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Fault Detection of Offshore Wind Turbines Using Data Driven Methods

Salari, Soorena | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56613 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Sadati, Nasser
  7. Abstract:
  8. Technology systems are vulnerable to various defects. Acuator failures reduce the performance of control systems and even they can damage the entire system. Incorrect reading from the sensors is the cause of many inefficiencies, because errors can reduce the quality and efficiency of a product line. In addition, failure of a single system's component can cause a system failure. Electromechanical energy conversion devices are composed of hundreds different components and subunits that are subject to varying adverse environmental conditions, and that is why the sensors, actuators, and components are likely to malfunction. Unexpected defects and unplanned repairs result in high operating costs and repairs that are more significant for offshore wind turbines, because of their inaccessibility and their harsh operating environment. To reduce these costs and optimize the use of wind turbines, development of fault detection systems is highly desirable for the creation of early-warning alarms, which will help to reduce the damage and catastrophic events along with providing optimal maintenance and logistics planning. In this research, by using the machine learning techniques, several fault detection methods for the NREL 5MW offshore wind turbine with SP platform is proposed
  9. Keywords:
  10. Wind Energy ; Offshore Wind Energy ; Offshore Wind Turbine ; Fault Detection ; Machine Learning ; Spar Buoy Floating Platform

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