ArticleScientific reports2024
Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Temporal-aware transformer networks for state of charge multi-output prediction in unmanned aerial vehicle lithium-ion batteries.Scientific reports · 2025Article
- Nonlinear degradation modeling and remaining useful life prediction for electric drive system with multiple failure modes.Scientific reports · 2025Article
- Fault Diagnosis Method for Pumping Station Units Based on the tSSA-Informer Model.Sensors (Basel, Switzerland) · 2025Article
- A hybrid approach for lithium-ion battery remaining useful life prediction using signal decomposition and machine learning.Scientific reports · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods.
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