (Iph), the saturated diode current (Is), the series resistance (Rs), the parallel resistance (Rsh), and the ideality factor (n). The measured I-V data from the experiment with the uncertainties arising from measurement noise in various weather conditions is adopted to extract these parameters by GA in order to overcome the local minima trap which occurs in the non-convex optimization problem. The preliminary solutions by GA extraction are used to train the estimation model including multi-layered perceptron neural network (MLPNN), radial basis function neural network (RBFNN), and adaptive neuro-fuzzy inference system (ANFIS)model for all weather conditions. The ANFIS modelremarkably showed the best performance which preserves the RMSE lower than the rest for both the interpolation and extrapolation. However, the complexity causedbynumber of ANFIS model parameters and high computation time makes it unsuitable in the real practice. The ANFIS reduced model is then introduced and designed to select the significant rule node of ANFIS model by keeping an acceptable accuracy. The overall proposed model was used to determine the corresponding maximum power point (MPP)from the I-V characteristic obtained from the estimated parameter whichgenerates the power difference as the input of the fuzzy logic controller (FLC)implementation based the maximum power point tracking (MPPT).

References

A. Chouder, S. Silvestre, N. Sadaoui, and L. Rahmai, “Modeling and simulation of a grid connected PV system based on the evaluation of main PV module parameters,” Simul. Model Pract. Theory, (2012) 46-58.

Available online at: www.egco.com (last accessed 10/10/2015)

2M.A. De Blas, J.L. Torres, E. Prieto, A. Garcia, “Selecting a suitable model for characterizing photovoltaic devices,” Renewable Energy, 25 (2002) 371-380.

Y.C. Kuo, T. J. Liang, and J. Chen, “Novel maximum power point tracking controller for photovoltaic energy conversion system,” IEEE Trans. On Ind. Elect., 48 (3) (2001) 594-601.

R. Chenni, M. Makhlouf, T. Kerbache, and A. Bouzid, “A detailed modeling method for photovoltaic cells,” Energy, 32(9) (2007)1724-1730.

I. Kashif, S. Zainal, T. Hamed, “Simple, fast and accurate two diode model for photovoltaic modules,” Solar Energy Mater Solar Cells, 95(2) (2011) 586-594.

A. Jain, and A. Kapoor, “Exact analytical solution of the parameters of real solar cells using Lambert W-function,” Solar and Energy Mat. & Solar cells, 81(2004) 269-277.

D. Picault, B. Raison, S. Bachan, J. de la Casa, and J. Aguilera, “Forecasting photovoltaic array power production subject to mismatch losses,” Solar Energy, 84(7)(2010)1301-1309.

Y. Chen, X. Wang, D. Li, R. Hong, and H. Shen, “Parameters extraction from commercial solar cells I-V characteristics and shunt analysis,” Applied Energy, 88(6) (2011) 2239-2244.

Y. Chen, X. Wang, D. Li, R. Hong, and H. Shen, “Parameters extraction from commercial solar cells I-V characteristics and shunt analysis,” Applied Energy, 88(6) (2011) 2239-2244.

A. Dobos, “An improved coefficient calculator for the California energy commission 6 parameter photovoltaic module model,” J. of Solar Energy Eng., 134(2)(2012).

H. Tian, and et. al., “ a cell-to-module-to-array detailed model for photovoltaic panels,” Solar Energy, 86(9) (2012) 2695-2706.

K.M. El-Naggar, M.R. AlRashidi, M.F. AlHajri, and A. K. Al-Othman, “Simulated annealing algorithm for photovoltaic parameters identification,” Solar Energy, 86 (2012) 266-274.

E.Q.B. Macabebe, C.J. Sheppard, and E.E. van Dyk, “Parameter extraction from I-V characteristics of PV devices,” Solar Energy, 85 (2011) 12-18.

K. Ishaque, Z. Salam, S. Mkhilef, and A. Shamsudin, “Parameter extraction of solar photovoltaic modules using penalty-based differential evolution,” Appl. Energy, 99 (2012) 297-308.

J.A. Jervase, H. Bourdoucen, and A.Al-Lawati, “Solar cell parameter extraction using genetic algorithms,” Measurement Science and Tech., 12 (2001) 1992-1925.

M. Ye, X. Wang, and Y. Xu, “Real-time identification of optimal operating points in photovoltaic power system,” IEEE Trans. on Industrial Elect., 53 (2006) 1017-1026.

W. Huang, C. Jiang, L. Xue, and D. Song, “Extracting solar cell model parameters based on chaos particle swarm algorithm,” in Proc. Of the Int. Conf. on Elect. Info. and Ctrl. Eng. (ICEICE’11), (2011) 398-402.

I. Fister, I. Fister Jr, XS. Yang, and J. Brest, “A comprehensive review of firefly algorithms,” Swarm and Evo. Comp., 2013.

M.R. Alrashidi, K.M. El-Naggar, M.F. AlHajri, and A.K. Al-Othman, “A new estimation approach for determining the I-V characteristics of solar cells,” Solar Energy, 85(7) (2011) 1543-1550.

M.R. Alrashidi, K.M. El-Naggar, M.F. AlHajri, and A.K. Al-Othman, “Extraction of photovoltaic characteristics using simulated annealing,” Conf. on Advances in Eng. Sci. and Appl. Math., 2014.

H. Al-Hamadi, “Fuzzy estimation analysis of photovoltaic model parameters,” J. of Power and Energy Eng., 3 (2015) 39-43.

F. Bonanno, G. Capizzi, G. Napoli, and G.M. Tina, “A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module,” Appl. Energy, 97 (2012) 956-961.

S. Kalika, L. Rajaji, and S. Gupta, “Intelligent technique based modeling for PVPS,” Int. J. of Eng. and Innovative Tech, 2 (2012) 211-215.

G.Walker, “Evaluating MPPT converter topologies using a MATLAB PV model,” J. Elect. Electron. Eng. Aus., 21 (2001) 49-56.

L. Davis, Handbook of Genetic Algorithms, New York: Van Nostrand Reinhold, 1991.

T. Xie, H. Yu and B. Wilamowski, “Comparison between traditional neural networks and radial basis function networks,” Ind. Electrn. (ISIE), (2011) 1194-1199.

J.K. Sing, D.K. Basu, M. Nasipuri, M. Kundu, “Improved K-means algorithm in the design of RBF neural networks,” IEEE, 2(2003).

S. Chen, C.F.N Cowen, and P.M Grant, “Orthogonal least square learning algorithm for radial basis function networks,” IEEE Trans. On NN., 2(2) (1991).

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Published

21 June 2017

How to Cite

Wongsathan, R., & Seedadan, I. (2017). Artificial intelligence and ANFIS reduced rule for equivalent parameter estimation of PV module on various weather conditions utilized for MPPT. Journal of Renewable Energy and Smart Grid Technology, 12(1), 37–56. Retrieved from https://ph01.tci-thaijo.org/index.php/RAST/article/view/90328

Issue

Vol. 12 No. 1 (2017): January - June 2017

Section

Articles

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