Partial discharge pattern classification using multilayer neural networks

Partial discharge measurement is an important means of assessing the condition and integrity of insulation systems in high voltage power apparatus. Commercially available partial discharge detectors display them as patterns by an elliptic time base. Over the years, experts have been interpreting and recognising the nature and cause of partial discharges by studying these patterns. A way to automate this process is reported by using the partial discharge patterns as input to a multilayer neural network with two hidden layers. The patterns are complex and can be further complicated by interference. Therefore the recognition process appropriately qualifies as a challenging neural network task. The simulation results, and those obtained when tested with actual patterns, indicate the suitability of neural nets for real world applications in this emerging domain. Some limitations of this method are also mentioned.

[1]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[2]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[3]  Nathan H. Combs,et al.  PROBLEM OF INTERNAL REPRESENTATIONS IN NEURAL NETS: CONCEPTS, IMPLICATIONS, AND A PROPOSED METRIC. , 1987 .

[4]  P. Reynolds.  Computer — Aided partial discharge measurements , 1987, Conference on Electrical Insulation & Dielectric Phenomena — Annual Report 1987.

[5]  Raymond L. Watrous.  Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization , 1988 .

[6]  D.A. Nattrass,et al.  Partial discharge measurement and interpretation , 1988, IEEE Electrical Insulation Magazine.

[7]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[8]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[9]  Separable Regions.  On Hidden Nodes for Neural Nets , 1989 .

[10]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[11]  F. H. Kreuger,et al.  Partial Discharge Detection in High Voltage Equipment , 1990 .

[12]  D. F. Michaels.  On information representation in backpropagation classifier networks , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[13]  N. Hozumi,et al.  Discrimination of partial discharge patterns using a neural network , 1991 .

[14]  Dejan J. Sobajic,et al.  Neural Networks and Knowledge Engineering , 1991, IEEE Trans. Knowl. Data Eng..

[15]  Kishan G. Mehrotra,et al.  Bounds on the number of samples needed for neural learning , 1991, IEEE Trans. Neural Networks.

[16]  Ah-Hwee Tan,et al.  Connectionist Expert System with Adaptive Learning Capability , 1991, IEEE Trans. Knowl. Data Eng..

[17]  H. Suzuki,et al.  Pattern recognition of partial discharge in XLPE cables using a neural network , 1991, [1991] Proceedings of the 3rd International Conference on Properties and Applications of Dielectric Materials.

[18]  Panos J. Antsaklis,et al.  A simple method to derive bounds on the size and to train multilayer neural networks , 1991, IEEE Trans. Neural Networks.

[19]  Yih-Fang Huang,et al.  Bounds on the number of hidden neurons in multilayer perceptrons , 1991, IEEE Trans. Neural Networks.