A New Method for Fault On-line Diagnosis of Metal Oxide Arrester
Harmonic distortion and leakage current(especially resistive current)are the main fault diagnosis indexes of metal oxide arresters(MOA).Aiming at the problems of traditional MOA diagnosis method or the difficulty of multiple operation and low accuracy of measurement parameters,an MOA fault diagnosis method based on Goetzel and self-or-ganizing feature map(SOM)is proposed.By extracting the third,fifth,seventh and ninth harmonic amplitudes of the base wave in the MOA leakage current,SOM neural network model is trained to realize different types of MOA fault diagnosis.The results show that after training,the SOM neural network model has a correct diagnosis rate of up to 96%for MOA fault,and the calculation time is reasonable,both the correct rate and the calculation efficiency are taken into account.
metal oxide arresterfault diagnosisself-organizing feature mapleakage current
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