Neural network based global maximum power point tracking under partially shaded conditions

Partial shading changes characteristics of a solar panel and creates a number of local maximum power points (MPPs) that only one of them is the global MPP. On the other hand measurement of light intensity is quite hard and requires use of specialized sensors. In this paper, a new method for tracking the global MPP under partially shaded conditions using neural network is proposed in which instead of measuring light intensity, the network approximates it. For this purpose, at first by measuring the voltage, current and temperature of panels we estimate the radiation intensity and then a neural network is trained using radiation intensity and temperature of panels as inputs and MPP as output of the network. Finally, this method is simulated in MATLAB/Simulink environment and results show the effectiveness of the proposed method.

[1]  Dionisio Ramirez,et al.  Accurate and fast convergence method for parameter estimation of PV generators based on three main points of the I–V curve , 2011 .

[2]  Tsutomu Hoshino,et al.  Maximum photovoltaic power tracking: an algorithm for rapidly changing atmospheric conditions , 1995 .

[3]  Jean-Philippe Martin,et al.  A new approach in tracking maximum power under partially shaded conditions with consideration of converter losses , 2011 .

[4]  Masafumi Miyatake,et al.  A novel maximum power point tracking for photovoltaic applications under partially shaded insolation conditions , 2008 .

[5]  N. H. Helwa,et al.  Maximum power point traking controller for PV systems using neural networks , 2005 .

[6]  Muhammad Amjad,et al.  A direct control based maximum power point tracking method for photovoltaic system under partial shading conditions using particle swarm optimization algorithm , 2012 .

[7]  Vahan Gevorgian,et al.  A peak power tracker for small wind turbines in battery charging applications , 1999 .

[8]  Engin Karatepe,et al.  Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions , 2009 .

[9]  B. Cho,et al.  Design and analysis of a microprocessor-controlled peak-power-tracking system [for solar cell arrays] , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Mohammad Hamiruce Marhaban,et al.  Artificial neural network based maximum power point tracking controller for photovoltaic standalone system , 2016 .

[11]  Joseph A. Jervase,et al.  Solar radiation estimation using artificial neural networks , 2002 .

[12]  J.J. Schoeman,et al.  A simplified maximal power controller for terrestrial photovoltaic panel arrays , 1982, 1982 IEEE Power Electronics Specialists conference.

[13]  Mohammad Hassan Moradi,et al.  Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review , 2013 .

[14]  Gerard Champenois,et al.  Modeling of the photovoltaic cell circuit parameters for optimum connection model and real-time emulator with partial shadow conditions , 2012 .

[15]  Mohammad Hassan Moradi,et al.  A hybrid maximum power point tracking method for photovoltaic systems , 2011 .

[16]  Jongrong Lin,et al.  Implementation of a DSP-controlled photovoltaic system with peak power tracking , 1998, IEEE Trans. Ind. Electron..

[17]  Yaow-Ming Chen,et al.  Multiinput converter with power factor correction, maximum power point tracking, and ripple-free input currents , 2004 .

[18]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.

[19]  Kashif Ishaque,et al.  A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition , 2013 .

[20]  Takashi Hiyama,et al.  Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control , 1995 .

[21]  Toshihiko Noguchi,et al.  Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system , 2002, IEEE Trans. Ind. Electron..

[22]  Bimal K. Bose,et al.  Artificial neural network applications in power electronics , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[23]  Johan H R Enslin,et al.  Integrated photovoltaic maximum power point tracking converter , 1997, IEEE Trans. Ind. Electron..

[24]  N. Shalavi,et al.  A robust hybrid method for maximum power point tracking in photovoltaic systems , 2013 .

[25]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.