Operating Condition Identification of On-load Tap Changer Based on MSSST and RLCNN

Abstract

The operating condition identification of on-load tap changer under actual service environment yields no desired effect. To solve this problem， the paper proposes an operating condition recognition method based on MSSST （multi-synchronous squeezing S transform） and RLCNN (reinforced lightweight convolution neural network) is proposed. In this method， the multi-synchronous squeezing S transform is firstly introduced into the field of power equipment condition monitoring and applied to analyze the vibration signal of on-load tap changer so that the two-dimensional time frequency characteristic of signal can be effectively depicted. In additional， the MobileNetv2 lightweight convolution neural network is fused with the Adaboost adaptive lifting mechanism， and a novel RLCNN model is proposed. Then the two-dimensional time frequency maps of vibration signal are regarded as the samples to train the model， which is used to judge the operating condition of on-load tap changer. Experimental results show that the method can accurately judge the different operating conditions of on-load tap changer. Compared with other identification methods， this method has a higher precision rate and better stability as well as practical engineering application value.

Keywords

• on-load tap changer
• mssst
• adaboost adaptive lifting mechanism
• rlcnn
• operating condition identification