Examination of the sensitivity parameters to detect automatic cavitation in Hydro-turbines of the SefidRood dam considering remaining useful life

Document Type : Research Article

Authors

1 MS.C.Hydraulic structure, Islamic Azad University, Lahijan branch, Lahijan, Iran

2 Ph.d.department of civil engineer, Islamic Azad University, Lahijan branch, Lahijan, Iran

Abstract

In this research، the evaluation of cavitation threshold detection and the automation of the detection process with regard to the Remaining Useful Life (RUL) of the Sefidrood power plant turbine، has been studied. The input of generated model by MATLAB program includes data driven from kaplan hydro turbine located on Tarik hydro power plant. The proposed model is based on 61 features resulting from 6 cavitation sensitivity parameters and 17 operational conditions. For training in MATLAB program, 12 individual data sets and 4095 unique combinations were created and 408 data were selected for examination. The training data combined with sensor rating and cavitation sensitivity feature were employed to predict the cavitation and the best training data set with 98% accuracy. The results showed that the use of a fully automated process for sensitivity determination and cavitation classification was more suitable than the use of a process based on manually selected thresholds. Furthermore, considering the operational conditions and RUL, the automation of determination of cavitation threshold without human intervention was much more accurate.

Keywords


Bajc, B. (2002). “Multidimensional diagnostics of turbine cavitation”, Journal of Fluids Engineering, 124(4), pp. 943-950.
Bajic, B., Korto, C. (2003). “Methods for vibro-acoustic diagnostics of turbine cavitation Méthodes pour le diagnostic vibro-acoustique de la cavitation de turbine”, Journal of Hydraulic Research, 3(41), pp. 87-96.
Cencic, T., Hocevar, M., Sirok, B. (2014). “Study of erosive cavitation detection in pump mode of pump-storage hydropower plant prototype”, ASME, Journal of Fluids Engineering, 136(5), pp. 1-11.
Dorji, U., Ghomashchi, R. (2014). Hydro turbine failure mechanism: An overview, Engineering Failure Analysis, (44), pp. 136-147.
Dular, M. Stofefel, B., Sirok, B. (2006). “Development of cavitation erosion model”, Wear, 261(5-6), pp. 642-655.
Escaler, X., Vikor, E. Franke, H. (2014). “Detection of draft tube surge and erosive blade cavitation in a full-scale Francis turbine”, Journal of Fluids Engineering, 137(1), pp. 103-115.
Francois, L. (2012). “Vibratory detection system of cavitaion erosion: Historic and algorithm validation”, Proceedings of the 8th international symposium on cavitation, pp. 44-65, Singapore.
Holick, M. (2013). Introduction to probability and statistics for engineers, University of California, Berkeley.
Jolliffe, T .(2002). Principal Component Analysis, Second Edition, Encyclopedia of Statistics in Behavioral Science, University of Aberdeen,UK
Rus, T., Dular. M., Marko, H. (2007). “An investigation of relationship between acoustic emission , vibration ,noise and cavitation structures on Kaplan turbine”, Journal of Fluids Engineering, 129(9), pp. 1112-1122.
Wolff, P. (2013). “Evaluation of results from acoustic emission-based cavition monitor”, Grand Coulee Project, Technical report, Hydro Performance Processes, Incpp. 89-99.