Abstract
The wind turbine drive system is one of the key components in converting wind energy into electrical energy. The life prediction of drive system is very important for the maintenance of wind turbine. With increasing capacity, the wind turbine system has become more complicated. Consequently, for the life prediction of drive system, it is necessary to consider the problems of multi-information fusion of big data, quantification of time-varying dynamic loads, and analysis of multiple-damage coupling. In order to solve the above challenges, the fatigue life analysis and evaluation method considering the interaction of coupled multiple damages are proposed in this study. The hierarchical Bayesian theory with fault physics technology is introduced to deal with the uncertainty of wind turbine drive system. Then, a time-varying performance analysis model is established based on the multiple-damage coupling competition failure mechanism. Moreover, the Internet of Things (IoT) technology is introduced and combined with the proposed model. Through the data collection by IoT, the time-stress curve of drive system can be obtained. A case study about the remaining fatigue life estimation of drive system is utilized to illustrate the effectiveness of the proposed method.
Highlights
Wind energy is a common clean and renewable energy. e effective utilization of wind energy can reduce the energy crisis and protect the environment. e early development of wind energy is mainly carried out on land
Uncertain environment has restricted the development of offshore wind turbines and threatened the wind turbine drive system security [2]. e design requirements for offshore wind farms are more difficult than those for onshore wind farms [3,4,5,6]
The current research mainly focuses on the relationship between a certain fault state and system dynamics. ese research works are short on developing dynamic fault response mechanism. is paper proposes the coupling fault diagnosis mechanism and the fatigue life prediction method based on wind farm complex conditions
Summary
Wind energy is a common clean and renewable energy. e effective utilization of wind energy can reduce the energy crisis and protect the environment. e early development of wind energy is mainly carried out on land. Us, the dynamic model is proposed for failure mechanism analysis, fault diagnosis, and life prediction. Is paper proposes the coupling fault diagnosis mechanism and the fatigue life prediction method based on wind farm complex conditions. A remaining life prediction method based on deep learning is proposed, and it can be operated under multiple failure models. En, this method can achieve nonlinear regression analysis of the fusion data and product remaining life under multiple failure models. E state monitoring and life prediction system of the wind turbine drive system based on the Internet of ings can be divided into three functional levels. Its function is to achieve acceptance, storage, display, and intelligent processing of the working condition information and manage the entire life cycle of the wind turbine. The ARM processor collects fault signals, status signals, current signals, and vibration signals of the transmission system and delivers them to the PC/104 for processing. e PC/104 processes the collected information and displays the results and status monitoring information on the LCD screen. e PC/104 can share the information with the centralized control center through the Ethernet communication module. en, the centralized control center can monitor the transmission system in real time
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