A Study on the Detection of Defects in Ceramic Insulators based on Radio Frequency Signatures
The presence of defects in outdoor insulators results in the initiation of partial discharge (PD) activities. As insulation failure and hence a failure of power equipment can occur due to the cumulative adverse effects of partial discharges, it is important to detect PD activities at early stages. The current techniques used in PD off-line analyses are not suitable for detecting defected insulators in the field. In this work, several cases of insulator defects are investigated in an effort to develop an online RF based PD monitoring technique using ceramic disc insulators with different types of defects. Like, an intentionally cracked ceramic insulator disc, a disc with a hole through the cap which results in internal discharges, and a completely broken insulator disc forming the first three classes. An external corona noise using a point to plane setup comprised the forth class. The defected discs are considered individually as well as incorporated into strings of 2, 3 and 4 insulators to capture the radiated RF signatures. The captured RF pulses are processed to extract statistical, spectral and wavelet based features. To classify the discharges from different types of defects, the Artificial Neural Network (ANN) algorithm is applied to the extracted features, and recognition rates of more than 90% are reported for each class.