Outdoor Insulators Testing Using Artificial Neural Network-Based Near-Field Microwave Technique

This paper presents a novel artificial neural network (ANN)-based near-field microwave nondestructive testing technique for defect detection and classification in nonceramic insulators (NCI). In this paper, distribution class 33-kV NCI samples with no defects, air voids in silicone rubber and fiber glass core, cracks in the fiberglass core, and small metallic inclusion between the fiber core and shank were inspected. The microwave inspection system uses an open-ended rectangular waveguide sensor operating in the near-field at a frequency of 24 GHz. A data acquisition system was used to record the measured data. ANN was trained to classify the different types of defects. The results showed that all defects were detected and classified correctly with high recognition rates.

I. Introduction

Nonceramic insulators (NCI), particularly those made of silicone rubber (SIR), are increasingly being used in both distribution and transmission overhead lines. They exhibit several advantages over ceramic insulators such as low surface energy that helps to maintain a hydrophobic surface that facilitates a good performance in polluted outdoor service conditions. In addition, SIR insulators cost less and weigh less than the heavy ceramic insulators. However, NCI still have certain disadvantages such as aging, their unknown long-term reliability, and the difficulty to detect defective insulators.