There is a newer version of the record available.

Published November 19, 2025 | Version v1
Journal article Restricted

Performance Evaluation of P&O, ANN and ANFIS Based MPPT Controllers of PV Array Under Partial Shading Effect in South Pole and Leading Solar Sites

Authors/Creators

  • 1. ROR icon Istanbul Aydın University

Description

The full-text file of the published paper is available at:[https://www.researchgate.net/publication/398734195_Performance_Evaluation_of_P_and_O_ANN_and_ANFIS_Based_MPPT_Controllers_of_PV_Array_Under_Partial_Shading_Effect_in_South_Pole_and_Leading_Solar_Sites]

 

Fuel oil is a primary source used by the research stations located at Antarctica. However, shipping fuel oil to this remote region is costly and harms the environment. Due to global warming, it has become a necessity to implement green energy technologies. Due to the nonlinear nature of PV cells and their dependence on solar irradiation and temperature, obtaining maximum power is a challenge. The system must be optimized to obtain the maximum power using Power Electronics. One way of achieving this is called Maximum Power Point Tracking (MPPT). Our research evaluates the performance of conventional method, Perturb and Observe (P&O) based MPPT technique, with modern techniques, Artificial Neural Network (ANN) and Adaptive neuro-fuzzy inference system (ANFIS) based MPPT techniques. Simulations of the ANN, ANFIS and P&O algorithms were carried out using MATLAB-Simulink. The comparison between these controllers is made considering the partial shading and their performance at different temperatures at the leading solar sites and South pole. Results indicate that there is an improvement in MPP tracking for both ANN and ANFIS controllers as compared to the P&O algorithm with respect to the settling time, overshoot, oscillations and time to achieve MPP at both environments.

Files

Restricted

The record is publicly accessible, but files are restricted. Log in to check if you have access.

