| Abstract: |
| This paper proposes a novel scheme for detecting and classifying faults in stator windings of a synchronous
generator (SG). The proposed scheme employs a new method for fault detection and classification based on
Support Vector Machine (SVM). Two SVM classifiers are proposed. SVM1 is used to identify the fault occurrence in
the system and SVM2 is used to determine whether the fault, if any, is internal or external. In this method, the
detection and classification of faults are not affected by the fault type and location, pre-fault power, fault resistance
or fault inception time. The proposed method increases the ability of detecting the ground faults near the neutral
terminal of the stator windings for generators with high impedance grounding neutral point. The proposed
scheme is compared with ANN-based method and gives faster response and better reliability for fault classification. |
| Key words: Support vector machine, Artificial neural networks, Synchronous generator, Differential protection,Fault detection, Fault classification |
| DOI：10.1186/s41601-017-0057-x |
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| Fund: |
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