作者:
Y Aslan
关键词:
Fault location；Distribution lines；Artificial neural networks；Embedded generation
摘要:
In this paper, the design and implementation of a feed-forward artificial neural network (ANN)-based fault locator to classify and locate shunt faults on primary overhead power distribution lines with load taps and embedded remote-end power generation is presented. In the ANN algorithm, the standard back-propagation technique with a sigmoid activation function is used. The fault locator utilizes fault voltage and current samples obtained at a single location of a typical radial distribution system. The ANNs are trained with data under a wide variety of fault conditions and used for the fault type classification and fault location on the distribution line. A 34.5聽kV distribution system is simulated using electro-magnetic transients program and their results are used to train and test the ANNs. The ANN-based fault locator gives high accuracy for the vast majority of the practically encountered systems and fault conditions, including the presence of load taps and the remote-end in-feed source.
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