Open AccessNew statistical approach to interpret power transformer frequency response analysis: non‐parametric statistical methods
Author(s) -
Behjat Vahid,
Mahvi Mojtaba,
Rahimpour Ebrahim
Publication year - 2016
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2015.0204
Subject(s) - parametric statistics , randomness , wilcoxon signed rank test , transformer , statistical hypothesis testing , statistical inference , statistical analysis , computer science , engineering , statistics , mathematics , voltage , electrical engineering , mann–whitney u test
The frequency response analysis (FRA) test has been recognised as one of the sensitive tools for detecting electrical and mechanical faults inside power transformers. However, there is still no universally systematic interpretation technique for these tests. Many research efforts have employed different statistical criteria in order to aid the interpretative capability of the FRA, but it is shown that the methods used so far are based on parametric statistics, which need a set of assumptions about the normality, randomness and statistical independence of FRA data. Therefore, this study aims to propose some non‐parametric statistical methods which are based on explicitly weaker assumptions than such classical parametric methods. The proposed statistical methods are applied to the experimental FRA measurements obtained from two test objects: a three phase distribution transformer (35/0.4 kV, 100 kVA) to study the winding interturn fault and a two winding transformer (1.2 MVA, 10 kV) for the study of radial deformation. The non‐parametric statistical methods, namely Spearman correlation coefficient, Wilcoxon signed rank test and Friedman test are used to compare FRA traces. It was found through this research work that the applied non‐parametric methods can effectively reflect the differences between compared FRA data and diagnose the fault.