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.
Highlights
The results show that the proposed feature extraction technique is appropriate for the classification of acoustic partial discharge (PD) sources and could be applied during online measurement
An acoustic emission (AE) sensor connected with an oscilloscope at a sampling frequency of 10 M sample/sec for a window of 2500 samples interfaced with MATLAB is used for data acquisition
The main objective of this study is to examine the recognition rate with different measurement conditions
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Recent rapid technological development has emphasized the need for a reliable power grid. Power transformers are one of the main components of the power system network. Reliable and continuous performance is, a quintessential objective to achieve profitable generation and transmission of electric power. During the transmission and distribution of energy from generating stations to load points, the insulation system of power transformers plays a major role in the reliability of the whole power system. The deterioration of the transformer insulation system is a natural occurrence during its service life. The insulation system will inevitably deteriorate during long-term operation, resulting in partial discharge (PD). Insulation ageing may be intensified by abnormal electric, mechanical, and thermal stresses [1]
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