Operating Condition Identification of On-load Tap Changer Based on MSSST and RLCNN
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 conv...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
zhejiang electric power
2022-04-01
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| Series: | Zhejiang dianli |
| Subjects: | |
| Online Access: | https://zjdl.cbpt.cnki.net/WKE3/WebPublication/paperDigest.aspx?paperID=41588050-a883-4bbd-822e-366ac070799b |
| Summary: | 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. |
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| ISSN: | 1007-1881 |