Machine learning techniques for power transformer insulation diagnosis

Power transformers are one of the most critical equipments in electricity network. A number of techniques such as dissolved gas analysis (DGA), polarization and depolarization currents (PDC) measurement and frequency domain spectroscopy (FDS) have been adopted across utilities for transformer insulation diagnosis. However, there are still considerable challenges remaining in interpreting measured data of these techniques. This paper develops machine learning algorithms, which utilise archived data for making insulation diagnosis on the transformer of interest. Analysis and interpretation of field test data are presented in the paper.