Extraction of wave parameter for classification of partial discharge using Pearson's correlation method

• Published in: Australian journal of electrical & electronics engineering 2014-02, Vol.11 (1), p.105
• Main Authors: Maheswari, R.V, Subburaj, P, Vigneshwaran, B, Sharmila, G
• Format: Article
• Language: English
• Subjects: Analysis; Methods; Neural networks
• ISSN: 1448-837X
• DOI: 10.7158/E13-049.2014.11.1.

Partial discharge (PD) behaviour in electrical apparatus leads to the identification of the degradation of insulating materials. The major problem here is identifying the different types of PD discharges that affect the insulation system. In general the characteristics of PD pulse will be in the form of internal, corona and surface discharges (both in air and oil conditions). Thus the classification of PD patterns aims to recognise the discharges from unknown source. In this paper a new enhanced technique is used to identify the PD discharges namely Pearson's correlation coefficient techniques. Starting with PD data on different families of specimen, extract φ-q-n PD patterns (3D patterns) and apply to the enhanced techniques, then segmented the 3D patterns in to number of samples. Thereafter, by evaluating statistical operators like skewness, kurtosis and Pearson's correlated values from the PD signals and recognise the different PD patterns using artificial neural networks and adaptive neuro fuzzy interference system. KEYWORDS: Partial discharge; 3D patterns; artificial neural network (ANN); adaptive neuro fuzzy inference system (ANFIS); Pearson's cross correlation (PCC).