Photovoltaic Fuzzy - MPPT Based Smart Battery Charger for Low Power Applications

Authors

  • Aarti S. Pawar Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Pune.,Savitribai Phule Pune University, Pune, India
  • Nilkanth B. Chopade Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Pune.,Savitribai Phule Pune University, Pune, India 1
  • Mahesh T. Kolte Department of Electronics and Telecommunication, Pimpri Chinchwad College of Engineering, Pune.,Savitribai Phule Pune University, Pune, India
  • Hrishikesh Mehta Research and Development, Aethertec InnovativeSolutionsPune, India

Keywords:

MPPT Technology, Converter Topology, Battery Charge Controller

Abstract

The advent of photovoltaic (PV) modules has revolutionized electricity generation, but their nonlinear characteristics impose constraints on achieving maximum energy output. To address this challenge, the utilization of Maximum Power Point Tracking (MPPT) techniques has become crucial to optimize power generation even in unfavorable conditions. Although MPPT-enabled battery chargers for high-power systems are readily available, there is an increasing need to develop chargers specifically designed for low-power applications. These chargers will ensure efficient power supply during emergencies, catering to the demands of various low-power scenarios. This research paper aims to create a self-contained solar PV charge controller that incorporates MPPT capabilities. The MATLAB/Simulink environment is used to simulate the circuitry, and the charge controller utilizes a buck converter setup. The goal is to accurately track and optimize the solar panel's maximum power output, which is achieved by implementing the Fuzzy MPPT technique. The effectiveness of the Fuzzy MPPT approach is compared to the Perturb and Observe (P&O) and Incremental Conductance (INC) MPPT strategies in a comparative analysis. Additionally, the battery charge controller (BCC) charges lead-acid and lithium-ion batteries in three stages. MPPT bulk charge, constant voltage (CV) absorption charge, and float charge are among the various stagesThe efficiency of the model is assessed based on its capacity to track MPPT, the effectiveness of battery charging, and the charge controller's overall performance. The results show that the Fuzzy MPPT technique demonstrates quick tracking of the PV panel's maximum power point, achieving this in less than 0.5 seconds even when subjected to variations in solar irradiation circumstances. It also accomplishes a remarkable maximum power tracking efficiency of 99.7%.

Downloads

Download data is not yet available.

References

https://www.pv-magazine.com/2023/03/22/new-global-solar-capacity-additions-hit-191-gw-in-2022-says-irena/.

B. N. Mohapatra, A. Dash, B. P. Jarika, Power Saving Solar Street Lights, International Journal of Emerging Technologies in Engineering Research, 5, 105-109 (2017).

Noridzuan Idris, Ahmad Maliki Omar, SulaimanShaari, Stand-Alone Photovoltaic Power System Applications in Malaysia, 4th International Power Engineering and Optimization Conference, (2010).

TrishanEsram, Patrick L. Chapman, Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques, IEEE Transactions on Energy Conversion, 22, 439-449 (2007).

Mohamed A. Eltawil, Zhengming Zhao, MPPT Techniques for Photovoltaic Applications, Renewable and Sustainable Energy Reviews, 25, 793-813 (2013).

Nur AtharahKamarzaman, Chee Wei Tan, A Comprehensive Review of Maximum Power Point Tracking Algorithms for Photovoltaic Systems, Renewable and Sustainable Energy Reviews, 37, 585-598 (2014).

Nabil Karami, Nazih Moubayed, Rachid Outbib, General Review and Classification of Different MPPT Techniques, Renewable and Sustainable Energy Reviews, 68, 1-18 (2017).

Mohamed A. Enany, Mohamed A. Farahat, Ahmed Nasr, Modeling and Evaluation of Main Maximum Power Point Tracking Algorithms for Photovoltaic Systems, Renewable and Sustainable Energy Reviews, 58, 1578-1586 (2016).

E. Koutroulis, K. Kalaitzakis, Novel Battery Charging Regulation System for Photovoltaic Applications, IEE Proceeding - Electric Power Applications, 151, 191-197 (2004).

Ankur Bhattacharjee, Design and Comparative Study of Three Photovoltaic Battery Charge Control Algorithms in MATLAB/Simulink Environment, International Journal of Advanced Computer Research, 2, 129-135 (2012).

