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Integrated Intelligent Control and Fault System for Wind Generators


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Abstract

The goal of this paper is to show the possibility of combining fault detection analysis, detection, modeling, and control of the doubly-fed induction generator (DFIG) wind turbine using intelligent control and diagnostic techniques. To enable online detection of problems inside the power electronics converter we apply the wave direct analysis method which enables a complete model for fault detection that includes the power electronic stage itself. A neural network system based on Hebbian networks is applied for fault classification with good detection results in simulation. For controlling the wind turbine a number different artificial intelligence techniques are presented including fuzzy logic and an adaptive fuzzy inference systems (ANFIS) which combines the characteristics of fuzzy logic and neural networks. A Grey predictor is also integrated in the control scheme for predicting the wind profile. The combined fault detection and control scheme are validated using simulation results. The software development and control platform is LabVIEW which is one of the most powerful tools for simulating and implementing industrial control systems.


Keywords


Pages

Total Pages: 17
Pages: 373-389

DOI
10.1080/10798587.2013.778038


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Published

Volume: 19
Issue: 3
Year: 2013

Cite this document


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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

TWO YEAR CITATIONS PER DOCUMENT (SJR DATA): 0.993 (2018)
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."





Journal: 1995-Present


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