Rapid Non-Destructive Condition Assessment of Insulating Materials

Abstract

The condition of high voltage electrical insulation systems used in power cables, power transformers and generators is influenced by a number of manufacturing and operating variables which affect performance and failure. Although forensic examinations of solid insulation failures are commonly conducted and there are many electrical tests to assess the condition of insulation systems, there has been only a limited effort to investigate and spatially map the condition of solid insulating materials in regard to their chemical characteristics, physical properties and structure, all of which can affect failure. Non-destructive and portable methods of condition assessment based upon various spectroscopies are now being developed. This includes infrared and Raman based approaches, including so-called wide wavelength methods, and microprobe methods, all of which are capable of rapidly measuring spatially resolved chemical composition and structure of polymeric insulated power cables, power transformer winding insulation and generator stator insulation. By combining multivariate statistical analysis (MVSA) and spectroscopic methods, we have been able to explore and extract relationships between chemical change in the material and "intrinsic variables" such as stressing time, temperature and voltage as well as explore the relationships with physical properties and performance such as electrical breakdown strength, mechanical strength and charge mobility. Recent work illustrates the power of such methods in exploring key property and performance relationships and the ability to map these spatially with new remote and rapid spectroscopic optical probe methods. We conclude that well-populated MVSA models can relate chemical and physical change in solid insulation systems to electrical measurements, to potential indicators of insulation ageing and provide a predictive capability for insulation condition assessment which could assist in insulation life prediction and improved asset management.