M. Lokesh Reddy, P.J.R. Pavan Kumar, S. Aneel Manik Chandra, T. Sudhakar Babu, N. Raasekar, Comparative Study on Charge Controller Techniques for Solar PV System, Energy Procedia, 117, 1070-1077 (2017).

B. Sree Manju, R. Ramaprabha, B.L . Mathur, Modelling and Control of Standalone Solar Photovoltaic Charging System, International Conference on Emerging Trends in Electrical and Computer Technology (2011).

Salman Salman, Xin Al, Zhouyang Wu, Design of a P&O Algorithm based MPPT Charge Controller for a Stand-alone 200W PV System, Protection and Control of Modern Power Systems, 3, 1-8 (2018).

R. K. Agarwal, I. Hussain, and B. Singh, “LMF-based control algorithm for single stage three-phase grid integrated solar PV system,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1379–1387, Oct. 2016.

Y. Yang and H. Wen, “Adaptive perturb and observe maximum power point tracking with current predictive and decoupled power control for grid-connected photovoltaic inverters,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 2, pp. 422-432, Mar. 2019.

K. Y. Yap, H. Chua, M. J. K. Bashir et al., “Central composite design (CCD) for parameters optimization of maximum power point tracking (MPPT) by response surface methodology (RSM),” Journal of Mechanis of Continua and Mathematical Sciences, vol. 1, no. 1, pp. 259- 270, Mar. 2019.

J.-C. Kim, J.-C. Kim, and J.-S. Ko, “Optimization design and test bed of fuzzy control rule base for PV system MPPT in micro grid,” Sustainability, vol. 12, no. 9, pp. 3763-3787, May 2020.

B. Benlahbib, N. Bouarroudj, S. Mekhilef et al., “A fuzzy logic controller based on, maximum power point tracking algorithm for partially shaded PV array-experimental validation,” ElektronikaIrElektrotechnika, vol. 24, pp. 38-44, Aug. 2018.

A. Youssefa, M. E. Telbany, and A. Zekry, “Reconfigurable generic FPGA implementation of fuzzy logic controller for MPPT of PV systems,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 1313- 1319, Feb. 2018.

B. K. Naick, K. Chatterjee, and T. Chatterjee, “Fuzzy logic controller based maximum power point tracking technique for different configurations of partially shaded photovoltaic system,” Archives of Electrical Engineering, vol. 67, pp. 307-320, Aug. 2018.

S. Blaifi, S. Moulahoum, R. Benkercha et al., “M5P model tree based fast fuzzy maximum power point tracker,” Solar Energy, vol. 163, pp. 405-424, Mar. 2018.

S. D. Al-Majidi, M. F. Abbod, H. S. Al-Raweshidy et al., “A particle swarm optimisation- trained feedforward neural network for predicting the maximum power point of a photovoltaic array,” Engineering Applications of Artificial Intelligence, vol. 92, pp. 103688-103700, Jun. 2020.

J. M. Lopez-Guede, J. Ramos-Hernanz, N. Altin et al., “Neural modeling of fuzzy controllers for maximum power point tracking in photo voltaic energy systems,” Journal of Electronic Materials, vol. 47, pp. 4519-4532, Jun. 2018.

J. J. Khanam and S. Y. Foo, “Modeling of a photovoltaic array in MATLAB Simulink and maximum power point tracking using neural network,” Electrical & Electronic Technology Open Access Journal, vol. 2, no. 2, pp. 40-46, Jul. 2018.

L. Chen and X. Wang, “An enhanced MPPT Method based on ANN assisted sequential Monte Carlo and quickest change detection,” IET Smart Grid, vol. 2, no. 4, pp. 635-644, Dec.2019.

P. N. J. Lakshmi and M. R. Sindhu, “An artificial neural network based MPPT algorithm for solar PV system,” in Proceedings of 2018 4th International Conference on Electrical Energy Systems (ICEES), Chennai, India, Feb. 2019, pp. 375-380.

J. Zhang, N. Liu, J. Xu et al., “Novel MPPT method based on large variance GA-RBF,” Journal of Engineering, vol. 2019, no. 16, pp. 3365-3370, Mar. 2019.

H. D. Tafti, A. Sangwongwanich, Y. Yang et al., “A general algorithm for flexible active power control of photovoltaic systems,” in Proceedings of IEEE Applied Power Electronics Conference and Exposition (APEC), San Antonio, USA, Mar. 2018, pp. 1115-1121.

