Zhu, Jiangsheng



PROJECT TITLE: Reliability and Condition Monitoring of Wind Turbine Drivetrain  


PhD period: 2015.12.01 – 2018.11.30.
Section: Esbjerg Energy Section
Research Programmes: Wind Power Systems and Offshore Energy Systems
Supervisor: Mohsen Soltani
Co-Supervisor: Zhe Chen  
Contact Information

Collaborator: HUAREN Wind Power Company.
Funding: Scholarship from China Scholarship Council.

ABSTRACT

As the demand for wind energy continues to grow at exponential rates, increasing reliability and reducing operation and maintenance costs is now a top priority. Aside from developing more advanced wind turbines designs to improve the availability, an effective way to achieve this improvement is to apply reliable and cost-effective condition monitoring techniques.  Reliability is the ability of a device to perform required functions under given conditions, for a given time. The reliability of a wind turbine is critical to extracting the maximum energy available from the wind. It can be highly improved by the implementation of condition monitoring system (CMS).

This research presents a reliability analysis model of wind turbine drive train by developing a generic drive train configuration and modular structure. A wind turbine drive train is made up of a torque/speed conversion step (e.g., a gearbox), a mechanical to electrical conversion step (e.g., an induction generator), and an electrical power conversion step (e.g. a fully rated converter).  Reliability block diagrams of drive train modules and components will be established by using Reliability Analysis software. Failure rates of the critical components are estimated by applying existing industrial standards and datasheets for general mechanical applications. To improve the reliability prediction of wind turbine drive train, an advanced prediction model based on failure modes and load carrying capability of individual components under operational conditions will be analyzed. This research will highlight the importance of validation of the reliability prediction models using available field failure data. Our failure data is collected from ten-minute Supervisory control and data acquisition (SCADA) database, automated fault logs, O&M reports, and supplemented with data from references.

Some methods will be used in the research such as Failure mode and effects analysis (FMEA) and Fault tree analysis (FTA). FMEA has been extensively used for analyzing, evaluating and prioritizing potential/known failure modes. FTA is also used to describe the complete set of potential system failures and their propagation into different system levels. The FTA is one of the most popular and systematic technique to analyze the undesired states of a system.

PAPERS

Publications in journals and conference papers may be found at VBN.