Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM
To address the problems of high difficulty and poor accuracy in extracting the structural state information of transmission towers, a transmission tower tilt state recognition solution is proposed based on the northern goshawk optimized variational mode decomposition (NGO-VMD) and long short-term me...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2023-12-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302065 |
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| Summary: | To address the problems of high difficulty and poor accuracy in extracting the structural state information of transmission towers, a transmission tower tilt state recognition solution is proposed based on the northern goshawk optimized variational mode decomposition (NGO-VMD) and long short-term memory (LSTM) neural network. The problem to determine the VMD parameters is solved by NGO, and it is demonstrated that the decomposed intrinsic mode function (IMF) components of each order can effectively extract the modal information of the tower structure. In order to make the information features more obvious, the singular value decomposition (SVD) of IMF components is performed, and it is found that the singular values of each order component have more obvious differences in different states of the tower. Finally, the LSTM neural network is introduced for feature classification to form a fault diagnosis model. A 110 kV cathead-type tower is used to verify the proposed model, and the results show that the proposed method can achieve an accuracy of 96.68% in identification of tower tilting state. Compared with other methods, this solution has the advantages of higher efficiency, stronger stability and better accuracy. |
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| ISSN: | 1004-9649 |