Improving detection sensitivity for partial discharge monitoring of high voltage equipment

Partial discharge (PD) measurements are an important technique for assessing the health of power apparatus. Previous published research by the authors has shown that an electro-optic system can be used for PD measurement of oil-filled power transformers. A PD signal generated 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 earthed tap point. Therefore, an effective PD current measurement can be implemented at the bushing tap by using a radio frequency current transducer around the bushing-tap earth connection. In addition, the use of an optical transmission technique not only improves the electrical noise immunity and provides the possibility of remote measurement but also realizes electrical isolation and enhances safety for operators. However, the bushing core bar can act as an aerial and in addition noise induced by the electro-optic modulation system may influence overall measurement sensitivity. This paper reports on a machine learning technique, namely the use of a support vector machine (SVM), to improve the detection sensitivity of the system. Comparison between the signal extraction performances of a passive hardware filter and the SVM technique has been assessed. The results obtained from the laboratory-based experiment have been analysed and indicate that the SVM approach provides better performance than the passive hardware filter and it can reliably detect discharge signals with apparent charge greater than 30 p