@Article{ee.2026.081825,
AUTHOR = {Ziwei Zhong, Lingkai Zhu, Yuheng Zhang, Zhiqiang Gong, Wei Zheng, Wenlong Fu},
TITLE = {Vibration Trend Prediction of Pumped Storage Unit Based on IBiGRU-KAN and IESC-KELM},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/27105},
ISSN = {1546-0118},
ABSTRACT = {As a key equipment of the power system, the operational stability of pumped storage units (PSUs) is crucial for the safety and efficiency of the power grid. Since vibration serves as a critical indicator of the operational state and structural health of PSUs, accurate vibration trend prediction plays an important role in ensuring the stable operation. To enhance the prediction accuracy of PSUs vibration trend, a novel prediction method is proposed in this paper based on combination of the improved bidirectional gated recurrent unit Kolmogorov-Arnold network (IBiGRU-KAN) and kernel extreme learning machine optimized by the improved escape algorithm (IESC-KELM). Firstly, variational mode decomposition (VMD) is employed to decompose the original vibration signal into multiple subsequences, effectively reducing its nonlinearity and non-stationarity. Secondly, singular spectrum analysis (SSA) is applied to extract the dominant components of each subsequence, while the residual components are superimposed and reconstructed to preserve critical features. Subsequently, training and test sets are constructed using phase space reconstruction (PSR). Then, IESC-KELM is utilized to predict high-frequency subsequences, while IBiGRU-KAN is utilized to predict low-frequency ones. Ultimately, the prediction results of all subsequences are superposed to obtain the final vibration trend prediction result for PSUs. The case analysis and comparative experiments show that compared with the optimal contrastive model, the proposed model has reduced RMSE, MAE, and MAPE by 0.73%, 6.08%, and 6.55%, respectively, verifying the excellent prediction accuracy and stability of the proposed method.},
DOI = {10.32604/ee.2026.081825}
}