@phdthesis{ghosh_development_2018,
  howpublished = {Student Thesis},
  address = {München},
  type = {Master Thesis},
  title = {Development of a Framework for Fault Diagnosis of a Wind Turbine},
  abstract = {Condition Monitoring System and Fault Detection System have come a long way now with its continuous development since 1980s. They have been proved essential and beneficial by various studies encompassing wind turbine productivity, operation and maintenance costs. The costs involved in maintenance is a major part in determining the cost of energy. This serves as a good motivation for improvement of this factor via condition monitoring and fault detection in structures which can prevent major failures and allow savings on repairs. There are various methods to design a monitoring system and some of them are discussed along with their history of development. Fault detection is a major of a monitoring system where an anomaly in the system performance is detected by the method of comparison between a healthy operating system and system under consideration. However, only the knowledge of the faults are not sufficient, instead, an analysis of the nature of the faults reveals quite lot of information about a structure. This information can be used for further developments of advanced control systems for robust operation of wind turbines. The fault diagnosis part of the whole system is studied in this master's thesis. A Fault Diagnosis Toolbox in MATLAB is used to perform diagnostic tests. For this purpose, models in Modelica are taken as standard models. A conversion process is done in this study for the models from Modelica to MATLAB via an XML file. The XML file generated by Dymola is studied in-depth to develop a parser in MATLAB to translate the models to MATLAB. The parser is developed to handle complex Modelica models. This thesis also suggests various postprocessing of the models in MATLAB to adjust the toolbox requirements. Finally, the toolbox is implemented for the model to visualise some concrete results which give the readers hope to continue further research with this toolbox.},
  school = {Technische Universität München},
  author = {Ghosh, Tiasa},
  year = {2018},
  note = {Supervisors: Tobias Meyer, Philipp Thomas. Examiners: Carlo L. Bottasso, Marta Bertéle}
}