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.
Highlights
Synchronous Generators are the majority source of commercial electrical energy
Internal faults present a real challenge for the protection of electrical machines; especially ground faults in case of high impedance grounding as they are not detectable by differential relays, the most commonly devices used for generator protection
5 Conclusion This paper proposed a novel scheme based on Support Vector Machine (SVM) for detecting and classifying faults in stator windings of a synchronous generator
Summary
Synchronous Generators are the majority source of commercial electrical energy. The failure of SGs cause severe damage to the machine, interruption of electrical supply, and ensuing economic loss. A reliable and accurate diagnosis of internal faults is still a challenging problem in the area of fault diagnosis of electrical machines [1]. This fact has motivated many works over long period to develop various protection techniques [2,3,4,5,6,7,8,9,10,11,12,13] include digital, Artificial Intelligence (AI) and other machine learning techniques
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