A knowledge-based genetic algorithm for the job shop scheduling problem
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
EEG analysis using neural networks for seizure detection
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Neural network aided breast cancer detection and diagnosis
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
ECG analysis using nonlinear PCA neural networks for ischemiadetection
IEEE Transactions on Signal Processing
Determination of neural-network topology for partial discharge pulse pattern recognition
IEEE Transactions on Neural Networks
WSEAS TRANSACTIONS on SYSTEMS
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Partial discharge (PD) measurement and recognition is a significant tool for potential failure diagnosis of the high-voltage equipment. This paper proposes the application of fuzzy c-means (FCM) clustering approach to recognize partial discharge patterns of cast-resin current transformer (CRCT). The PD patterns are measured by using a commercial PD detector. A set of features, used as operators, for each PD pattern is extracted through statistical schemes. The significant features of PD patterns are extracted by using the nonlinear principal component analysis (NLPCA) method. The proposed FCM classifier has the advantages of high robustness and effectiveness to ambiguous patterns and is useful in recognizing the PD patterns of the high-voltage equipment. To verify the effectiveness of the proposed method, the classifier was verified on 250 sets of field-test PD patterns of CRCTs. The test results show that the proposed approach may achieve quite satisfactory recognition of PD patterns.