Dynamic modeling and failure mechanism study of herringbone gear planetary transmission system for wind turbine under gear cracks
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The herringbone gear planetary transmission system (HGPTS) is a common component in the gearbox of wind turbines. Studying the dynamic performance of gear systems is the key to improving their stability. To reveal the influence of cracks on the dynamic characteristics of the HGPTS, using slicing method, we explore the influence of different crack factors on the time-varying meshing stiffness (TVMS) of the system. A 55-degrees of freedom bending torsion axis pendulum dynamic model was constructed using the centralized mass parameter method; in the model, factors such as TVMS of cracks as well as receding groove and errors are considered. The Runge–Kutta method was used to solve the dynamics, and the evolution diagram of the vibration and load distribution characteristics of the system under crack changes was obtained. Vibration tests were conducted on the system studied in this work. The results show that under the influence of cracks, the TVMS of the external and internal meshing pairs of the system will decrease, and as the cracks intensify, the fluctuation of the TVMS will decrease. Cracks can lead to regular impact behavior in the system. A modulation sideband centered on the meshing frequency appears in the vibration response, and the vibration trajectory of the gears will also become disordered, at the same time, under the influence of cracks, there are significant excitation factors in the load-sharing characteristics of the system. As the cracks continue to intensify, the vibration of the system becomes increasingly evident, and the load-sharing characteristics show a trend of first decreasing and then increasing. The vibration test results are in good agreement with the theoretical predictions. The correctness of the established model is verified. The research results can provide reference for the reliability research of wind turbine HGPTS.Keywords:
Failure mechanism
Transmission system
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The accurate prediction of wind direction is an effective way to improve wind energy utilization and extend the life of wind turbine yaw system. The "Measure-Share-Correlate- Predict-Verify" (MSCPV) wind parameter sensing technology based on the turbine network provides a new perspective for wind direction prediction, in which the screening of correlated wind turbines is a key step. This paper proposes a calculation method of wind direction spatial correlation and describes its process in detail. The correlated turbines are selected for the target turbine according to the calculations. The paper first introduces the principle of MSCPV wind prediction and proposes the concept of correlated turbine screening based on yawing correlation. Then, the modeling and calculation method of yawing correlation are analyzed. Case study shows that the screening of spatial correlated turbines based on yawing correlation has better effect than that directly based on wind direction. The prediction accuracy based on different correlated turbines is positively related with the degree of yawing correlation, indicating the proposed screening method is effective.
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This paper evaluates the electricity production at Divandareh, a place located at Kurdistan, Iran through wind energy assessment using wind data of the site recorded in duration of a year at three different heights of 10, 30 and 50 meters. For this purpose, a statistical analysis of the measured wind data is performed. According to the US standards, the site is found to be a class-3 wind power site with power density of 336.18 W/m 2 at 50 m height. Moreover, dominant flow direction of wind is checked through wind rose plotting. It has been shown that the site is suitable for wind energy development by installing wind turbines with tall towers. Thereupon, four different commercial wind turbines have been nominated in order for studying. Using characteristics of nominated wind turbines and analyzed wind data, the average power and annual output energy are obtained for each of them. Finally, after determination of capacity and availability factors and comparison of these factors among wind turbines and due to economical criteria, it is achieved that turbine model De Wind 48 has the most appropriateness with the selected site.
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Wind‐turbine operations are associated with bat mortality worldwide; minimizing these fatalities is critically important to both bat conservation and public acceptance of wind‐energy development. We tested the effectiveness of raising wind‐turbine cut‐in speed – defined as the lowest wind speed at which turbines generate power to the utility system, thereby reducing turbine operation during periods of low wind speeds – to decrease bat mortality at the Casselman Wind Project in Somerset County, Pennsylvania, over a 2‐year period. Observed bat mortality at fully operational turbines was, on average, 5.4 and 3.6 times greater than mortality associated with curtailed (ie non‐operating) turbines in 2008 and 2009, respectively. Relatively small changes to wind‐turbine operation resulted in nightly reductions in bat mortality, ranging from 44% to 93%, with marginal annual power loss (≤ 1% of total annual output). Our findings suggest that increasing turbine cut‐in speeds at wind facilities in areas of conservation concern during times when active bats may be at particular risk from turbines could mitigate this detrimental aspect of wind‐energy generation.
