试运行期间风电机组平均故障间隔时间的估计

An Estimation Method of Wind Turbines' Mean Time Between Failures During the Trial Operation Period

试运行期间平均故障间隔时间(mean time between failures,MTBF)是反映风电机组可靠性的重要指标,但由于此期间的运行故障数据样本少且故障停机随机性较强,现有MTBF分析方法的误差较大。针对此种小样本估计问题和故障的随机性,提出了一种利用多台机组运行信息的MTBF估计方法。其基本思路是:根据风电机组安装及其故障数据的特点,构造具有相同配置的多台故障停机的随机截尾数据,对机组的可靠度进行Kaplan-Meier非参数估计;基于这种初步估计结果,再进行二参数威布尔(Weibull)分布拟合,并根据Weibull分布的性质计算得到机组的MTBF。该文利用北方某风电场的试运行数据,对2012年11月投产的20台风电机组进行了MTBF分析计算,结果表明该方法能够有效提高机组试运行期MTBF估计的精度。

Mean time between failures （MTBF） is an important indicator which reflects wind turbines＇ reliability during the trial operation period. However, due to the lack of the failure data during this period and the randomness of downtime, there are larger errors in the existing estimation methods. For such a small sample estimation problem and the randomness of downtime, a MTBF estimation method was proposed, by using multi-unit operation information. The basic idea was： 1） the reliability of .the unit was estimated with Kaplan-Meier non-parametric method, by constructing the random censored fault down data of several units with the same configuration, according to the characteristics of the wind turbine installation and failure data; 2） based on the above preliminary estimating result, the two parameters Weibull distribution was fitted, and then the units＇ MTBF was calculated according to the nature of the Weibull distribution. The MTBF of 20 wind turbines was calculated, which were installed in a wind farm in north China and put into operation in November 2012. The results show that this method can improve the accuracy of MTBF＇s estimation effectively during the trial operation period.