Determination of neural-network topology for partial discharge pulse pattern recognition
IEEE Transactions on Neural Networks
Applying self-organizing mapping neural network for discovery market behavior of equity fund
WSEAS Transactions on Information Science and Applications
WSEAS TRANSACTIONS on SYSTEMS
Comparison of competitive learning for SOM used in classification of partial discharge
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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Partial discharge (PD) measurement and recognition is a significant tool for potential failure diagnosis of a power transformer. This paper proposes the application of self organizing map (SOM) 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 proposed SOM classifier has the advantages of high robustness to ambiguous patterns and is useful in recognizing the PD patterns of electrical transformers. 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.