Work

Health assessment and deterioration prediction of pumped storage units based on improved KAN and optimal asynchronous characteristic spatial-temporal method

Year: 2025

Type: article

Abstract: It is imperative to conduct a comprehensive health assessment and deterioration prediction of pumped storage units (PSUs) to guarantee the stable operation under prolonged and intricate conditions. To attain the optimal asynchronous health state of each component, a systematic and effective health assessment and prediction method is essential. An optimal asynchronous feature spatial-temporal data acquisition method and an improved Kolmogorov-Arnold network (KAN) network are proposed. Firstly, based on the asynchrony of the monitoring data distribution for each component of the unit, the data with the optimal distribution characteristics of each monitoring component of the unit is integrated to build optimal state health data (OSTHD). Compared with the traditional health data after overhaul, it can reflect the health status of the unit more comprehensively. Secondly, an improved model combining KAN and autoencoder is constructed based on OSTHD training, which has the capability of extracting features from complex, multi-dimensional data. Subsequently, in view of the coordinate vibration data of the monitoring parts of three components, the triangular area deterioration index (ADI) with the ability to reflect the change of the overall performance status of the unit is proposed and verified to be effective. Finally, based on the ADIs and performance degradation indexes, the health degradation trend is predicted using a prediction model combining Gaussian filter and bidirectional gated recurrent unit. A pumped storage power plant in China is used as the research case. The efficiency and feasibility of the presented methodology are demonstrated by comparing the results of several experiments.

Source: Journal of Energy Storage

DOI: 10.1016/j.est.2025.117281

Authors: Bo Fu, Yunpeng Yang, Ani Song, Chaoshun Li, Xilin Zhao, Jiankang Li

Institutions: Hubei University of Technology, Huazhong University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,560