A Robust Online Adaptive B-Spline MPPT Control of Three-Phase Grid-Coupled Photovoltaic Systems Under Real Partial Shading Condition

This work contributes to the research on three-phase photovoltaic systems via a new more robust online adaptive neuro-fuzzy maximum power point tracking (MPPT) control technique considering real partial shading and load conditions. The trapping in local minima and high computational cost in the existing neuro-fuzzy structure are addressed by incorporating B-spline function in the proposed control method. The system parameters are adjusted online via an adaptive neuro-fuzzy inference system rules acquired from the MPPT error. The optimization part of the proposed control law is performed through an online learning gradient-descent back-propagation algorithm. The superiority of the proposed control in terms of energy conversion efficiency, MPPT error, and output power is checked under the same operating conditions with well-known used traditional and intelligent MPPT control algorithms. Finally, the robustness of the proposed control is confirmed through a complete day simulation and comparison indexes.

[1]  A. K. Mukerjee,et al.  DC power supply used as photovoltaic simulator for testing MPPT algorithms , 2007 .

[2]  Yi-Hua Liu,et al.  Optimization of a Fuzzy-Logic-Control-Based MPPT Algorithm Using the Particle Swarm Optimization Technique , 2015 .

[3]  Babloo Kumar,et al.  Design of FPGA based open circuit voltage MPPT charge controller for solar PV system , 2013, 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT).

[4]  H. Guillard,et al.  Robust nonlinear control associating robust feedback linearization and H/sub /spl infin// control , 2006, IEEE Transactions on Automatic Control.

[5]  Jian-Liung Chen,et al.  Maximum photovoltaic power tracking for the PV array using the fractional-order incremental conductance method , 2011 .

[6]  Jianwei Zhang,et al.  Unsupervised learning of control surfaces based on B-spline models , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[7]  Yi-Hua Liu,et al.  Neural-network-based maximum power point tracking methods for photovoltaic systems operating under fast changing environments , 2013 .

[8]  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..

[9]  Najib Essounbouli,et al.  A GA-based optimization of a fuzzy-based MPPT controller for a photovoltaic pumping system, Case study for Laghouat, Algeria , 2016 .

[10]  Haitham Abu-Rub,et al.  Adaptive neuro-fuzzy inference system-based maximum power point tracking of solar PV modules for fast varying solar radiations , 2012 .

[11]  Chih-Ming Hong,et al.  Dynamic operation and control of microgrid hybrid power systems , 2014 .

[12]  Sapto Wibowo,et al.  Maximum power point tracking for photovoltaic using incremental conductance method , 2015 .

[13]  Laiq Khan,et al.  Adaptive B-Spline Based Neuro-Fuzzy Control for Full Car Active Suspension System , 2013 .

[14]  Youssef Barradi,et al.  The MPPT control of PV system by using neural networks based on Newton Raphson method , 2014, 2014 International Renewable and Sustainable Energy Conference (IRSEC).

[15]  Zhao Zhengming,et al.  A Single-Stage Three-Phase Grid-Connected Photovoltaic System With Modified MPPT Method and Reactive Power Compensation , 2007, IEEE Transactions on Energy Conversion.

[16]  Jianwen Wu,et al.  Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems , 2016 .

[17]  Daricha Sutivong,et al.  Avoiding Local Minima in Feedforward Neural Networks by Simultaneous Learning , 2007, Australian Conference on Artificial Intelligence.

[18]  Jaw-Kuen Shiau,et al.  A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables , 2015, Algorithms.

[19]  Christopher J. Harris,et al.  A neurofuzzy network structure for modelling and state estimation of unknown nonlinear systems , 1997, Int. J. Syst. Sci..

[20]  D. Nichols,et al.  An optimal design of a grid connected hybrid wind/photovoltaic/fuel cell system for distributed energy production , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[21]  Mohammed A. Hannan,et al.  Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller , 2012 .

[22]  Ching-Hung Lee,et al.  Identification and control of dynamic systems using recurrent fuzzy neural networks , 2000, IEEE Trans. Fuzzy Syst..

[23]  G. Petrone,et al.  Predictive & Adaptive MPPT Perturb and Observe Method , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[24]  Kesheng Wang,et al.  Using B-spline neural network to extract fuzzy rules for a centrifugal pump monitoring , 2001, J. Intell. Manuf..

[25]  S. Umashankar,et al.  Perturb and observe MPPT algorithm for solar PV systems-modeling and simulation , 2011, 2011 Annual IEEE India Conference.

[26]  Mohd Amran Mohd Radzi,et al.  A Photovoltaic-Based SEPIC Converter with Dual-Fuzzy Maximum Power Point Tracking for Optimal Buck and Boost Operations , 2016 .

[27]  L. Fan,et al.  Adaptive Non-singular Terminal Sliding Mode Control for DC-DC Converters , 2011 .

[28]  Carlos Andrés Ramos-Paja,et al.  Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems , 2015 .

[29]  Sibghatullah Nasir,et al.  Microfinance in India: Contemporary Issues and Challenges , 2013 .

[30]  M. Neila,et al.  Adaptive terminal sliding mode control for rigid robotic manipulators , 2011 .

[31]  Aranzazu D. Martin,et al.  Neuro-fuzzy control of a grid-connected photovoltaic system with power quality improvement , 2013, Eurocon 2013.

[32]  S. K. Kollimalla,et al.  A Novel Adaptive P&O MPPT Algorithm Considering Sudden Changes in the Irradiance , 2014, IEEE Transactions on Energy Conversion.

[33]  Nuri Gokmen,et al.  Voltage band based global MPPT controller for photovoltaic systems , 2013 .

[34]  L. Junfeng,et al.  A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems , 2016 .

[35]  Mahdi Salimi,et al.  Cascade nonlinear control of DC-DC buck/boost converter using exact feedback linearization , 2015, 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS).

[36]  Kashif Ishaque,et al.  The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions , 2014 .

[37]  Ziqian Liu,et al.  Self‐tuning control of electrical machines using gradient descent optimization , 2007 .

[38]  B N Alajmi,et al.  Fuzzy-Logic-Control Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System , 2011, IEEE Transactions on Power Electronics.