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Condition monitoring of wind turbines is gaining importance as turbines become larger and move to more inaccessible locations, such as offshore. Condition monitoring based on methods conventionally used in the power generation industry have been demonstrated to work successfully on large wind turbines when attention is paid to data collection. In view of the large number of wind turbines deployed this paper proposes a methodology for wind turbine condition monitoring that compares conventional condition monitoring signals with operational signals, such as load or energy, which could be applied automatically. A multi-parameter approach, based on comparison of independent signals, should increase confidence in fault signal interpretation and alarms generated, potentially reducing the risk of false alarms.
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Modern utility-scale wind farms consist of a large number of wind turbines. In order to improve the power generation efficiency of wind turbines, accurate quantification of power generation levels of multi-turbines is critical, in both wind farm design and operational controls. One challenging issue is that the power output levels of multiple wind turbines are different, due to complex interactions between turbines, known as wake effects. In general, upstream turbines in a wind farm absorb kinetic energy from wind. Therefore, downstream turbines tend to produce less power than upstream turbines. Moreover, depending on weather conditions, the power deficits of downstream turbines exhibit heterogeneous patterns. This study proposes a new statistical approach to characterize heterogeneous wake effects. The proposed approach decomposes the power outputs into the average pattern commonly exhibited by all turbines and the turbine-to-turbine variability caused by multi-turbine interactions. To capture the wake effects, turbine-specific regression parameters are modeled using a Gaussian Markov random field. A case study using actual wind farm data demonstrates the proposed approach's superior performance.
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The aim of the current paper is to present an approach to a wind turbine selection based on an annual wind measurements. The proposed approach led to a choice of an optimal device for the given wind conditions. The research was conducted for two potential wind farm locations, situated on the north of Poland. The wind measurements pointed out a suitability of the considered localizations for a wind farm development. Six types of wind turbines were investigated in each localization. The power of the wind turbines were in the range of 2.0 to 2.5 MW and with a medium size of the rotor being in the range of 82 to 100 m. The purpose of the research was to indicate a wind turbine with the lowest sensitivity to the variation of wind speed and simultaneously being most effective energetically. The Weibull density distribution was used in the analyses for three values of a shape coefficients k. The energy efficiency of the considered turbines were also assessed. In terms of the hourly distribution of the particular wind speeds, the most effective wind turbines were those with a nominal power of 2 MW, whereas the least effective were those with the nominal power of 2.3–2.5 MW. The novelty of the proposed approach is to analyze the productivity for many types of wind turbines in order to select the one which is the most effective energy producer. The analyses conducted in the paper allowed to indicate a wind turbine which generates the highest amount of energy independently on the wind speed variation.
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This paper examines the effect of different wind turbine classes on the electricity production of wind farms in two areas of Cyprus Island, which present low and medium wind potentials: Xylofagou and Limassol. Wind turbine classes determine the suitability of installing a wind turbine in a particulate site. Wind turbine data from five different manufacturers have been used. For each manufacturer, two wind turbines with identical rated power (in the range of 1.5 MW–3 MW) and different wind turbine classes (IEC II and IEC III) are compared. The results show the superiority of wind turbines that are designed for lower wind speeds (IEC III class) in both locations, in terms of energy production. This improvement is higher for the location with the lower wind potential and starts from 7%, while it can reach more than 50%.
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With the rise of new energy power generation technology, the installed capacity of wind turbines continues to increase. At the same time, the potential faults of wind turbines have also increased with the increase of wind turbines. Therefore, early prediction of potential faults of wind turbines and ensuring the safe and stable operation of wind turbines is of great significance for improving power generation efficiency and reducing maintenance costs. In order to realize the fault early warning of the main bearing of the wind turbine, an early warning method of the main bearing of the wind turbine based on Stacked Auto encoder (SAE) is proposed.
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Power production of a wind turbine system is strongly dependent on both the wind regime at the site and the operating parameters of the wind turbine (design parameters). In order to evaluate the performance of a wind turbine in a given location, an accurate estimation of the turbine's Capacity Factor (CF) is required. This parameter shows the degree of match between the characteristics of the turbine and the wind patterns on the site. In this paper, four widely used empirical models are presented and compared using the method of bins, which is based on the manufacturer-provided power curves. The generic models considered in this paper are Linear Model (LM), Quadratic Model (QM), Cubic Model (CM), and General Model (GM). The validity of these models was investigated using a case study of four locations across Morocco which are namely: Tetouan, Essaouira, Taza and Ouarzazate. Four small scale wind turbines presenting different ranges of characteristic speeds and rated powers (10 kW, 20 kW, and 50 kW) were used to conduct the comparative study. From the obtained results, the recommended models are the Quadratic and the Cubic models. These two models present a good description of the turbines' power curves.
Empirical modelling
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