Modeling and Simulation of a Hybrid Solar-Wind System with Adaptive Neural Network MPPT Control in MATLAB/SIMULINK

Authors

  • Devendra Kumar Verma Student, Department of Electrical & Electronics Engineering, Kamla Nehru Institute of Technology, Sultanpur-228159, Uttar Pradesh, India
  • Varun Kumar Professor, Department of Electrical & Electronics Engineering, Kamla Nehru Institute of Technology, Sultanpur-228118, Uttar Pradesh, India
  • Y. K. Chauhan Professor, Department of Electrical & Electronics Engineering, Kamla Nehru Institute of Technology, Sultanpur-228118, Uttar Pradesh, India

DOI:

https://doi.org/10.37591/.v13i2.7586

Abstract

Over the last ten years, there has been an increasing demand for the electrical power
supply. The installation of power generators (PGs) is expensive and time-consuming. Solar power
plants are therefore thought to be a practical substitute for supplying the present demand for
electricity. The primary challenges with solar plants, however, are output power balancing and
critical maintenance. A proper technique is needed to lessen output power balance and
maintenance issues in solar facilities. For hybrid photovoltaic (PV) and wind energy systems
(WES), this research suggests a novel single maximum power point tracking (MPPT) technique to
track maximum power. An artificial neural network (ANN) serves as the foundation for the
recommended MPPT method. A source inverter and a separate converter are used to link the
hybrid PV and WES systems to the grid.

References

Inthamoussou, F.A.; Battista, H.D.; Mantz, R.J. New concept in maximum

power tracking for the control of a photovoltaic/hydrogen system. Int. J. Hydrog.

Energy 2012, 37, 14951–14958.

Femia, N.; Petrone, G.; Spagnuolo, G.; Vitelli, M. A technique for improving P & O

MPPT performances of double-stage grid-connected photovoltaic systems. IEEE Trans.

Ind. Electron. 2009, 56, 4473–4482.

Munir, H.K.; Nur, S.M.; Ahmed, E.-S. Wavelet based hybrid ANN-ARIMA models for

meteorological drought forecasting. J.Hydrol. 2020, 590, 125380.

Ruiz-Aguilar, J.J.; Turias, I.; González-Enrique, J. A permutation entropy-based EMD–

ANN forecasting ensemble approach for wind speed prediction. Neural Comput. Appl.

, 33, 2369–2391.

. Akbal, Y.; Ünlü, K.D. A deep learning approach to model daily particular matter

ofAnkara: Keyfeatures and forecasting. Int. J. Environ. Sci. Technol. 2021.

Abdul, R.P.; Damhuji, R.; Kharudin, A.; Muhammad, Z.M.; Ahmed, N.A.; Moneer,

A.F. Solar irradiance measurement instrumen- tation and power solar generation

forecasting based on Artificial Neural Networks (ANN): A review of five years

research trend. Sci. Total Environ. 2020, 715, 136848.

Esram, T.; Chapman, P.L. Comparison of photovoltaic array maximum power point

tracking techniques. IEEE Trans. Energy Convers. 2007, 22, 439–449.

Femia, G.N.; Petrone, G.; Spagnuolo, G.; Vitelli, M. Optimization of perturb and

observe maximum power point tracking method. IEEE Trans. Power Electron. 2005,

, 963–973.

Li, G.; Wang, H.A. Novel stand-alone PV generation system based on variable step size

INC MPPT and SVPWM control. In Proceedings of the IEEE 6th International Power

Electronics and Motion Control Conference, IEEE-IPEMC’09, Wuhan, China, 17–20

May 2009; p. 2155e60.

Safari, A.; Mekhilef, S. Simulation and hardware implementation of incremental

conductance MPPT with direct control method using cuk converter. IEEE Trans. Ind.

Electron. 2011, 58, 1154–11561.

Reisi, A.R.; Moradi, M.H.; Jamasb, S. Classification and comparison of maximum

power point tracking techniques for photovoltaic system: A review. Renew. Sustain.

Energy Rev. 2013, 19, 433–443.

Xiao, W.; Dunford, W.G. A modified adaptive hill climbing MPPT method for

photovoltaic power systems. In Proceedings of the 35th Annual IEEE Power

Electronics Specialists Conference, Aachen, Germany, 20–25 June 2004; pp. 1957–

Liu, F.; Kang, Y.; Zhang, Y.; Duan, S. Comparison of P & O and hill climbing

MPPT methods for grid-connected PV converter. In Proceedings of the 3rd IEEE

Conference on Industrial Electronics and Applications, Singapore, 3–5 June 2008; pp.

–807.

Mutoh, N.; Matuo, T.; Okada, K.; Sakai, M. Prediction-databased maximum-power-

point tracking method for photovoltaic power generation systems. In Proceedings of the

IEEE 33rd Annu. Power Electronics Specialists Conference, Cairns, QLD, Australia,

–27 June 2002; pp. 1489–1494.

Chao, K.H.; Li, C.J.; Wang, M.H. A Maximum Power Point Tracking Method Based

on Extension Neural Network for PV Systems [Part I, LNCS 5551]; Springer: Wuhan,

China, 2009; pp. 745–755.

Yasushi, K.; Koichiro, Y.; Masahito, K. Quick Maximum Power Point Tracking of

Photovoltaic Using Online Learning Neural Network. In Proceedings of the

International Conference on Neural Information Processing ICONIP 2009: Neural

Information Processing, Bangkok, Thailand, 1–5 December 2009; pp. 606–613.

Majed, B.A.; Maher, C.; Zied, C. Artificial Neural Network based control for PV/T

panel to track optimum thermal and electrical power. Energy Convers. Manag. 2013,

, 372–380.

Shahzad, A.; Hafiz, M.; Muhammad, A. Iftikhar, A.; Muhammad, K.A.; Zil, H.; Safdar,

A.K. Supertwisting Sliding Mode Algorithm Based Nonlinear MPPT Control for a

Solar PV System with Artificial Neural Networks Based Reference Generation.

Energies 2020, 13, 3695.

Liu, Y.H.; Liu, C.L.; Huang, J.W.; Chen, J.H. Neural-network-based maximum power

point tracking methods for photovoltaic systems operating under fast changing

environments. Sol. Energy 2013, 89, 42–53.

Downloads

Published

2024-01-16

Issue

Vol. 13 No. 2 (2023)

Section

Research Article

License

Journal Title:

Title of the Paper:

Corresponding Author’s Information:

Name: Address:

E-mail:

Contact Number:

It is herein agreed that: The copyright to the above-listed unpublished and original article is transferred to STM Journals.

This copyright transfer covers the exclusive right to reproduce and distribute the contribution, including reprints, translations, photographic reproductions, microform, electronic form (offline, online), or any other reproductions of similar nature.

I/We declare that above manuscript is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

 I/ We warrant(s) that his/her/their contribution is original, except for such excerpts from copyrighted works as may be included with the permission of the copyright holder and author thereof, that it contains no libelous statements, and does not infringe on any copyright, trademark, patent, statutory right, or propriety right of others.

I/We will not publish his/her/their above said contribution anywhere else without the prior written permission of the publisher unless it has been changed substantially. I/We also agree to the authorship of the article in the following order: Author(s) Name Signature(s)

1. ________________

2. ________________

3. ________________

4. ________________

The author(s) agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal (comments if any to the editor can be added below).