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On-Line Detection and Evaluation of Cavitation in Large Kaplan Turbines Based on Sound Wave

机译:基于声波的大型卡普兰汽轮机空化在线检测与评估

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The work is dedicated to the development of an on-line monitoring and analysis system of cavitation in large Kaplan turbines (TrbMAU) in order to evaluate the cavitation degree in real time. Sound wave emitted by cavitation is continuously monitored, including audible sound and ultrasound.Considering the influence of the operating states of turbine-generator sets on cavitation, adaptive data acquisition (DAQ) and storage is proposed. The DAQ period and storage vary with operating states to capture all sound features in different operating states with less data redundancy.Based on the real-time evaluation of the signal characteristics, such as standard deviation, noise level, and frequency compositions, the tendency of cavitation intensity with time and different operating states has been traced out. Furthermore, the integrated cavitation intensity will be estimated periodically, which can figure out the degree of cavitation erosion approximately. And the research methodology and pivotal concerns are discussed on the evaluation of the metal loss caused by cavitation.The TrbMAU has been successfully put into service in Gezhouba Hydro Power Plant. Its performance has been proved to be very good.
机译:这项工作致力于开发大型Kaplan涡轮机(TrbMAU)中的气蚀在线监测和分析系统,以便实时评估气蚀程度。持续监测汽蚀产生的声波,包括可听见的声音和超声。考虑汽轮发电机组运行状态对汽蚀的影响,提出了自适应数据采集(DAQ)和存储技术。 DAQ周期和存储随操作状态的不同而变化,以捕获不同操作状态下的所有声音特征,并减少数据冗余。基于实时评估信号特性(例如标准差,噪声水平和频率组成),空化强度随时间和不同的运行状态已被找出。此外,将对空化强度进行综合估算,从而可以大致计算出空蚀的程度。并就气蚀引起金属损失的评估方法和关键问题进行了讨论。TrbMAU已在葛洲坝水电站成功投入使用。它的性能已被证明是非常好的。

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