Abstract
Hydroturbine operators who wish to collect cavitation intensity data to estimate cavitation erosion rates and calculate remaining useful life (RUL) of the turbine runner face several practical challenges related to long term cavitation detection. This paper presents a novel method that addresses these challenges including: a method to create an adaptive cavitation threshold, and automation of the cavitation detection process. These two strategies result in collecting consistent cavitation intensity data. While domain knowledge and manual interpretation are used to choose an appropriate cavitation sensitivity parameter (CSP), the remainder of the process is automated using both supervised and unsupervised learning methods. A case study based on ramp-down data, taken from a production hydroturbine, is presented and validated using independently gathered survey data from the same hydroturbine. Results indicate that this fully automated process for selecting cavitation thresholds and classifying cavitation performs well when compared to manually selected thresholds. This approach provides hydroturbine operators and researchers with a clear and effective way to perform automated, long term, cavitation detection, and assessment.
Highlights
Hydroturbines produce 6.3% of all electrical generation and 48% of renewable energy in the USA (U.S Energy Information Administration, 2015)
Hydroturbines are designed to prevent cavitation from forming under normal running conditions; discussion with hydroturbine operators has revealed several factors outside of the control of designers make eliminating cavitation, and damage caused by cavitation, a difficult task including: 1) available head may change outside of design conditions due to seasonal reservoir variations, floods, or drought; 2) turbulent flow caused by damage or obstructions at the inlet of the hydroturbine; 3) erosion damage on the runner can encourage the formation of cavitation; and 4) the complexity of cavitation formation and collapse makes the amount of damage caused by cavitation difficult to predict in hydroturbines (Dular & Petkovsek, 2015; Jian, Petkovsek, Houlin, Sirok, & Dular, 2015)
The method can be used to identify a cavitation sensitivity parameter (CSP), automate the training and classification process, and keep thresholds relevant through changes in operating conditions. This paper presents both a novel method for creating adaptive cavitation thresholds as well as a machine learning frame
Summary
Hydroturbines produce 6.3% of all electrical generation and 48% of renewable energy in the USA (U.S Energy Information Administration, 2015). This paper presents a method to automatically detect damaging cavitation events using existing installed sensors whose data are used to recalibrate the cavitation detection algorithm using hydroturbine ramp-down or ramp-up. This is of particular interest to hydro plant operators since it eliminates required user input and hydroturbine downtime. Hydroturbines are designed to prevent cavitation from forming under normal running conditions; discussion with hydroturbine operators has revealed several factors outside of the control of designers make eliminating cavitation, and damage caused by cavitation, a difficult task including: 1) available head may change outside of design conditions due to seasonal reservoir variations, floods, or drought; 2) turbulent flow caused by damage or obstructions at the inlet of the hydroturbine; 3) erosion damage on the runner can encourage the formation of cavitation; and 4) the complexity of cavitation formation and collapse makes the amount of damage caused by cavitation difficult to predict in hydroturbines (Dular & Petkovsek, 2015; Jian, Petkovsek, Houlin, Sirok, & Dular, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
