Key Operation Data Enhancement Method of Traction Power Supply System Driven by Hybrid Long Short-Term Memory Network and Generative Adversarial Network
Ge Leijiao1,2, Lin Keyuan1, Ren Limiao1
1. School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou 730070 China; 2. School of Electrical and Information Engineering Tianjin University Tianjin 300072 China
Abstract：In modern railway transportation systems, the efficient operation and precise control of traction power supply systems are critical for ensuring safety, reliability, and performance. However, existing data acquisition systems, which operate at second-level frequencies, are insufficient to meet the demands of modern traction power supply systems, particularly in scenarios involving high-speed trains, renewable energy integration, and complex fault conditions. This paper proposes an innovative data enhancement method based on a hybrid model that combines long short-term memory (LSTM) networks and generative adversarial networks (GANs). The proposed method targets two critical issues: (1) the difficulty in capturing complex multivariate correlations among key operational parameters, (2) the inability of existing data enhancement methods to handle the long-term and short-term dependencies in time-series data effectively. Regarding the first challenge, the paper integrates principal component analysis (PCA) and grey relational analysis (GRA) to construct a measurement enhancement matrix. This matrix not only reduces data dimensionality but also thoroughly explores the correlations among variables, thereby enhancing the ability to analyze trends and dynamic changes in the data. For the second challenge, the paper introduces an LSTM-GAN model that leverages the temporal dependency- capturing capabilities of LSTM networks and the high-quality data generation capabilities of GANs. This hybrid model accurately captures the dynamic changes and deep dependency structures in measurement data, achieving high-precision time-series data enhancement. The LSTM-GAN model is designed to overcome the limitations of traditional GANs in handling time-series data. By integrating LSTM networks, the model effectively captures long-term dependencies to analyze transient phenomena such as overvoltage and short-circuit faults during bow-net disconnection. The model’s architecture includes an LSTM module that extracts temporal trends from the input data, which are then used as conditional inputs to the GAN. It allows the generator to produce synthetic data that aligns with the time-series context, ensuring high fidelity to the original data. Additionally, the model incorporates upper and lower bound constraints in the loss function to ensure generated data remains within realistic ranges, further enhancing its applicability to real-world scenarios. The proposed method was validated using real-time data collected from the onboard monitoring system of a high-speed railway section in Northern China. The results demonstrate significant improvements in data accuracy and variable correlation analysis compared to other data enhancement algorithms. Specifically, the absolute error is reduced by an average of 0.019, and the data generation frequency is enhanced from seconds to milliseconds. The mean absolute error (MAE) and mean absolute percentage error (MAPE) for both current and voltage enhancement using the LSTM-GAN model were significantly lower than those of the other methods.
葛磊蛟, 林可愿, 任丽苗. 长短期记忆网络和生成对抗网络混合驱动的牵引供电系统关键运行数据增强方法[J]. 电工技术学报, 2026, 41(2): 607-621.
Ge Leijiao, Lin Keyuan, Ren Limiao. Key Operation Data Enhancement Method of Traction Power Supply System Driven by Hybrid Long Short-Term Memory Network and Generative Adversarial Network. Transactions of China Electrotechnical Society, 2026, 41(2): 607-621.
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