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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2017

26-04-2016 | New Trends in data pre-processing methods for signal and image classification

ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array

Author: Erkan Deniz

Published in: Neural Computing and Applications | Issue 10/2017

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Abstract

In this paper, artificial neural network (ANN) based on a maximum power point tracking (MPPT) algorithm is developed for a solar permanent magnet synchronous motor (PMSM) drive system used without a boost converter and batteries. The discontinuous space vector PWM technique is used to drive two-level inverter which is directly fed by three parallel-connected Kyocera KD205GX-LP PV modules. The ANN-based MPPT algorithm estimates the voltages and currents corresponding to maximum powers produced by PV array at the maximum power point (MPP) for swiftly changing situations such as solar radiance and temperature. These maximum powers are given as input signal to vector control algorithm of PMSM. The PMSM is designed by using Infolytica/MotorSolve software so that the phase-to-phase maximum value of its operating voltage is 20 V. The use of three-phase PMSM presents more efficient solutions to the trading solar systems with dc motor or induction motor. Thus, an effective solar system is achieved. The performance of developed ANN-based MPPT algorithm, designed PMSM, vector-controlled driver and solar system is analyzed by using MATLAB/SimPowerSystems blocks under the rapidly changing environmental conditions.
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Appendix
Available only for authorised users
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Metadata
Title
ANN-based MPPT algorithm for solar PMSM drive system fed by direct-connected PV array
Author
Erkan Deniz
Publication date
26-04-2016
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2326-4

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