Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Said Zakaria Said; Lamine Thiaw; Cyrus Wekesa Wabuge

doi:doi:10.11648/j.ijepe.20170606.12

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Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm

Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge

Published in International Journal of Energy and Power Engineering (Volume 6, Issue 6)

Received: 10 October 2017 Accepted: 23 October 2017 Published: 7 December 2017

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Abstract

This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.

Published in	International Journal of Energy and Power Engineering (Volume 6, Issue 6)
DOI	10.11648/j.ijepe.20170606.12
Page(s)	91-99
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Maximum Power Point Tracking, Particle Swarm Optimization, Artificial Neural Network, Photovoltaic Generators

References

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[4]	Xu W, Mu C, Jin J (2014) Novel linear iteration maximum power point tracking algorithm for photovoltaic power generation. IEEE Trans Appl Supercond 24 (5):1–6.
[5]	Ji YH, Jung DY, Kim JG, Kim JH, Lee TW, Won CY (2011) A real maximum power point tracking method for mismatching compensation in PV array under partially shaded conditions. IEEE Trans Power Electron 26 (4):1001–1009.
[6]	Blanes JM, Toledo FJ, Montero S, Garrigós A (2013) In-site real-time photovoltaic I-V curves and maximum power point estimator. IEEE Trans Power Electron 28 (3):1234–1240.
[7]	A. K. Rai, N. D. Kaushika, B. Singh, N. Agarwal, Simulation model of ANN based maximum power point tracking controller for solar PV system, Solar Energy Materials and Solar Cells, 95 (2), pp. 773778, 2011.
[8]	A. A. Kulaksız, ve R. Akkaya, Training Data Optimization for ANNs using Genetic Algorithms to Enhance MPPT Efficiency of a Stand-alone PV System, Turkish Journal of Electrical Engineering & Computer Sciences, 20 (2), pp. 241-254, 2012.
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[10]	M. Miyatake, M. veerachary, F. Toriumi, N. Fujii, and H. Ko, “Maximum power point tracking of multiple photovoltaic arrays: a PSO approach,” IEEE Trans. Aerosp. Electron. Syst., vol. 47, no. 1, pp. 367–380, Jan. 2011.
[11]	K. L. Lian, J. H. Jhang, and I. S. Tian, “A maximum power point tracking method based on perturb-and-observe combined with particle swarm optimization,” IEEE J. Photovoltaics, vol. 4, no. 2, pp. 626–633, Mar. 2014.
[12]	R. B. A. Koad and A. F. Zobaa, “Comparison between the conventional methods and PSO based MPPT algorithm for photovoltaic systems,” World Academy of Science, Engineering and Technology, International Science Index 88, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering, vol. 8, no. 4, pp. 673-678. 2014.
[13]	Punitha K.; Devaraj D.; Sakthivel S., Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions, In: Energy Vol. 62, December 2013, Page (s): 330-340.
[14]	M. Abdulkadir, A. S. Samosir, and A. H. M. Yatim, “Modelling and simulation of maximum power point tracking of photovoltaic system in Simulink model,” in Proceedings of the IEEE International Conference on Power and Energy (PECon’12), pp. 325–330, Kota Kinabalu, Malaysia, December 2012.
[15]	M. Miyatake, F. Toriumi, T. Endo, and N. Fujii, “A novel maximum power point tracker controlling several converters connected to photovoltaic arrays with particle swarm optimization technique,” in Proceedings of the European Conference on Power Electronics and Applications (EPE ’07), pp. 1–10, Aalborg, Denmark, September 2007.
[16]	Lyden S, Haque ME (2015) Maximum Power Point Tracking techniques for photovoltaic systems: a comprehensive review and comparative analysis. Renew Sustain Energy Rev 52:1504–1518.
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[18]	K. T. K. Teo, P. Y. Lim, B. L. Chua, H. H. Goh, and M. K. Tan, “Maximum power point tracking of partially shaded photovoltaic arrays using particle swarm optimization,” Proc. 4 th International Conference on Artificial Intelligence with Applications in Engineering and Technology, Kota Kinabalu, Malaysia, 2014, pp. 247-252, doi: 10.1109/ICAIET.2014.48

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APA Style

Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge. (2017). Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. International Journal of Energy and Power Engineering, 6(6), 91-99. https://doi.org/10.11648/j.ijepe.20170606.12

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ACS Style

Said Zakaria Said; Lamine Thiaw; Cyrus Wekesa Wabuge. Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. Int. J. Energy Power Eng. 2017, 6(6), 91-99. doi: 10.11648/j.ijepe.20170606.12

