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A Frequency Response Analysis and Machine Learning Combined Method for Interturn Faults Identification in Synchronous Generators

Electrical machines and especially synchronous generators are key pieces of electrical power systems. Their operation conditions can lead into electrical faults where ground faults and interturn faults are the most probable in large machines. To avoid these types of faults, they must be correctly monitored and diagnosed during their scheduled stops. This paper proposes a new off-line interturn fault location method for synchronous machines based on frequency response analysis (FRA) combined with a cascade machine learning techniques application. The method uses some key points detected as the FRA transfer function points more susceptible to change during interturn faults in system gain, frequency and angle. Using these parameters with different machine learning techniques provided by Regression Learner and Classification Learner Matlab Toolboxes, as exponential Gaussian Process Regression, Linear Discriminant technique or Boosted Trees algorithm, the fault severity is estimated and the fault location delimited. This allows to save time during repairments of maintenances. The method has been corroborated by 252 FRA tests performed to a 40 MVA synchronous generator rotor’s pole.