Author(s): Yujie Liu; Jie Liu; Fengyuan Zhang; Hua Gao; Yuxin Li; Xiaohui Yuan

Linked Author(s): Jie Liu

Keywords: Health condition assessment; Hydropower unit; Data-model interaction; Long short-term memory; Performance degradation index

Abstract: The health condition assessment (HCA) of hydropower unit plays an important role in enhancing safe operation of hydropower stations and reducing maintenance costs. Due to the harsh environment, there are some problems of sensor data during the unit operations, including abnormal data, missing data and high-noise data. Also, it is difficult to obtain complete monitoring data during run-to-failure maintenance. These issues definitely cause the low-confidence HCA. Thus, a data-model-interactive HCA for hydropower unit was proposed. First, a high-fidelity 3D mechanism simulation model, interacting with the actual unit, was built using unit drawing data provided by manufacturers. Average values of the volute inlet pressure and draft tube outlet pressure were fed into the dynamics simulation model to obtain the simulated pressure pulsation data under working conditions (water head H and power P). Then, a long short-term memory based healthy condition model was constructed using the power parameters, simulation and normal pressure pulsation. After model construction, difference values between simulated pressure pulsation dataset and degradation state pressure pulsation dataset were calculated to build the performance degradation index (PDI), describing the HCA of units. Finally, the PDI was fed into the convolutional neural networks and long short-term memory model to achieve degradation trend prediction. Validation experiment was conducted to verify the effectiveness of proposed method using actual monitoring data and operating parameters of 6# unit in the hydropower station.

DOI: https://doi.org/10.1088/1742-6596/2752/1/012020

Year: 2023