摘要
风电齿轮传动系统是风力发电设备中关键部件之一,区别于通用齿轮传动系统,风电齿轮传动具有几个特点：1.输入载荷具有低转速大扭矩特点；2.由于风速变化导致齿轮载荷非恒定；3.用于可靠性推断的齿轮样本数据十分缺少。因此,风电齿轮传动系统可靠性研究具有较大理论价值及实际意义。为此本文紧密围绕其传动特点,在国内外相关研究基础上,以风电齿轮传动系统为研究对象,重点研究风电齿轮的可靠性并对整个齿轮传动体系的动态可靠性预测展开深入探讨。主要内容如下：
1)针对变风速特点建立非恒定风速条件下齿轮应力概率计算模型。从测风数据入手建立叶片受力概率模型,推导非恒定外载荷条件下齿轮应力波动和风速波动之间的概率特性关系。该模型充分考虑风速稳定波动及突变两种情况下系统输入载荷的随机特性,为齿轮应力验前数据提供计算依据。
2)针对小样本条件下齿轮失效数据缺乏的特点建立多源齿轮数据转换模型。从历史测试数据中选取参数、结构、工况、材料4个因素相似的齿轮数据,改进灰色关联理论并计算各因素对数据转换的影响权重。该模型克服了小样本条件下不能进行可靠性推断的瓶颈限制,为齿轮强度的验前数据提供计算依据。
3)为计算风电齿轮应力及强度时变轨迹建立Bayes数据融合模型。在获取应力、强度验前值及理论计算值的前提下建立数据融合模型以估计应力、强度轨迹参数,在此基础上,建立齿轮应力-强度干涉模型,进而求解其动态可靠性曲线。Bayes数据融合模型综合了验前信息以及理论计算信息,实现了齿轮数据扩容及更新,同时,假设检验准则的提出确保了验前数据的有效性。
4)为求解系统动态可靠性,综合本文齿轮可靠性研究及其他传动部件可靠性研究成果,建立系统动态故障树模型。为简化动态故障树模型,将故障树划分子单元,定义基本特征事件以表征所有底事件的概率特征,由此确定故障树求解策略并求解系统动态可靠度曲线,并通过对比验证了该策略的客观性。
5)最后,为说明本文方法的客观性、科学性、系统性,用工程实例—1.5MW风电齿轮传动系统为计算对象贯穿全文所有理论与方法。从风速统计、应力特性分析、强度数据转换到数据融合,建立系统动态故障树模型并进行求解,得到齿轮传动系统动态可靠性曲线并对计算结果进行分析。实例表明该方法体系既可在设计初期提供设计参考,也可对齿轮传动系统可靠性进行动态预测和跟踪。
Gear transmission system is one of the key components of the wind power generation equipment. Different from the common gear drive, the wind power gear drive has three significant features:firstly, the input load is in characteristic of low speed and large torque; secondly, unsteady gear load caused by the fluctuation of wind speed; thirdly, very scarce effective gear samples for the statistical inference of reliability. So, the research on its dynamic reliability is of great theoretical value and practical significance. For this reason, this dissertation takes the gear transmission system in a wind turbine for an example. In order to form a complete research system and carry out further explorer with consideration of their own characteristics, reliability of the transmission gear is focused on and research results of other transmission parts are combinedthe on basis of domestic and foreign researches. Here are the main works:
1) For the characteristic of variable wind speed, a probability model is presented for the gear stress under the condition of variable wind speed. The probability model of the blade load is studied. In addition, the probability relationship between the fluctuation characteristic of gear stress and the disturbance of wind speed is deduced under the condition of non-constant external load. This model fully considers the random feature of mutational wind speed and provides basis for the calculating of the prior data of gear stress.
2) For the characteristic that the prior data of gear strength used for data fusing is lacking under the conditions of small samples, a model of multi-source data translation is presented. The gear strength data of4similar factors (parameters, structure, working condition, material) is selected from the historical test data. The gray correlation theory is improved and the weighing of each factor is taken into account. This model overcomes the bottleneck that the statistical inference of reliability can't be carried out on the condition of small samples and provides basis for the calculating of the prior data of gear strength.
3) In order to establish the time-varying paths for wind power gear stress/strength, a Bayes data fuse model is presented. In the premise that prior values and theoretical values are acquired, the Bayes data fuse model is used to estimate the parameters of time-varying path for gear stress/strength. On this basis, a stress-strength interference model is established and the dynamic reliability curve of wind power gear is solved. The data fuse model combines the prior and observed information. It realizes update and expansion of the gear data. At the same time, the hypothesis testing guideline ensures equivalence of the prior data.
4) In order to solve dynamic reliability of the whole gear transmission system, dynamic reliability researches of this dissertation and those of other transmission parts are combined to establish the dynamic fault tree model. For the purpose of simplifying the dynamic fault tree model, it is divided into several subunits and basic feature events are defined to generalize the probability characteristics of all basic events. So, the solution strategy is fixed on and the dynamic reliability curve of gear transmission system is solved. Meanwhile, the objectivity of this solution strategy is verified by comparing.
5) In the end, in order to prove the objectivity, scientific, systematic of this work, an engineering case of1.5MW wind gear transmission system is selected to apply all the theory and methods of this dissertation. Establishing the statistic and analysis model for wind speed; establishing the data fusion model; establishing the dynamic fault tree model and solving it. Finally get the dynamic reliability curve of gear transmission system and make a deep analysis. The engineering case indicates that the methodology can not only provide the design reference early in the design, but also dynamically predict and track the reliability of gear transmission system.
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