Modeling and control of a low power wind turbine
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CIUPAGEANU, Dana Alexandra, LAZAROIU, Gheorghe, BERBECE, Viorel, TÎRŞU, Mihai, GALBURÂ, Victor. Modeling and control of a low power wind turbine. In: 2018 14th International Conference on Development and Application Systems: DAS 2018 - Proceedings, 24-26 mai 2018, Suceava. New Jersey, SUA: Institute of Electrical and Electronics Engineers Inc., 2018, Ediția a 14-a, pp. 26-30. ISBN 978-153861495-2. DOI: https://doi.org/10.1109/DAAS.2018.8396065
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2018 14th International Conference on Development and Application Systems
Ediția a 14-a, 2018
Conferința "14th International Conference on Development and Application Systems"
Suceava, Romania, 24-26 mai 2018

Modeling and control of a low power wind turbine

DOI:https://doi.org/10.1109/DAAS.2018.8396065

Pag. 26-30

Ciupageanu Dana Alexandra1, Lazaroiu Gheorghe1, Berbece Viorel1, Tîrşu Mihai2, Galburâ Victor2
 
1 University Politehnica of Bucharest,
2 Institute of Power Engineering
 
 
Disponibil în IBN: 22 iulie 2021


Rezumat

The unpredictability characterizing renewable energy sources severely impacts power systems as their share continuously enlarges. Choosing the right technology to exploit the available potential of the intended resource and implementing appropriate plant control represents a solution that improves system's operating conditions. This paper presents simulations results for a Permanent Magnet Synchronous Generator (PMSG) driven by a Wind Turbine (WT) considering a control strategy employing a Neural Network Predictive Controller (NNPC), as one of the most viable among suitable options for rising wind energy share while mitigating its highly stochastic characteristics. The model system was represented in Matlab programming environment, using Simulink/Simscape/Power Systems blocks. The behavior of the system is analyzed both in steady and dynamic conditions, in the hypothesis that the controller provides the torque reference for the generator. 



Cuvinte-cheie
low-power wind turbine, Matlab/Simulink, neural network predictive control, permanent magnet synchronous generator, wind energy