An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

Numerical study of a case of the free discharge of water through the turbine with a braked runner

The paper presents a numerical study of the free discharge of water through the turbine with a braked runner. The simulation was carried out for a unit of a full-scale Francis turbine. The finite volume method was employed for unstructured meshes using the DES method. The simulation results show the flow structures, integral characteristics, and pressure pulsations in the flow path. The analysis of the applicability of this approach to real conditions is carried out.

Large-eddy simulations of a swirling flow in a model Francis turbine

We performed Large-eddy simulations of the flow in a model air Francis turbine in a range of low-load regimes with a swirler rotating at fixed frequency. All investigated regimes revealed the presence of coherent helical vortex structure in the draft tube: the precessing vortex core. We identified the frequency of this instability and obtained mean flow velocity fields to be utilized in further works.