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Power Extraction from PV Module Using Hybrid ANFIS Controller

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Solar Photovoltaic Power Plants

Part of the book series: Power Systems ((POWSYS))

Abstract

The characteristic of PV module is nonlinear, complex in nature and its performance depends on different environmental factors. In order to enhance the efficiency of photovoltaic power system, selection of a suitable power converters and control strategies are essential. In this chapter, the performance of soft-computing techniques of MPPT such as ANN and Hybrid-ANFIS are compared with well established Modified Incremental Conductance method under load and solar irradiance change. The ANFIS is able to exploit both data and knowledge to formulate more efficient hybrid intelligent system. It learns the information from experimental data and automatically determines the best membership parameters and rule bases associated to Fuzzy Inference System (FIS) to map given input output data. In this chapter, the parameters of FIS are tuned by Back-Propagation (BP) or hybrid (combination of Least Square Estimation and BP) method. Also, the effect of load impedance and converter topologies on ANFIS controller design has been investigated. Further, the detailed description of hardware implementation of ANFIS controller on DSP/FPGA platform has been presented.

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References

  1. Catalão JPS (2015) Smart and sustainable power systems: operations, planning, and economics of insular electricity grids. CRC Press, Boca Raton

    Book  Google Scholar 

  2. Committee RS (2016) Renewables 2016 global status report, REN21, renewable energy policy network for the 21st century, Athens

    Google Scholar 

  3. Panagiotis Karampelas LE (2016) Electrical distribution-intelligent solution for electricity transmission and distribution networks, 2016th edn. Springer, Berlin

    Google Scholar 

  4. Charles JP, Pierre M (1981) A practical method of analysis of the current-voltage characteristics of solar cells. Sol Cells 4:169–178

    Article  Google Scholar 

  5. Kok BC, Goh HH, Chua HG (2012) Optimal power tracker for stand-alone photovoltaic system using artificial neural network (ANN) and particle swarm optimisation (PSO). In: International conference on renewable energies and power quality, pp 1–6

    Google Scholar 

  6. Kulaksiz AA, Akkaya R (2012) Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system. Turk J Elec Eng Comp Sci 20(2):241–254

    Google Scholar 

  7. Rizzo SA, Scelba G (2015) ANN based MPPT methods for rapidly variable shading conditions. Appl Energy 145:124–132

    Article  Google Scholar 

  8. Negnevisky M (2005) Artificial intelligence: a guide to intelligent systems, 2nd edn. Pearson, Delhi

    Google Scholar 

  9. Rekioua D, Matagne E (2012) Modeling of solar irradiance and cells. Optimization of photovoltaic power systems, 2012th edn. Springer, London, pp 31–87

    Chapter  Google Scholar 

  10. Sumathi S, Ashok KL, Surekha P (2015) Application of MATLAB/SIMULINK in solar PV systems. Solar PV and wind energy conversion systems, vol XXIV. Springer, Cham, pp 59–143

    Google Scholar 

  11. Kumari JS, Babu CS (2012) Mathematical modeling and simulation of photovoltaic cell using Matlab-Simulink environment. Int J Electr Comput Eng 2(1):26–34

    Google Scholar 

  12. el Tayyan AA (2013) A simple method to extract the parameters of the single-diode model of a PV system. Turk J Phys 37:121–131

    Google Scholar 

  13. King DL, Kratochvil JA, Boyson WE (1997) Temperature coefficients for PV modules and arrays: measurement methods, difficulties, and results. In: 26th IEEE photovoltaic specialists conference

    Google Scholar 

  14. Mohamed MA (2015) Solar irradiance estimation of photovoltaic module based on Thevenin equivalent circuit model. Int J Renew Energy Res 5(4):971–972

    Google Scholar 

  15. Haihong B, Weiping Z, Bing C (2016) Control simulation and experimental verification of maximum power point tracking based on RT-LAB. Int J Eng 29(10):1372–1379

    Google Scholar 

  16. Karanjkar DS, Chatterji S, Kumar A, Shimi SL (2014) Fuzzy adaptive proportional-integral-derivative controller with dynamic set-point adjustment for maximum power point tracking in solar photovoltaic system. Taylor Fr. Jr. Syst Sci Control Eng 2583

    Google Scholar 

  17. Haque A (2014) Maximum power point tracking (MPPT) scheme for solar photovoltaic system. Energy Technol Policy 1(1):115–122

    Article  Google Scholar 

  18. Hua C, Lin J, Tzou H (2003) MPP control of a photovoltaic energy system. Eur Trans Electr Power 13(4):239–246

    Article  Google Scholar 

  19. Taghvaee MH, Radzi MAM, Moosavain SM, Hizam H, Marhaban MH (2013) A current and future study on non-isolated DC-DC converters for photovoltaic applications. Renew Sustain Energy Rev 17:216–227

    Article  Google Scholar 

  20. Walker GR, Sernia PC (2004) Cascaded DC-DC converter connection of photovoltaic modules. IEEE Trans Power Electron 19(4):1130–1139

