A qualitative assessment of the insulation systems of medium voltage induction motors

Abstract

The aim of this study is to qualitatively assess the insulation system's condition of medium voltage induction motors. In essence this aim of the research was to analyse and classify the data. The initial step was to understand the data. The literature review gives the background of the insulation system and the different tests done and its interpretations. The research methodology used has been explained along with description of the data. The aging of the insulation is a wide and complex topic, thus in this research the electrical aspects of the insulation were looked into and explained in detail. The data and the limitations of this study are also discussed. Data mining processes were used to gain insight into the data and the condition of the insulation system. The different stages of data mining are explained. The different stages are: identifying the problem, data understanding, data preparation and data analysis. An analysis was done using self-organizing maps, which is an unsupervised neural network technique. Hierarchical and K-mean clustering techniques were used to classify the data. The results of the different techniques were compared to an expert's assessment. The study is was an attempt to understand the condition of the insulation system and to classify the data according to its condition. A comparison was done between the different techniques used. The data was divided into four groups based on the voltage rating and class of insulation used in the motors. Good classification was obtained for three out of the four groups of data. In conclusion, the patterns in the different features of the data due to ageing were observed. The data was qualitatively assessed and classified into groups according to the deterioration of the insulation system using the classification techniques. Finally the results correlated well with the expert's assessment. In essence, the goals set for the research were achieved.