| Summary: | This thesis focuses on improving shaft-voltage-based condition monitoring of synchronous
generators. The work presents theory for describing and modelling shaft
voltages using fundamental electromagnetic principles. A modern framework is
adopted in developing an online, automated and intelligent fault-diagnosis system.
Novel processing and inferential methods are used by the system to provide accurate
and reliable incipient-fault detection and diagnosis. The literature shows
that shaft-voltage analysis is recognised as a technique with potential for use in
condition monitoring. However, deficiencies in the fundamental theory and the inadequacy
of methods for extracting useful information has limited its widespread
application. This work extends the knowledge of shaft voltages, validates the
merits of its use for fault diagnosis, and provides methods for practical application.
Validation of the model is completed using an experimental synchronous
generator, and results indicate that simulated shaft voltages compare well with
the measurements - i.e. total average error of the model combined with experimental
uncertainty is below 16%. The fault detection and diagnosis components
are tested separately and together as a complete shaft-voltage-based conditionmonitoring
system in an experimental setting. Results indicate that the system
can accurately diagnose faults and it represents a unique and valuable contribution
to shaft-voltage-based condition monitoring. Additionally, techniques such as optimal
measurement selection, multivariate model monitoring, and fault inference
developed for the investigations and system presented in this thesis, will assist
engineers and researchers working in the field of condition monitoring of electrical
rotating machines.
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