A. Harrag and S. Messalti, “Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions,” Neural Comput&Applic, vol. 30, pp. 1145-1170, Dec,2016.

A. Tian, S. Chu, J. Pan et al., “A novel pigeon-inspired optimization based MPPT technique for PV systems,” Processes, vol. 8, no. 3, pp. 356-378, Mar. 2020.

K. Anoop and M. Nandakumar, “A novel maximum power point tracking method based on particle swarm optimization combined with one cycle control,” in Proceedings of International Conference on Power, Instrumentation, Control and Computing (PICC), Thrissur, India, Jan. 2018, pp. 1-6.

N. Kalaiarasi, S. S. Dash, S. Padmanaban et al., “Maximum power point tracking implementation by dspace controller integrated through z-source inverter using particle swarm optimization technique for photovoltaic applications,” Applied Science, vol. 8, no. 1, pp. 145-162, Jan 2018.

H. Li and D. Yang, “An overall distribution particle swarm optimization MPPT algorithm for photovoltaic system under partial shading,” IEEE Transactions on Industrial Electronics, vol. 66, no. 1, pp. 265- 275, Apr. 2018.

B. R. Peng, K. C. Ho, and Y. H. Liu, “A novel and fast MPPT method suitable for both fast changing and partially shaded conditions,” IEEE Transactions on Industrial Electronics, vol. 65, no. 4, pp. 3240-3251, Aug. 2017.

L. Li, G. Lin, M. Tseng et al., “A maximum power point tracking method for PV system with improved gravitational search algorithm,” Applied Soft Computing, vol. 65, pp. 333-348, Apr. 2018.

S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, “A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems,” International Journal of Hydrogen Energy, vol. 43, pp. 14158-14171, Aug. 2018.

C. Vimalarani, N. Kamaraj, and B. C. Babu, “Improved method of maximum power point tracking of photovoltaic (PV) array using hybrid intelligent controller,” Optik, vol. 168, pp. 403-415, Sept. 2018.

A. A. Aldair, A. A. Obed, and A. F. Halihal, “Design and implementation of ANFIS- reference model controller based MPPT using FPGA for photovoltaic system,” Renewable and Sustainable Energy Reviews, vol. 82, pp. 2202-2217, Feb. 2018.

N. Priyadarshi, V. K. Ramachandaramurthy, S. P. F. Azam, “An ant colony optimized mppt for standalone hybrid PV-wind power system with single cuk converter,” Energies, vol. 12, no. 1, pp. 167-189, Jan. 2019.

K. S. Tey, S. Mekhilef, M. Seyedmahmoudian et al., “Improved differential evolution- based MPPT algorithm using SEPIC for PV systems under partial shading conditions and load variation,” IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4322-4333,Jan. 2018.

T. Pei, X. Hao, and Q. Gu, “A novel global maximum power point tracking strategy based on modified flower pollination algorithm for photovoltaic systems under non-uniform irradiation and temperature conditions,” Energies, vol. 11, pp. 2708-2723,Oct.2018.

Yap Et Al.: Artificial Intelligence Based MPPT Techniques For Solar Power System: A Review”, Journal Of Modern Power Systems And Clean Energy, Vol. 8, No. 6, November 2020.

N. S. D’Souza, L. A.C. Lopes, and X. J. Liu, “Comparative study of variable size perturbation and observation maximum power point trackers for PV systems,” Elect. Power Syst. Res., vol. 80, no. 1, pp. 296–305, 2010.

W. Xiao, M. G. J. Lind, W. G. Dunford, and A. Capel, “Real-time identification of optimal operating points in photovoltaic power systems,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1017–1026, Jun. 2006,

M. N. M. Hussain, A. M. Omar, and A. A. A. Samat, “Identification of multiple input- single output (miso) model for MPPT of photovoltaic system,” in Proc. IEEE Int. Conf. Control Syst. Comput. Eng., Penang, Malyasia, Nov. 2011, pp. 49–53.

Bidyadhar Subudhi, Raseswari Pradhan, “A New Adaptive Maximum Power Point Controller for a Photovoltaic System”, IEEE Transactions On Sustainable Energy, Vol. 10, No. 4, October 2019.

