Abstract
This paper proposes an automatic classification system for impulsive electromagnetic (EM) noises. The classification is based on the frequency spectrum of EM waves emitted from partial discharges in power apparatus. This concept is validated by confirming the reproducibility of waveforms of EM waves. In order to clarify the effectiveness of our proposal, six defects are considered: a needle-plane electrode, a strain insulator, a pin-type insulator, FDF (Forced Draft Fun), CBP (Condensate Booster Pump), and COOL-P (Cooling Water Pump). Neural networks are used to classify the frequency spectrum of EM waves. As a main result, these sources are well classified with the recognition rate of 80% or better. This is supported by treating a thousand of EM pulses.