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AMA Style

Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge. Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. Int J Energy Power Eng. 2017;6(6):91-99. doi: 10.11648/j.ijepe.20170606.12

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@article{10.11648/j.ijepe.20170606.12,
  author = {Said Zakaria Said and Lamine Thiaw and Cyrus Wekesa Wabuge},
  title = {Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm},
  journal = {International Journal of Energy and Power Engineering},
  volume = {6},
  number = {6},
  pages = {91-99},
  doi = {10.11648/j.ijepe.20170606.12},
  url = {https://doi.org/10.11648/j.ijepe.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20170606.12},
  abstract = {This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.},
 year = {2017}
}

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TY  - JOUR
T1  - Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm
AU  - Said Zakaria Said
AU  - Lamine Thiaw
AU  - Cyrus Wekesa Wabuge
Y1  - 2017/12/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijepe.20170606.12
DO  - 10.11648/j.ijepe.20170606.12
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 91
EP  - 99
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20170606.12
AB  - This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.
VL  - 6
IS  - 6
ER  -

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Author Information

Said Zakaria Said

Department of Electrical Engineering, Pan African University for Basic Sciences Technology and Innovation, Nairobi, Kenya
Lamine Thiaw

Department of Electrical and Information Engineering, University of Nairobi, Nairobi, Kenya
Cyrus Wekesa Wabuge

Department of Electrical Engineering, Ecole Superieure Polytechnique, Chekh Anta Diop University, Dakar, Senegal

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APA Style

Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge. (2017). Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. International Journal of Energy and Power Engineering, 6(6), 91-99. https://doi.org/10.11648/j.ijepe.20170606.12

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ACS Style

Said Zakaria Said; Lamine Thiaw; Cyrus Wekesa Wabuge. Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. Int. J. Energy Power Eng. 2017, 6(6), 91-99. doi: 10.11648/j.ijepe.20170606.12

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AMA Style

Said Zakaria Said, Lamine Thiaw, Cyrus Wekesa Wabuge. Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm. Int J Energy Power Eng. 2017;6(6):91-99. doi: 10.11648/j.ijepe.20170606.12

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@article{10.11648/j.ijepe.20170606.12,
  author = {Said Zakaria Said and Lamine Thiaw and Cyrus Wekesa Wabuge},
  title = {Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm},
  journal = {International Journal of Energy and Power Engineering},
  volume = {6},
  number = {6},
  pages = {91-99},
  doi = {10.11648/j.ijepe.20170606.12},
  url = {https://doi.org/10.11648/j.ijepe.20170606.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20170606.12},
  abstract = {This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.},
 year = {2017}
}

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TY  - JOUR
T1  - Maximum Power Point Tracking of Photovoltaic Generators Partially Shaded Using a Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm
AU  - Said Zakaria Said
AU  - Lamine Thiaw
AU  - Cyrus Wekesa Wabuge
Y1  - 2017/12/07
PY  - 2017
N1  - https://doi.org/10.11648/j.ijepe.20170606.12
DO  - 10.11648/j.ijepe.20170606.12
T2  - International Journal of Energy and Power Engineering
JF  - International Journal of Energy and Power Engineering
JO  - International Journal of Energy and Power Engineering
SP  - 91
EP  - 99
PB  - Science Publishing Group
SN  - 2326-960X
UR  - https://doi.org/10.11648/j.ijepe.20170606.12
AB  - This paper addresses the research methodology for Maximum Power Point Tracking (MPPT). Photovoltaic (PV) Generators may receive different level of solar irradiance and temperature, such as partially shaded by clouds, tree leaves or nearby building. Under partial shaded conditions, several peak power points can occur when the PV module is shaded, which would significantly reduce the energy produced by PV Generators without proper control. Therefore, a Maximum Power Point Tracking (MPPT) Algorithm is used to extract the maximum available PV power from the PV array. However, the common used conventional MPPT algorithms are unable to detect global peak (GP) power point with the presence of several local peaks (LP). In this paper, a hybrid Particle Swarm Optimization and Artificial Neural Network (PSO-ANN) algorithm is proposed to detect the global peak power. MATLAB/Simulink is used to simulate a PV system which consists of PV Generators, DC–DC boost converter, a hybrid PSO-ANN Algorithm, and a resistive load. The simulation results are compared and discussed. The proposed algorithm should perform well to detect the Global Peak of the PV array even under partial shaded conditions.
VL  - 6
IS  - 6
ER  -

Copy | Download