    Article  Google Scholar 

  21. Radjai T, Gaubert JP, Rahmani L, Mekhilef S (2015) Experimental verification of P&O MPPT algorithm with direct control based on fuzzy logic control using CUK converter. Int Trans Electr Energy Syst 25(12):3492–3508

    Article  Google Scholar 

  22. Dixit TV (2018) Real time investigation on performance enhancement of power conditioing units for photo-voltic and fuel cell, NIT Raipur, (C.G.) India

    Google Scholar 

  23. Enrique JM, Duran E, Sidrach-de-Cardona M, Andujar JM (2007) Theoretical assessment of the maximum power point tracking efficiency of photovoltaic facilities with different converter topologies. Sol Energy 81:31–38

    Article  Google Scholar 

  24. Ahmed J, Salam Z (2016) A modified P & O maximum power point tracking method with reduced steady-state oscillation and improved tracking efficiency. IEEE Trans Sustain Energy 7(4):1506–1515

    Article  Google Scholar 

  25. Kok ST, Mekhilef S (2014) Modified incremental conductance algorithm for photovoltaic system under partial shading conditions and load variation. IEEE Trans Ind Electron 61(10):5384–5392

    Article  Google Scholar 

  26. Chekired F, Mellit A, Kalogirou SA, Larbes C (2014) Intelligent maximum power point trackers for photovoltaic applications using FPGA chip: a comparative study. Sol Energy 101:83–99

    Article  Google Scholar 

  27. Safari A, Mekhilef S (2011) Simulation and Hardware implementation of incremental conductance MPPT with direct control method using cuk converter. IEEE Trans Ind Electron 58(4):1154–1161

    Article  Google Scholar 

  28. Thangavelu A, Vairakannu S, Parvathyshankar D (2017) Linear open circuit voltage variable step-size incremental conductance strategy-based hybrid MPPT controller for remote power applications. IET Power Electron 10(11):1363–1376

    Article  Google Scholar 

  29. Zheng H, Li S (2016) Fast and robust maximum power point tracking for solar photovoltaic systems. Am J Eng Appl Sci 9:755–769

    Article  Google Scholar 

  30. Liao T, Huang N (1999) Genetic algorithm-based self-learning fuzzy pi controller for buck converter. Eur Trans Electr Power 9(4):233–239

    Article  Google Scholar 

  31. Jang JR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  32. Rezvani A, Izadbakhsh M, Gandomkar M, Vafaei S (2015) Implementing GA-ANFIS for maximum power point tracking in pv system. Indian J Sci Technol 8(May):982–991

    Article  Google Scholar 

  33. Sheraz MAAM, Muhammed K (2015) An efficient ANFIS-based pi controller for maximum power point tracking of pv systems. Springer Trans Arab J Sci Eng 40:2641–2651

    Article  Google Scholar 

  34. Tarek B, Said D, Benbouzid MEH (2013) Maximum power point tracking control for photovoltaic sys-tem using adaptive neuro- fuzzy ’ANFIS. In: Eighth international conference and exhibition on ecologi-cal vehicles and renewable energies, pp. 1–7

    Google Scholar 

  35. Acar M, Avci D (2010) An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst Appl 37:7908–7912

    Article  Google Scholar 

  36. Ecosense, FOUR CHANNEL SOLAR PV EMULATOR:USER, IGE-PV4C400-001/Power: 1600 W (400 \(\times \) 4)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Goverment Polytechnic Kondagaon (C.G.), Jagdalpur, India

    Tata Venkat Dixit

  2. Department of Electrical Engineering, National Institute of Technology, Raipur, India

    Anamika Yadav & S. Gupta

  3. Faculty of Engineering, Electrical Power and Machines Department, Ain Shams University, Cairo, Egypt

    Almoataz Y. Abdelaziz

Authors
  1. Tata Venkat Dixit
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  2. Anamika Yadav
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  3. S. Gupta
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  4. Almoataz Y. Abdelaziz
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Corresponding author

Correspondence to Almoataz Y. Abdelaziz .

Editor information

Editors and Affiliations

  1. Faculty of Automation and Computers, Politehnica University of Timișoara, Timișoara, Romania

    Radu-Emil Precup

  2. Department of Electrical and Electronics Engineering, Sakarya University, Serdivan/Sakarya, Turkey

    Tariq Kamal

  3. School of Electrical Engineering, Chongqing University, Chongqing, China

    Syed Zulqadar Hassan

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Dixit, T.V., Yadav, A., Gupta, S., Abdelaziz, A.Y. (2019). Power Extraction from PV Module Using Hybrid ANFIS Controller. In: Precup, RE., Kamal, T., Zulqadar Hassan, S. (eds) Solar Photovoltaic Power Plants. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-6151-7_10

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  • DOI: https://doi.org/10.1007/978-981-13-6151-7_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6150-0

  • Online ISBN: 978-981-13-6151-7

  • eBook Packages: EnergyEnergy (R0)

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