Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM

doi:10.11930/j.issn.1004-9649.202302065

Abstract

Abstract:

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.

Key words: tower tilt, state recognition, northern goshawk optimized, adaptive variational mode decomposition, singular value decomposition, long short-term memory neural network

Long ZHAO, Guanru WEN, Zhicheng LIU, Peng YUAN, Xinsheng DONG. Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM[J]. Electric Power, 2023, 56(12): 217-226, 237.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202302065

https://www.electricpower.com.cn/EN/Y2023/V56/I12/217

Figures/Tables 17

Fig.1 Tower tilt state recognition process

Fig.2 NGO-VMD Process

Fig.3 LSTM cell structure

Fig.4 Finite element simulation

Table 1 Tower inherent frequency

模态数		固有频率/Hz
1		2.5770
2		3.2770
3		9.2728
4		14.8530
5		17.2590

Fig.5 Decomposition results of NGO-VMD and CEEMDAN

Fig.6 IMF frequency domain components of NGO-VMD and CEEMDAN decomposition

Fig.7 The third-order vibration mode of transmission tower

Fig.8 Response test site

Fig.9 Cathead tower vibration signals

Fig.10 Adaptation curve of each optimization algorithm

Fig.11 NGO-VMD decomposition of vibration signals

Fig.12 Distribution of singular values in different tilt states

Fig.13 The 3rd and 4th order singular value box plots

Fig.14 LSTM training progress

Fig.15 Classification results of test sets with different classification methods

Table 2 Accuracy of different classification methods

分类方法	训练集准确率/%	测试集准确率/%
LSTM	98.89	96.67
BP	95.56	88.34
RF	94.45	91.67
SVM	84.45	76.67

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