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Simulation Study of Fault Detection and Diagnosis for Wind Turbine System

Sarah Odofin, Sun Kai, Zhiwei Gao, Zabih Ghassemlooy

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    This paper presents a quantitative review of early detection and estimation of fault for wind turbine system. The model-based technique is proposed to provide an assessment of all possible faults for renewable energy sources. The augmented observer is applied to estimate the real physical data of the system sensor faults and states simultaneously. The fault detection and diagnosis is designed to be most sensitive to faults and states of wind turbine system. A mathematical example is specified to exhibit the dynamic system behavior for the wind turbine model to validate the competence of the system performance. The state space is used to explain the design. The satisfactory fault diagnosis performance is demonstrated in the simulation results.
    Original languageEnglish
    Publication statusPublished - 23 Jun 2014
    Event15th Annual Postgraduate Symposium on the Convergence of Telecommunications, Network and Broadcasting - Liverpool, UK
    Duration: 23 Jun 2014 → …

    Conference

    Conference15th Annual Postgraduate Symposium on the Convergence of Telecommunications, Network and Broadcasting
    Period23/06/14 → …

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 7 - Affordable and Clean Energy
      SDG 7 Affordable and Clean Energy

    Keywords

    • Condition monitoring
    • fault detection
    • fault diagnosis
    • augmented observer
    • fault estimation
    • wind turbine

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