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The concurrence of multiple faults makes the fault detection, in particular, the examination of both the fault types and severities, more challenging.
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However, the proposed scheme provides more accurate estimations, irrespective of the fault types, fault inception angles and fault impedances.
Back Propagation (BP) neural network is adopted to classify the fault types and determine the fault location according to the extracted features.
On the other hand, the fault types can be bus fault and feeder fault based on the fault location.
On other hand, the decision signal identifies the fault type.
The method does not require the fault type to be known, and is applicable to any type of faults.
The method is algorithmically simple and does not require the fault type.
Then, the BT-SVM is utilized to automatically complete the fault type identification.
They are inputted into three ANFISs to obtain the fault type.
Four-parallel ANN's are designed in order to achieve the fault type classification.
By recording the fault locations and syndromes, the diagnosis system can identify the fault type of each faulty cell.
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