Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Peng Chen, Yumin Deng, Xuegui Zhang, Li Ma, Yaoliang Yan, Yifan Wu and Chaoshun Li
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Peng Chen: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yumin Deng: China Yangtze Three Gorges Group Co. Ltd., Wuhan 430010, China
Xuegui Zhang: China Yangtze Three Gorges Group Co. Ltd., Wuhan 430010, China
Li Ma: China Yangtze Three Gorges Group Co. Ltd., Wuhan 430010, China
Yaoliang Yan: China Yangtze Three Gorges Group Co. Ltd., Wuhan 430010, China
Yifan Wu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Chaoshun Li: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 2, 1-21

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 ( | A V G | ) and standard deviation ( S T D ) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R 2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

Keywords: pumped storage unit; degradation trend prediction; maximal information coefficient; light gradient boosting machine; variational mode decomposition; gated recurrent unit (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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