Abstract
The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.
Highlights
Pump storage units (PSUs) store excessive power during light load periods and convert hydro energy into electricity at peak load periods [1]
To achieve precise degradation trend prediction for a PSU, a combined Degradation trend prediction (DTP) model of a PSU is proposed based on maximal information coefficient (MIC)-light gradient boosting machine (LGBM) and variational mode decomposition (VMD)-gated recurrent unit (GRU)
|si(t) − pi(t)| pi(t) where si(t) is the monitoring status data, wi1(t), wi2(t), · · ·, wim(t) are the monitoring working parameters selected by the MIC, f denotes the mapping relationship learned by the LGBM healthy model, pi(t) implies the presumptive status data under corresponding working parameters when the PSU is running well, and T denotes the number of points in the ith process
Summary
Pump storage units (PSUs) store excessive power during light load periods and convert hydro energy into electricity at peak load periods [1]. The PDI is obtained after building the healthy model, after which the degradation trend of the PSU should be predicted to support decision-making. The complex PDI sequences are decomposed into simpler modes by VMD before being fed into GRU to improve the accuracy of prediction. To achieve precise degradation trend prediction for a PSU, a combined DTP model of a PSU is proposed based on MIC-LGBM and VMD-GRU. The PDI sequence is sent into the VMD-GRU prediction model to obtain a reliable future degradation trend. (b) Inspired by the superiority of LGBM, the healthy model is constructed and achieves a high-precision fitting result and consumes fewer computational resources as it has a strongly competitive training speed.
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