Abstract
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.
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Shi, H., Chu, X., Li, Z. (2012). On-Line Detection and Evaluation of Cavitation in Large Kaplan Turbines Based on Sound Wave. In: Zhang, T. (eds) Instrumentation, Measurement, Circuits and Systems. Advances in Intelligent and Soft Computing, vol 127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27334-6_32
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DOI: https://doi.org/10.1007/978-3-642-27334-6_32
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