Analysis of Dual Stator Winding Induction Generator-Based Wind Energy Conversion System Using Artificial Neural Network Maximum Power Point Tracking

N. S. D. Prakash Korlepara, E. B. Elanchezhian, Pragaspathy Subramani

Abstract


This article proposes a switched Z source DC/DC converter based dual stator winding induction generator-based wind-energy-conversion-system (WECS) using an artificial neural network (ANN) maximum power point tracking (MPPT) control technique.  Nowadays, multiphase machines are widely preferred for their increased power density, efficiency and improved reliability. In this article, a dual stator winding induction generator is proposed for WECS. A DC-DC converter plays a vital role in peak power extraction. WECS is a high voltage and high-power application that necessitates high gain. A conventional boost converter may lead to an instability issue under a higher duty ratio for high gain. Hence, in this analysis, a switched Z source DC/DC converter is posit to avoid instability, which operates with a minimum duty ratio. The proposed topology utilizes a backpropagation based neural network control approach to achieve the most accessible energy from speed of the rotor and actual power. The results are compared with a classical power signal feedback method and ANN-based MPPT with a boost DC-DC converter. Various topologies are analyzed in terms of voltage quality, power tracking, and tracking time using Matlab.

Keywords


Wind energy; MPPT; Dual stator winding induction generator; electrical engineering; Neural network

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References


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DOI (PDF): https://doi.org/10.20508/ijrer.v12i1.12759.g8411

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