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A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

  • Kuei-Hsiang Chao
  • Ching-Ju Li
  • Meng-Huei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5551)

Abstract

In this paper, a maximum power point tracking (MPPT) technique based on extension neural network (ENN) was proposed to make full utilization of photovoltaic (PV) array output power which depends on solar insolation and ambient temperature. The proposed ENN MPPT algorithm can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed step size perturbation and observation (P&O) and incremental conductance (INC) methods, the presented method is able to effectively improve the dynamic response and steady state performance of the PV systems simultaneously. A theoretical analysis and the designed principle of the proposed method are described in detail. And some simulation results are made to demonstrate the effectiveness of the proposed MPPT method.

Keywords

Maximum power point tracking (MPPT) Perturbation and observation (P&O) method Incremental conductance (INC) method Photovoltaic (PV) system Extension neural network (ENN) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Kuei-Hsiang Chao
    • 1
  • Ching-Ju Li
    • 1
  • Meng-Huei Wang
    • 1
  1. 1.Department of Electrical EngineeringNational Chin-Yi University of TechnologyTaichungTaiwan, R.O.C.

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