Fault diagnosis of a transformer based on polynomial neural networks



In view of the low accuracy of transformer fault diagnosis with traditional method, a novel multi-input and multi-output polynomial neural network (PNN) is proposed and used for transformer fault diagnosis. Firstly, single output PNN I classification model is trained and constructed according to the five kinds of characteristic gas corresponding four fault types (high-energy discharge, low-energy discharge, superheat and normal state) sample data, the transformer states are divided into normal state and abnormal state, then a transformer fault diagnosis model based on multiple output PNN II is built to aim at the three fault types such as high-energy discharge, low-energy discharge and thermal heating. Simulation and test results show that accuracy can reach 100% by using the presented model, which has excellent anti-interference performance.


Neural network Polynomial Transformer Fault diagnosis 



The authors would like to thank the National Natural Science Foundation of China (Nos. 61173036, 61272534), the “Climbing Plan” Special Fund Project of Guangdong Province (No. pdjh2017a0233) and the Science and Technology Project of Guangdong Province (No. 2016A010101028) for financial supports.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Electronic and Information Engineering CollegeGuangdong Ocean UniversityZhanjiangChina
  2. 2.Mathematics and Computer CollegeGuangdong Ocean UniversityZhanjiangChina
  3. 3.School of Mathematical SciencesNankai UniversityTianjinChina

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