Wavelet transform and artificial intelligence based condition monitoring for GIS


Lin, T., Aggarwal, R. K. and Kim, C. H., 2003. Wavelet transform and artificial intelligence based condition monitoring for GIS. In: Transmission and Distribution Conference and Exposition, 2003 IEEE PES, 2003-01-01.

Related documents:

This repository does not currently have the full-text of this item.
You may be able to access a copy if URLs are provided below.


A condition monitoring (CM) system, which integrates wavelet analysis and artificial intelligent techniques to analyze the partial discharges (PD) patterns and classify the defective equipments in gas insulated switchgear (GIS), is presented here. The multi-resolution signal decomposition (MSD) attribute of the wavelet transform is employed to de-noise the PD signals measured on site, due to its spectrum decomposition capability. The performance of several well tried wavelets are investigated in the case of denoising, and this demonstrates that the combined use of the Biorthogonal and Daubechies type of wavelets achieves good de-noising results compared to other wavelet families. Different feature extraction methods are applied to the de-noised PD signals, to form pattern vectors which are further used as the inputs to a neural network based classifier, so as to identify the PD patterns and defective equipments in GIS. Several types of neural networks, both supervised and unsupervised (trained), are then evaluated with regard to their suitability as classifier according to training time and classification accuracy. Finally, the performance of the proposed CM system is ascertained by using a set of PD signals emanating from defects in a circuit breaker (CB), disconnect switch (DS), bus bar (BS) and insulation spacer (SP) in practical GISs in Korean 154 KV high voltage transmission networks.


Item Type Conference or Workshop Items (Paper)
CreatorsLin, T., Aggarwal, R. K. and Kim, C. H.
Uncontrolled Keywordslearning (artificial intelligence), wavelet transforms, partial discharges patterns, feature extraction, neural network, power engineering computing, gas insulated switchgear, 154 kv, multiresolution signal decomposition, circuit breaker, disconnect switch, partial discharges, feature extraction methods, artificial intelligence, high voltage transmission networks, wavelet transform, condition monitoring, condition monitoring system, neural nets, bus bar, signal denoising, insulation spacer
DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
ID Code6029


Actions (login required)

View Item