An SVDD-based post-processing approach for vibration risk assessment of the hydro-turbine-generator in a large hydropower station

Abstract

Due to the frequently occurred adverse vibration of hydraulic structures, vibration risk assessment is significant for the water energy efficiency of hydropower station and the safety of people and structures. Recently, the abnormal vibration of hydro-turbine-generator in a large hydropower station occurred and the main influencing factors of vibration are analyzed based on the prototype data and engineering experience. Different from the deterministic variable features in traditional support vector domain description (SVDD) algorithms, the feature of vibration amplitude is actually a random variable so that the different target objects will be obtained at different confidence levels. In order to assess the vibration range and excessive vibration probability, the original SVDD boundary at relatively low confidence level is firstly calculated. Then, the boundary extension operation with detailed theoretical deduction is performed and the extended boundary is further optimized inspired by path planning problem. The advantage of proposed approach is that it can improve the data fitting performance for single dimension (i.e. vibration amplitude) without leading to complex boundary which cannot be used for vibration risk assessment. By applying this approach to the practical vibration problem, the quantitative and slightly conservative assessment results are conveniently obtained, which indicate that this approach is reasonable and cost-effective.