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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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2023-12-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302065 |
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| _version_ | 1850249725520904192 |
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| author | Long ZHAO Guanru WEN Zhicheng LIU Peng YUAN Xinsheng DONG |
| author_facet | Long ZHAO Guanru WEN Zhicheng LIU Peng YUAN Xinsheng DONG |
| author_sort | Long ZHAO |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-36e9d522abb1406ca48b16e86b629c8c |
| institution | OA Journals |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-12-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-36e9d522abb1406ca48b16e86b629c8c2025-08-20T01:58:27ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-12-01561221722610.11930/j.issn.1004-9649.202302065zgdl-56-08-zhaolongTransmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTMLong ZHAO0Guanru WEN1Zhicheng LIU2Peng YUAN3Xinsheng DONG4School of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaSchool of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, ChinaXi'an Qinchuang Electric Co., Ltd., Xi'an 710000, ChinaState Grid Xinjiang Electric Power Research Institute, Urumqi 830063, ChinaTo 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.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302065tower tiltstate recognitionnorthern goshawk optimizedadaptive variational mode decompositionsingular value decompositionlong short-term memory neural network |
| spellingShingle | Long ZHAO Guanru WEN Zhicheng LIU Peng YUAN Xinsheng DONG Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM Zhongguo dianli tower tilt state recognition northern goshawk optimized adaptive variational mode decomposition singular value decomposition long short-term memory neural network |
| title | Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM |
| title_full | Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM |
| title_fullStr | Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM |
| title_full_unstemmed | Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM |
| title_short | Transmission Tower Tilt State Recognition Based on Parameter Optimization of VMD-SVD and LSTM |
| title_sort | transmission tower tilt state recognition based on parameter optimization of vmd svd and lstm |
| topic | tower tilt state recognition northern goshawk optimized adaptive variational mode decomposition singular value decomposition long short-term memory neural network |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202302065 |
| work_keys_str_mv | AT longzhao transmissiontowertiltstaterecognitionbasedonparameteroptimizationofvmdsvdandlstm AT guanruwen transmissiontowertiltstaterecognitionbasedonparameteroptimizationofvmdsvdandlstm AT zhichengliu transmissiontowertiltstaterecognitionbasedonparameteroptimizationofvmdsvdandlstm AT pengyuan transmissiontowertiltstaterecognitionbasedonparameteroptimizationofvmdsvdandlstm AT xinshengdong transmissiontowertiltstaterecognitionbasedonparameteroptimizationofvmdsvdandlstm |