Transactions of China Electrotechnical Society  2018, Vol. 33 Issue (1): 217-224    DOI: 10.19595/j.cnki.1000-6753.tces.161084
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Aging Prediction of Composite Tower Based on FBG Sensing Technology
Li Shanshan1, Wang Linong1, Fang Yaqi1, Guo Zhenping2, Song Bin1
1. School of Electrical Engineering Wuhan University Wuhan 430072 China;
2. Jingzhou Power Supply Company of State Grid Hubei Electric Power Company Jingzhou 434007 China

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Abstract  5 000 h multi-factor aging test data show that the cyclic loading of aging factor gave rise to certain rules of daily strain curve at each fiber monitoring sites, showing an overall shape of the two peaks and two valleys. The characteristic quantities KP1 (first local peak) gradually decreased with the aging process. In this paper, with the aid of Matlab software, the tendency of strain was predicted by using BP neural network and gray model, and priority was given to the TD21538 channel for its larger load. The results show that: take 60% KP1 as failure condition, the equivalent accelerated aging life of composite material tower is 9 500 h. As the consideration of extremely serious test condition, calculated life is far less than the actual service life, since the service life of composite material is much longer than the test time of 5 000 h, it shows high ageing-resistant performance of poly urethane composite material.
Key wordsMultifactor aging      fiber bragg grating      back propagation neural network       gray model       poly urethane composite material     
Received: 13 July 2016      Published: 16 January 2018
PACS: TM215  
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Li Shanshan
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Guo Zhenping
Song Bin
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Li Shanshan,Wang Linong,Fang Yaqi等. Aging Prediction of Composite Tower Based on FBG Sensing Technology[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 217-224.
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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.161084     OR     https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I1/217
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