Application of improved Hilbert-Huang transform to partial discharge signal analysis

As a key concern in a power system, a deteriorated insulation will cause a partial discharge phenomenon and hence degrades the power supply quality. Thus, a partial discharge test has been turned into an approach of significance to protect a power system from an unexpected fault. As the first step in this work, a defect cast resin transformer is treated as a test object, and the detected partial discharge data are then transformed into a time-frequency-energy distribution through the Hilbert-Huang-Transform (HTT). The distribution is capable of providing both time-domain and frequency-domain information. It is a highly promising approach to pattern identification of a partial discharge and fault diagnosis. There is an excellent adaptability when applied to a nonlinear as well as nonstationary signal analysis for HHT, but with two major concerns, that is, the determination of optimal shifting number and the identification of unintended illusive components. For this sake, a combination of Kolmogorov-Smirnov (K-S test) and a sorting by signal energy ratio is proposed in the determination of the optimal shifting number. Subsequently, illusive components are ridded through a cumulative K-S test, such that an intrinsic mode function (IMF) is precisely extracted, intrinsic physical meaning contained over each spectral band is well preserved, and a mode confusion problem is removed as well.