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    Robust fault estimation for wind turbine energy via hybrid systems.

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    ROBUST FAULT JOURNAL 1.pdf
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    Authors
    Odofin, Sarah cc
    Bentley, Edward
    Aikhuele, Daniel
    Affiliation
    University of Derby
    Nothumbria University
    Bells University of Technology
    Issue Date
    2017-12-15
    
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    Abstract
    The rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.
    Citation
    Odofin, S. et al (2018) 'Robust fault estimation for wind turbine energy via hybrid systems', Renewable Energy, Vol. 120, pp. 289.- 299.
    Publisher
    Elsevier
    Journal
    Renewable Energy
    URI
    http://hdl.handle.net/10545/622102
    DOI
    10.1016/j.renene.2017.12.031
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0960148117312351
    Type
    Article
    Language
    en
    ISSN
    09601481
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.renene.2017.12.031
    Scopus Count
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    Institute for Innovation in Sustainable Engineering

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