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Detection and classification of partial discharge using a feature decomposition-based modular neural network

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2 Author(s)
Tao Hong ; Dept. of Electr. Eng. & Electron., Liverpool Univ., UK ; Fang, M.T.C.

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

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Instrumentation and Measurement, IEEE Transactions on  (Volume:50 ,  Issue: 5 )