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 → …

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