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Fault Recognition of On-Load Tap-Changer Based on Improved BP Neural Network

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DOI: 10.23977/iccsc.2017.1005


Chunbing Jiang

Corresponding Author

Chunbing Jiang


On-load tap-changer (OLTC) is an important part in power transformer, which is used to change the transformation ratio by switching contact form one winding tap to another without interrupting the load. It is necessary to make sure the effective running of OLTC. Thus, a fault recognition method for OLTC based on improved BP neural network is proposed in this paper, according to the working characteristicsof OLTC. The feature of this improved neural network is that the learning rate is variable. Experimental results on 2000 test samples from offline AC test device have showed the effectiveness of the proposed fault recognition method.


Fault recognition, On-load tap-changer, Neural network, Variable learning rate.

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