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Markov Modeling of the Availability of a Wind Turbine Utilizing Failures and Real Weather Data

机译:利用故障和真实天气数据的风力发电机可用性的马尔可夫模型

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The maintenance and reliability issues of the wind farms are a matter of high importance both for the offshore and onshore constructions. The technology applied in the wind turbines is fast growing and the wind farms expand rapidly all over the world. European Union countries have set high targets for electricity production through wind till 2020. The maintenance is very important part for a wind farm because we can prevent very important faults even disasters in vital parts of the Wind Turbines (WTs) that may cost hundred thousand euros and lead to long downtimes. Condition monitoring methods are applied in order to prevent high cost damages, save money from maintenance and increase the availability of the system. Moreover, the weather phenomena play a very important role for both the operation and maintenance of the system. For example, in the case of wind or bad weather we cannot have access for maintenance and repair, as a result the downtime increases. The weather parameter is more important in offshore wind farm because the accessibility is more difficult and costly. Using Markov chains we develop a model describing the availability of a wind turbine considering the wind intensity and the operational condition or the downtime of a wind turbine.
机译:风电场的维护和可靠性问题对于海上和陆上建设都至关重要。风力涡轮机中应用的技术正在迅速发展,风力发电场在世界范围内迅速扩展。欧盟国家已经设定了到2020年通过风能发电的高目标。维护是风电场的重要组成部分,因为我们可以防止非常重要的故障,甚至是风力涡轮机(WT)重要部分的灾难,这可能会花费数十万欧元并导致长时间的停机。为了防止高昂的成本损失,节省维护成本并提高系统可用性,应用了状态监视方法。而且,天气现象对于系统的运行和维护都起着非常重要的作用。例如,在大风或恶劣天气的情况下,我们无权进行维护和维修,结果是停机时间增加了。天气参数在海上风电场中更为重要,因为可访问性更困难且成本更高。使用马尔可夫链,我们开发了一个模型,该模型描述了考虑到风力强度和运行状况或风力发电机组停机时间的风力发电机组的可用性。

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