Development of Hankel Singular-Hypergraph Feature Extraction Technique for Acoustic Partial Discharge Pattern Classification

Abstract

Different types of classifiers for acoustic partial discharge (PD) pattern classification have been widely discussed in the literature. The classifier performance mainly depends on the measurement conditions (location and type of the PD, acoustic sensor position and frequency response) as well as extracted features. Recent research posits that features extracted by singular value decomposition (SVD) can exhibit the natural characteristics and energy contained in the signal. Though the technique by itself is not novel, in this paper, SVD is employed for PD classification in a revised way starting from data arrangement in Hankel form, to embedding the hypergraph-based features and finally to extracting the required set of optimal features. The algorithm is tested for various measurement conditions that include the influences of various PD locations and oil temperatures. The robustness of the algorithm is also tested using noisy PD signals. Experimental results show the proposed feature extraction method supremacy.

Discharging state recognition method of intelligent ring network cabinet based on audio signal spectrum analysis

“…At this time, the free electrons of the ring main unit collide with the gas molecules, and the energy carried by the electrons is enough to ionize positive ions and new electrons. New electrons continue to move and collide again to produce more positive ions and electrons, thus forming a circular space, and the electrons in the space will multiply [20][21][22]. After the formation of avalanche, the self sustained discharge condition of the ring network cabinet can be met.…”