Saad Motahhir , Ayoub Aoune, Abdelaziz El Ghzizal, Souad Sebti and Aziz Derouich ,”Comparison between Kalman filter and incremental conductance algorithm for optimizing photovoltaic energy”, Renewables: Wind, Water, and Solar, 2017.

MostefaKermadi, Zainal Salam, Jubaer Ahmed, Madjid Berkouk,”An Effective Hybrid Maximum Power Point Tracker of Photovoltaic Arrays for Complex Partial Shading Conditions”, IEEE Transactions On Industrial Electronics, 2018.

F. Raziya, M. Afnaz, S. Jesudason, I. Ranaweera and H. Walpita, "MPPT Technique Based on Perturb and Observe Method for PV Systems Under Partial Shading Conditions," 2019 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2019, pp. 474-479, doi: 10.1109/MERCon.2019.8818684.

N. Kumar, I. Hussain, B. Singh and B. K. Panigrahi, "Framework of Maximum Power Extraction From Solar PV Panel Using Self Predictive Perturb and Observe Algorithm," in IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 895-903, April 2018, doi: 10.1109/TSTE.2017.2764266.

Saad Motahhir, Aboubakr El Hammoumi, Abdelaziz El Ghzizal,Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation, Energy Reports, Volume 4,2018,Pages 341-350.

Tomy Abuzairi, Wing Wira Adimas Ramadhan, Kresna Devara, "Solar Charge Controller with Maximum Power Point Tracking for Low-Power Solar Applications", International Journal of Photoenergy,Hindwai,2019.

Unal Yilmaza, Ali Kircaya,, Selim Borekcib PV system fuzzy logic MPPT method and PI control as a charge controller", Renewable and Sustainable Energy Reviews,Elsevier,2018.

P. Maithili, K. Kanakaraj,"Charge Controller Techniques, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-8,

S. J. Chiang, Hsin-Jang Shieh, Member, IEEE, and Ming-Chieh Chen,"Modeling and Control of PV Charger System With SEPIC Converter", IEEE Transactions On Industrial Electronics,2009.

Venkatramanan D, Vinod John ,"Dynamic Modeling and Analysis of Buck Converter based Solar PV Charge Controller for Improved MPPT Performance",IEEE 2019.

Joydip Jana, Konika Das Bhattacharya, Hiranmay Saha,"Design & Implementation of MPPT Algorithm for Battery Charging with Photovoltaic Panel Using FPGA", IEEE,2014.

Byamakesh Nayak, Alivarani Mohapatra, Kanungo Barada Mohanty, Selection Criteria of DC-DC Converter and Control Variable for MPPT of PV System Utilized in Heating and Cooking Applications, Cogent Engineering, 4, 1-16 (2017).

M.H Taghvaee, M.A.M. Radzi, S.M. Moosavain, Hashim Hizam, M. HamiruceMarhaban, A Current and Future Study on non-isolated DC-DC Converters for Photovoltaic Applications. Renewable and Sustainable Energy Reviews, 17, 216-227 (2013).

Illan Glasner, Joseph Appelbaum, Advantage of Boost vs Buck Topology for Maximum Power Point Tracker in Photovoltaic Systems, IEEE Proceedings of 19th Convention of Electrical and Electronic.

M, V. ., P U, P. M. ., M, T. ., & Lopez, D. . (2023). XDLX: A Memory-Efficient Solution for Backtracking Applications in Big Data Environment using XOR-based Dancing Links. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 88–94. https://doi.org/10.17762/ijritcc.v11i1.6054

Rodriguez, L., Rodríguez, D., Martinez, J., Perez, A., & Ólafur, J. Leveraging Machine Learning for Adaptive Learning Systems in Engineering Education. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/103

Dhanikonda, S.R., Sowjanya, P., Ramanaiah, M.L., Joshi, R., Krishna Mohan, B.H., Dhabliya, D., Raja, N.K. An Efficient Deep Learning Model with Interrelated Tagging Prototype with Segmentation for Telugu Optical Character Recognition(2022) Scientific Programming, 2022, art. no. 1059004

Downloads

Published

27.10.2023

How to Cite

Pawar, A. S. ., Chopade, N. B. ., Kolte, M. T. ., & Mehta, H. . (2023). Photovoltaic Fuzzy - MPPT Based Smart Battery Charger for Low Power Applications. International Journal of Intelligent Systems and Applications in Engineering, 12(2s), 140–162. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3566

Issue

Section

Research Article