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

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