Neuro-Fuzzy Wavelet Based Adaptive MPPT Algorithm for Photovoltaic Systems

An intelligent control of photovoltaics is necessary to ensure fast response and high efficiency under different weather conditions. This is often arduous to accomplish using traditional linear controllers, as photovoltaic systems are nonlinear and contain several uncertainties. Based on the analysis of the existing literature of Maximum Power Point Tracking (MPPT) techniques, a high performance neuro-fuzzy indirect wavelet-based adaptive MPPT control is developed in this work. The proposed controller combines the reasoning capability of fuzzy logic, the learning capability of neural networks and the localization properties of wavelets. In the proposed system, the Hermite Wavelet-embedded Neural Fuzzy (HWNF)-based gradient estimator is adopted to estimate the gradient term and makes the controller indirect. The performance of the proposed controller is compared with different conventional and intelligent MPPT control techniques. MATLAB results show the superiority over other existing techniques in terms of fast response, power quality and efficiency.

[1]  K. B. Mohanty,et al.  Performance improvement in MPPT of SPV system using NN controller under fast changing environmental condition , 2016, 2016 IEEE 6th International Conference on Power Systems (ICPS).

[2]  Chih-Ming Hong,et al.  Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system , 2013 .

[3]  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).

[4]  Faa-Jeng Lin,et al.  Adaptive fuzzy-neural-network control for a DSP-based permanent magnet linear synchronous motor servo drive , 2006, IEEE Transactions on Fuzzy Systems.

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

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

[7]  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).

[8]  Weidong Xiao,et al.  Topology Study of Photovoltaic Interface for Maximum Power Point Tracking , 2007, IEEE Transactions on Industrial Electronics.

[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]  Abdelghani Harrag,et al.  A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation , 2017 .

[11]  P. Rodriguez,et al.  Identification and maximum power point tracking of photovoltaic generation by a local neuro-fuzzy model , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[12]  Marian K. Kazimierczuk,et al.  Dynamic performance of PWM DC-DC boost converter with input voltage feedforward control , 1999 .

[13]  Djamila Rekioua,et al.  Tracking power photovoltaic system with sliding mode control strategy , 2013 .

[14]  Xin-Jian Zhu,et al.  Maximum power point tracking of a photovoltaic energy system using neural fuzzy techniques , 2009 .

[15]  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.

[16]  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).

[17]  T. Shanthi,et al.  ANFIS Controller based MPPT Control of Photovoltaic Generation System , 2013 .

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

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

[20]  Bidyadhar Subudhi,et al.  Design and real-time implementation of a new auto-tuned adaptive MPPT control for a photovoltaic system , 2015 .

[21]  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 .

[22]  Deok-Hwan Kim,et al.  Solving local minima problem with large number of hidden nodes on two-layered feed-forward artificial neural networks , 2008, Neurocomputing.

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

[24]  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..

[25]  Luis M. Fernández,et al.  ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries , 2014, IEEE Transactions on Industrial Informatics.

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

[27]  Okyay Kaynak,et al.  Identification and Control of Dynamic Plants Using Fuzzy Wavelet Neural Networks , 2008, 2008 IEEE International Symposium on Intelligent Control.

[28]  Shamsodin Taheri,et al.  An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays , 2016, 2016 IEEE Electrical Power and Energy Conference (EPEC).

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

[30]  Cheng-Jian Lin,et al.  A wavelet-based neuro-fuzzy system and its applications , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[31]  Weidong Xiao,et al.  A novel modeling method for photovoltaic cells , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).

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

[33]  Ahmed Abbou,et al.  Robust adaptive integral backstepping control for MPPT and UPF of PV system connected to the grid , 2016, 2016 7th International Renewable Energy Congress (IREC).

[34]  George Papadakis,et al.  An Intelligent MPPT controller based on direct neural control for partially shaded PV system , 2015 .

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

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

[37]  A. K. Gupta,et al.  A numerical investigation of time-fractional modified Fornberg-Whitham equation for analyzing the behavior of water waves , 2015, Appl. Math. Comput..

[38]  A. K. Gupta,et al.  An investigation with Hermite Wavelets for accurate solution of Fractional Jaulent-Miodek equation associated with energy-dependent Schrödinger potential , 2015, Appl. Math. Comput..

[39]  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 .

[40]  Xin-Jian Zhu,et al.  A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks , 2009 .

[41]  Yunjun Xu,et al.  Adaptive control theory and applications , 2012 .

[42]  Yun Peng,et al.  Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE Training Method , 2012, ISNN.

[43]  Zhengming Zhao,et al.  MPPT techniques for photovoltaic applications , 2013 .

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

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

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

[47]  Hui Li,et al.  Fuzzy embedded MPPT modeling and control of PV system in a hybrid power system , 2016, 2016 International Conference on Emerging Technologies (ICET).

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

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