Additional details

References

  • T. Tin, B. K. Sovacool, D. Blake, P. Magill, S. El Naggar, S. Lidstrom, and M. Ishizawa, "Energy efficiency and renewable energy under extreme conditions: Case studies from Antarctica," Renew. Energy, vol. 35, no. 8, pp. 1715–1723, Aug. 2010
  • A. D. Hemmings, "Why fuel matters: Energy needs in Antarctic systems," Polar Rec., vol. 47, no. 4, pp. 374–385, 2011
  • P. Guderian and C. Kleinert, "Renewable energy for polar regions: A feasibility study," Cold Reg. Sci. Technol., vol. 31, no. 3, pp. 231–240, 2000
  • R. Roura, "The footprint of Antarctic tourism: Policy changes and long-term management implications," Polar Res., vol. 31, no. 1, p. 108, 2012
  • S. Brunet and D. Hourcard, "Renewable energy for polar research stations: An innovative strategy," Renew. Energy Syst. Rev., vol. 24, no. 2, pp. 78–87, 2008
  • National Science Foundation, U.S. Antarctic Program: Final Environmental Impact Statement, Washington, DC:NSF, 1996
  • L. Pyle, "South Pole Station: A history and overview," Geophys. Monogr. Ser., 1999
  • H. van Loon, "Temperature fluctuations in the Antarctic," Mon. Weather Rev., vol. 95, no. 12, pp. 701–705, 1967
  • J. Blunden and D. S. Arndt, "State of the climate in 2015," Bull. Amer. Meteorol. Soc., vol. 97, no. 8, 2016
  • Amundsen-Scott South Pole Station – RESPEC. Available: https://www.respec.com/project/amundson-scott-south-pole-station/.[Accessed: Jan. 3, 2025]
  • B. Lazzarini, R. Gambelli, and M. Cerratti, "Solar and wind systems for Antarctic conditions," Appl. Energy, vol. 102, pp. 892–901, 2012
  • NASA Surface Meteorology and Solar Energy. Available: http://eosweb.larc.nasa.gov/sse. [Accessed: Dec. 29, 2024]
  • S. R. Wenham and M. A. Green, "Solar energy technologies: Advanced applications in polar climates," Prog. Photovolt., vol. 6, no. 5, pp. 379–385, 1998
  • A. Waleed et al., "Study on hybrid wind-solar system for energy saving analysis in energy sector," in Proc. 3rd Int. Conf. Comput. Math. Eng. Technol. (iCoMET), 2020, pp. 1–6
  • S. D. Al-Majidi, M. F. Abbod, and H. S. Al-Raweshidy, "A modified P&O-MPPT based on Pythagorean theorem and CV-MPPT for PV systems," in Proc. 53rd Int. Univ. Power Eng. Conf. (UPEC), 2018, pp. 1–6
  • A. Waleed, M. T. Riaz, M. F. Muneer, M. A. Ahmad, A. Mughal, M. A. Zafar, et al., "Solar (PV) water irrigation system with wireless control," in Proc. 2019 Int. Symp. Recent Adv. Electr. Eng. (RAEE), 2019, pp. 1–4
  • G. K. Singh, "Solar power generation by PV (photovoltaic) technology: A review," Energy, vol. 53, pp. 1–13, 2013
  • M. A. Enany, M. A. Farahat, and A. Nasr, "Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems," Renew. Sustain. Energy Rev., vol. 58, pp. 1578–1586, 2016
  • M. S. Ngan and C. W. Tan, "A study of maximum power point tracking algorithms for stand-alone photovoltaic systems," in Proc. IEEE Appl. Power Electron. Colloq. (IAPEC), 2011, pp. 22–27
  • T. Esram and P. L. Chapman, "Comparison of photovoltaic array maximum power point tracking techniques," IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 439–449, Jun. 2007
  • R. Reisi, M. H. Moradi, and S. Jamasb, "Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review," Renew. Sustain. Energy Rev., vol. 19, pp. 433–443, Mar. 2013
  • M. A. A. M. Zainuri et al., "Adaptive P&O-fuzzy control MPPT for PV boost DC-DC converter," in Proc. IEEE Int. Conf. Power Energy (PECon), 2012, pp. 2–5
  • R. Ramaprabha and B. L. Mathur, "Intelligent controller-based maximum power point tracking for solar PV system," Int. J. Comput. Appl., vol. 12, no. 10, pp. 37–42, Dec. 2011
  • P. Q. Dzung, "The new MPPT algorithm using ANN-based PV," in Proc. Int. Forum Strategic Technol., 2010, pp. 402–407
  • L. Jie and C. Ziran, "Research on the MPPT algorithms of photovoltaic system based on PV neural network," in Proc. Chin. Control Decis. Conf. (CCDC), 2011, pp. 1851–1854
  • S. Premrudeepreechacharn and N. Patanapirom, "Solar-array modeling and maximum power point tracking using neural networks," in Proc. IEEE Bologna Power Tech Conf., 2003, vol. 2, no. 3, pp. 419–423
  • S. E. K. T. Hiyama, "Artificial neural network-polar coordinated fuzzy controller-based maximum power point tracking control under partially shaded conditions," Renew. Power Gener., vol. 3, no. 2, pp. 239–253, 2009
  • N. Femia et al., "Optimization of perturb and observe maximum power point tracking method," IEEE Trans. Power Electron., vol. 20, no. 4, pp. 963–973, Jul. 2005
  • M. a. G. de Brito et al., "Main maximum power point tracking strategies intended for photovoltaics," in Proc. XI Brazilian Power Electron. Conf., 2011, pp. 524–530
  • M. Negnevitsky, Artificial Intelligence, 2nd ed. Hoboken, NJ: Pearson, 2005, pp. 1–415
  • J. Xu et al., "ANN based on IncCond algorithm for MPP tracker," in Proc. 6th Int. Conf. Bio-Inspired Comput. Theor. Appl., 2011, vol. 3, pp. 129–134
  • A. M. Z. Alabedin et al., "Maximum power point tracking for photovoltaic systems using fuzzy logic and artificial neural networks," in Proc. IEEE Power Energy Soc. Gen. Meeting, 2011, pp. 1–9
  • D. Mlakić and S. Nikolovski, "ANFIS as a method for determining MPPT in the photovoltaic system simulated in MATLAB/Simulink," in Proc. 39th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. (MIPRO), 2016, pp. 1082–1086
  • F. Khosrojerdi, S. Taheri, and A. Cretu, "An adaptive neuro-fuzzy inference system-based MPPT controller for photovoltaic arrays," in Proc. IEEE Electr. Power Energy Conf. (EPEC), 2016, pp. 1–6
  • F. Belhachat and C. Larbes, "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renew. Sustain. Energy Rev., vol. 77, pp. 875–889, 2017