Title :
Detection and classification of partial discharge using a feature decomposition-based modular neural network
Author :
Hong, Tao ; Fang, M.T.C.
Author_Institution :
Dept. of Electr. Eng. & Electron., Liverpool Univ., UK
fDate :
10/1/2001 12:00:00 AM
Abstract :
This paper develops a feature decomposition-based modular neural network (MNN) for the recognition of partial discharge (PD) sources. The original statistical analysis-based feature set is naturally partitioned into three disjointed feature subsets. These subsets are independently fed into three neural subnetworks. The aggregation of the sub-networks, by an integrating unit using a majority vote strategy, provides the final assignment of PD patterns to a particular PD source. Compared with a single neural network (SNN) with the same feature vector, the training of MNN is faster, the network is more robust, and the success rate of classifying "unseen" patterns is higher
Keywords :
feature extraction; neural nets; partial discharge measurement; pattern classification; feature decomposition; majority vote strategy; modular neural network; partial discharge classification; partial discharge detection; statistical analysis; Fault location; Feature extraction; Insulation; Multi-layer neural network; Neural networks; Partial discharges; Pattern recognition; Signal detection; Signal processing; Voltage;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on