Wind power, being a recyclable and renewable resource, makes for a sizable portion of the new energy generation sector. Nonetheless, the wind energy industry is experiencing early failure of important components of wind turbines, with the majority of these issues also involving wind power bearings. Bearing dependability is directly tied to the transmission efficiency and work performance of wind turbines as one of its major components. The majority of wind turbine failures are due to bearings, and the vast majority of bearing failures are due to lubrication. The topic of improving the accuracy and life of wind power bearing motion is becoming increasingly essential as the wind power industry develops rapidly. This study examines the various constructions and types of wind turbines, as well as their bearings. We also examined the most typical causes of friction and lubrication failure. Furthermore, contemporary research on wind turbine bearings has been compiled, which mostly comprises the study and development of lubrication technology and other areas. Finally, a conclusion and outlook on current challenges, as well as future research directions, are offered.
Bearings are crucial components that decide whether or not a wind turbine can work smoothly and that have a significant impact on the transmission efficiency and stability of the entire wind turbine’s life. However, wind power equipment operates in complex environments and under complex working conditions over long time periods. Thus, it is extremely prone to bearing wear failures, and this can cause the whole generator set to fail to work smoothly. This paper takes wind turbine bearings as the research object and provides an overview and analysis for realizing fault warnings, avoiding bearing failure, and prolonging bearing life. Firstly, a study of the typical failure modes of wind turbine bearings was conducted to provide a comprehensive overview of the tribological problems and the effects of the bearings. Secondly, the failure characteristics and diagnosis procedure for wind power bearings were examined, as well as the mechanism and procedure for failure diagnosis being explored. Finally, we summarize the application of fault diagnosis methods based on spectrum analysis, wavelet analysis, and artificial intelligence in wind turbine bearing fault diagnosis. In addition, the directions and challenges of wind turbine bearing failure analysis and fault diagnosis research are discussed.
Lubrication plays a key role in increasing availability of wind turbines, extending unit life and reducing operating costs. In view of the problems of valve core lag, grease hardening and difficulty in removing waste oil in a centralized lubrication system, an improved centralized lubrication system and waste oil recovery system were designed in this study. Discharge of waste grease in the bearing cavity was simulated under different vacuum conditions. It was shown that vacuum degree of bearing cavity is proportional to oil output speed of waste grease. Performance and fatigue reliability tests of the waste grease suction and drainer device test platform were conducted over 12,000 fatigue cycles. The results show that the vacuum degree error of the waste grease suction and drainer device before and after the test is less than 5%, and the power oil pressure, oil output pressure and oil output quantity of the test product are stable, indicating that the designed waste grease suction and drainer device has excellent sealing and reliability. The waste grease suction and drainer device can eliminate grease discharge resistance in the bearing cavity, facilitating discharge of waste oil and improving wind turbine operation efficiency.
The paper tried to integrate the DGA data with the gas production rate, which are the major indexes of transformer fault diagnosis. Duval's triangle method, BP neural network and IEC three-ratio method were weighted. Firstly, the paper regarded the gas production rate as the independent variables, fitted the cubic curves of the gas production rate and variance of each diagnosis method, and then defined the weights of each algorithm through the data processing method of unequal precision. At last, the dynamic weighted combination diagnosis model was established. That is, the weight is different as the gas production rate changes although the method is identical. The results of diagnosis examples show that the accuracy rate of the weighted combination model is higher than any single algorithm, and it has certain stability as well.
At present, the demand for ready-mixed concrete (RMC) in construction industry is increasing day by day, and the supply mode of multiple delivery depots corresponding to multiple construction sites has been widely used. In order to further improve the joint distribution efficiency between various delivery depots, this research establishes a multiobjective optimal distribution model with time window constraints and demand postponement attributes for the problem that the subbatching plants need to work together. The model divides the reasons for demand postponement into two types: the constraint for timely unloading of trucks cannot be met on time and the constraint for timely pouring at the construction site cannot be met on time. This work improved the coding method of genetic algorithm based on the characteristics of the distribution model. Using hierarchical real-coding form, the coding operator of each layer can be evolved separately, which ensures the globality of the search, and, at the same time, an improved immune operator is added to ensure the local search performance. By comparison, the results obtained by improved GA are 7.05% higher than those of the standard GA, and the early convergence speed of improved GA is obviously better than that of the standard GA. The simulation experiments show that the total trucks’ waiting time during the process of providing delivery services from 5 concrete plants to 8 construction sites is 769 minutes, and the total waiting time of 8 construction sites is 507 minutes. Through practical case analysis, this work can enable RMC production enterprises and construction sites to effectively reduce the waiting time of corresponding operations, and the obtained results are close to the simulation results. The proposed method indeed improves the efficiency of RMC distribution.
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