ANFIS modeling of photovoltaic systems to mitigate partially shaded conditions

The increasing demand for clean energy and sustainable living have been promising for Photovoltaic (PV) systems. However, in urban environments, uniform solar insolation is not guaranteed. Thus, conventional Maximum Power Point Tracking (MPPT) techniques severely fail at Partially Shaded Conditions (PSC). In response, a new hybrid technique is being proposed using Adaptive Network-Fuzzy Inference System (ANFIS) using Takagi-Sugeno Architecture in combination with an existing conventional technique for Power Point Tracking at PSC. Fuzzy Inference Systems (FIS) of different rule base size and membership functions are developed and their feasibility is assessed for differing grid size and variable insolation levels is discussed.

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

[2]  Barjeev Tyagi,et al.  Design and Implementation of Fuzzy Controller on FPGA , 2012 .

[3]  F. Blaabjerg,et al.  Power inverter topologies for photovoltaic modules-a review , 2002, Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344).

[4]  E. Karatepe,et al.  Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells , 2007 .

[5]  M. Vitelli,et al.  Optimization of perturb and observe maximum power point tracking method , 2005, IEEE Transactions on Power Electronics.

[6]  David Arancibia,et al.  Modular multilevel converter with integrated storage for solar photovoltaic applications , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[7]  B. Lehman,et al.  Solar Photovoltaic Array's Shadow Evaluation Using Neural Network with On-Site Measurement , 2007, 2007 IEEE Canada Electrical Power Conference.

[8]  A. Bidram,et al.  Control and Circuit Techniques to Mitigate Partial Shading Effects in Photovoltaic Arrays , 2012, IEEE Journal of Photovoltaics.

[9]  Efstratios I. Batzelis,et al.  Partial Shading Analysis of Multistring PV Arrays and Derivation of Simplified MPP Expressions , 2015, IEEE Transactions on Sustainable Energy.

[10]  Massimo Vitelli,et al.  A Multivariable Perturb-and-Observe Maximum Power Point Tracking Technique Applied to a Single-Stage Photovoltaic Inverter , 2011, IEEE Transactions on Industrial Electronics.

[11]  Josep M. Guerrero,et al.  A Novel Improved Variable Step-Size Incremental-Resistance MPPT Method for PV Systems , 2011, IEEE Transactions on Industrial Electronics.

[12]  E. Muljadi,et al.  A cell-to-module-to-array detailed model for photovoltaic panels , 2012 .

[13]  T. Hiyama,et al.  Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions Syafaruddin 1 , 2009 .

[14]  Engin Karatepe,et al.  Simple and high-efficiency photovoltaic system under non-uniform operating conditions , 2010 .

[15]  Jean-Paul Gaubert,et al.  An Improved Maximum Power Point Tracking for Photovoltaic Grid-Connected Inverter Based on Voltage-Oriented Control , 2011, IEEE Transactions on Industrial Electronics.

[16]  A. Bakhshai,et al.  A Novel topology and control strategy for maximum power point trackers and multi-string grid-connected PV inverters , 2008, 2008 Twenty-Third Annual IEEE Applied Power Electronics Conference and Exposition.

[17]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[18]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[19]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[20]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[21]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[22]  Sairaj V. Dhople,et al.  Multiple-input boost converter to minimize power losses due to partial shading in photovoltaic modules , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

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

[24]  Bogdan M. Wilamowski,et al.  Implementing a fuzzy system on a field programmable gate array , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[25]  Kumpati S. Narendra,et al.  Neural Networks In Dynamical Systems , 1990, Other Conferences.

[26]  Saad Mekhilef,et al.  Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter , 2011, IEEE Transactions on Industrial Electronics.