Abstract
Partial discharge (PD) measurement is an important technique for assessing the health of power apparatus. Previous published work (Hao et al., 2006) has shown that an electro-optic system can be used for PD measurement of oil-filled power transformers. The PD signal within an oil-filled power transformer may reach a winding, and then travel along the winding to the bushing core bar. The bushing, acting like a capacitor, can transfer the high frequency components of the partial discharge signal to its tap point. Therefore, an effective PD measurement can be implemented at the bushing tap by using a radio frequency current transducer around the bushing tap earth cable. The use of an optical transmission technique not only improves the noise immunity and provides the possibility of remote measurement but also realizes electrical isolation and enhances the safety for operators. The noise induced by the electro-optic modulator may influence the measurement sensitivity. This paper investigates the use of a machine learning technique to extract the useful PD signals from the noise signals.
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