Voltage abnormity prediction method of lithium-ion energy storage power station using informer based on Bayesian optimization

• Published in: Scientific reports Vol. 14; no. 1; pp. 21404 - 17
• Main Authors: Rao, Zhibo, Wu, Jiahui, Li, Guodong, Wang, Haiyun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13-09-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/4073; 639/166/987; Bayesian analysis; Bayesian optimization algorithm; Correlation coefficient; Energy storage; Energy storage power station; Humanities and Social Sciences; Informer; Lithium; Mathematical models; Mean square errors; multidisciplinary; Neural networks; Power plants; Prediction models; Sampling; Science; Science (multidisciplinary); Voltage; Voltage abnormity prediction; Energy storage power station; Voltage abnormity prediction; Informer; Bayesian optimization algorithm
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72510-z

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.