A Multistep Prediction Model for the Vibration Trends of Hydroelectric Generator Units Based on Variational Mode Decomposition and Stochastic Configuration Networks.

Accurately predicting the changes in turbine vibration trends is a key part of the operational condition maintenance of hydropower units, which is of great significance for improving both the operational condition and operational efficiency of hydropower plants. In this paper, we propose a multistep prediction model for the vibration trend of a hydropower unit. This model is based on the theoretical principles of signal processing and machine learning, incorporating variational mode decomposition (VMD), stochastic configuration networks (SCNs), and the recursive strategy. Firstly, in view of the severe fluctuations of the vibration signal of the unit, this paper decomposes the unit vibration data into intrinsic mode function (IMF) components of different frequencies by VMD, which effectively alleviates the instability of the vibration trend. Secondly, an SCN model is used to predict different IMF components. Then, the predicted values of all the IMF components are superimposed to form the prediction results. Finally, according to the recursive strategy, a multistep prediction model of the HGU's vibration trends is constructed by adding new input variables to the prediction results. This model is applied to the prediction of vibration data from different components of a unit, and the experimental results show that the proposed multistep prediction model can accurately predict the vibration trend of the unit. The proposed multistep prediction model of the vibration trends of hydropower units is of great significance in guiding power plants to adjust their control strategies to reach optimal operating efficiency.

Sources, Fraction Distribution and Health Risk Assessment of Selenium (Se) in Dashan Village, a Se-Rich Area in Anhui Province, China.

Soil, rock, potable water, animal food and human hair samples were collected from the Dashan village, a typical selenium (Se)-rich area of China. Se content and fraction distribution were determined to trace the source of soil Se and evaluate the potential health risk to humans. Total Se contents in soils ranged from 0.60 to 10.46 mg kg- 1. The fractions of soil Se followed the order: residual Se (R-Se) > organic-bound Se (O-Se) > acid soluble Se (A-Se) > exchangeable Se (E-Se) > water soluble Se (W-Se). Total Se contents in rocks ranged from 0.07 to 24.8 mg kg- 1. The dietary Se intake of local residents was estimated to be 261.2 µg day- 1 and hair Se content varied from 0.34 to 1.35 mg kg- 1, suggesting that the potential health risk should be concerned. Weathering of carbonaceous rock was speculated to be the primary source of soil Se according to the contents of Se in rocks, the distribution of Se in soil profiles and the relationships between Se and other elements in soils and parent rocks.