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Maximum Power Point Tracking Algorithms for Partial Shaded PV Systems

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Energy Harvesting and Energy Efficiency

Part of the book series: Lecture Notes in Energy ((LNEN,volume 37))

Abstract

Photovoltaic modules have nonlinear current-voltage (I-V) characteristics. Thus, output power of the photovoltaic module varies with module specification and its operation point. It means that, the photovoltaic system generates maximum power at a single operation point for an environmental condition such as the irradiation level and angle, ambient temperature level etc. In addition, energy conversion efficiency varies with load level and operation point of the photovoltaic system. Since these parameters are variable, operation point of the photovoltaic system should be controlled to get maximum output power and maximum energy conversion efficiency. This action is called as maximum power point tracking. The maximum power point tracking action is usually performed with a power electronics converter. A number of maximum power point tracking methods have been introduced to obtain fast response, especially in rapidly-changing atmospheric conditions, low oscillation and higher energy conversion efficiency values. However, most of these methods are effective for uniform solar irradiation conditions. If the solar irradiation is non-uniform, the power-voltage (P-V) curve of the photovoltaic module or array has multiple peak points: Several local maximum power points and one global maximum point. In this case, traditional maximum power point tracking methods determine the nearest peak power point, which may be a local maximum point. Thus, some improved maximum power point tracking methods have been proposed to determine the global maximum power point of the photovoltaic system even under partial shading conditions. A discussion on different maximum power point tracking methods for the solution of these problems will be given and the most powerful techniques in the literature will be outlined.

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Abbreviations

ACA:

Ant colony algorithm

ANN:

Artificial Neural Network

CC:

Constant current

CST:

Current sweep technique

CV:

Constant voltage

FLC:

Fuzzy logic control

GP:

Global peak

IC:

Incremental conductance

LP:

Local peak

MPP:

Maximum power point

MPPT:

Maximum power point tracking

PC:

Pilot cell

P&O:

Perturb and observe

RCC:

Ripple correlation control

PSC:

Partially shading condition

PSO:

Particle swarm optimization

PV:

Photovoltaic

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey

    Ibrahim Sefa & Necmi Altin

  2. Vocational School of Technical Sciences, Gazi University, Ostim, Ankara, Turkey

    Saban Ozdemir

Authors
  1. Ibrahim Sefa
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  2. Necmi Altin
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  3. Saban Ozdemir
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Corresponding author

Correspondence to Necmi Altin .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Communication, and Computers, University of PiteÅŸti, PiteÅŸti, Romania

    Nicu Bizon

  2. Electrical Engineering Department, Faculty of Engineering, Seraj Higher Education Institute, Tabriz, Iran

    Naser Mahdavi Tabatabaei

  3. Department of Energy Technology, Aalborg University, Aalborg East, Denmark

    Frede Blaabjerg

  4. Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey

    Erol Kurt

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Sefa, I., Altin, N., Ozdemir, S. (2017). Maximum Power Point Tracking Algorithms for Partial Shaded PV Systems. In: Bizon, N., Mahdavi Tabatabaei, N., Blaabjerg, F., Kurt, E. (eds) Energy Harvesting and Energy Efficiency. Lecture Notes in Energy, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-49875-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-49875-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49874-4

  • Online ISBN: 978-3-319-49875-1

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