刘晓明，博士，助理教授，硕士生导师，IEEE Member、IEEE IAS Member，担任IEEE Transactions on Sustainable Energy、Energy Conversion and Management等期刊审稿人。2025年获西安交通大学优秀毕业生干部称号。近年来参与国家重点研发计划课题3项、国家级产教融合平台建设项目1项，发表SCI/EI论文20余篇。2025年6月加入大连理工大学电气工程学院，隶属于李卫星教授团队。

主要研究方向：

    1.电力系统风险评估：基于贝叶斯深度学习的电力设备故障概率预测、新能源出力概率预测、电力系统母线电压、线路负载率概率预测。

    2.智能电网：基于深度强化学习的电力系统调度与控制、大模型在电力系统运行中的应用。

教育经历：

    2015.09-2019.06 西安交通大学电气工程学院 本科

    2019.09-2025.06 西安交通大学电气工程学院 博士研究生

招生信息：

    本人重点关注人工智能前沿研究在电力系统分析、运行与控制方面的应用，欢迎有数学建模竞赛、电子设计竞赛经验、代码能力强的学生加入本团队，尤其欢迎有兴趣参与大模型研究的同学加入！

代表性论文：

        1. X. Liu, J. Liu, Y. Zhao, et al., “A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions,” IEEE Transactions on Industrial Informatics, vol. 20, no. 2, pp. 1516-1527, Feb. 2024. (SCI收录, Top, 一作, 影响因子11.7)

2. X. Liu, J. Liu, J. Liu, et al., "A Bayesian Deep Learning-based Wind Power Prediction Model Considering the Whole Process of Blade Icing and De-icing," IEEE Transactions on Industrial Informatics, vol. 20, no. 7, pp. 9141-9151, July 2024. (SCI收录, Top, 一作, 影响因子11.7)

3. X. Liu, J. Liu, Y. Zhao, et al., “A Bayesian Deep Learning-based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle,” IEEE Transactions on Sustainable Energy, vol. 15, no. 4, pp. 2663-2674, Oct. 2024.  (SCI收录, Top, 一作, 影响因子8.6)

4. X. Liu, J. Liu, Y. Zhao, et al., “Dynamic Modeling of Gasbag-Structured Compressed Supercritical Carbon Dioxide Energy Storage,” IEEE Transactions on Sustainable Energy, vol. 16, no. 3, pp. 2251-2254, July 2025 . (SCI收录, Top, 一作, 影响因子8.6)

5. X. Liu, J. Liu, Y. Zhao, et al., “A Deep Reinforcement Learning-Based Resilience Enhancement Framework for Distribution Networks Under Extreme Weather Events,” CSEE JPES, 2022.  (SCI收录, 一作, 影响因子6.9)

6. Y. Zhao, J. Liu, X. Liu, et al., "Enhancing the Tolerance of Voltage Regulation to Cyber Contingencies via Graph-based Deep Reinforcement Learning," IEEE Transactions on Power Systems, vol. 39, no. 2, pp. 4661-4673, March 2024. (SCI收录, Top, 学生二作, 影响因子6.5)

7. J. Liu, J. Liu, X. Liu, et al., "Discriminative Signal Recognition for Transient Stability Assessment via Discrete Mutual Information Approximation and Eigen Decomposition of Laplacian Matrix," IEEE Transactions on Industrial Informatics, vol. 20, no. 4, pp. 5805-5817, April 2024. (SCI收录, Top, 学生二作, 影响因子11.7)

8. Y. Zhao, J. Liu, X. Liu, Y. Nie, J. Liu and C. Chen, "LDM: A Generic Data-Driven Large Distribution Network Operation Model," IEEE Transactions on Smart Grid, vol. 15, no. 4, pp. 4284-4287, July 2024. (SCI收录, Top, 学生二作, 影响因子8.6)

9. Y. Zhao, J. Liu, X. Liu, et al., "Distribution Network Expansion-Friendly Adaptive Deep Reinforcement Learning for Inverter-Based Volt-Var Control," IEEE Transactions on Smart Grid, 2025. (SCI收录, Top, 学生二作, 影响因子8.6)

10. Y. Nie, J. Liu, X. Liu, et al., "Asynchronous Multi-Agent Reinforcement Learning-Based Framework for Bi-Level Noncooperative Game-Theoretic Demand Response," IEEE Transactions on Smart Grid, vol. 15, no. 6, pp. 5622-5637, Nov. 2024. (SCI收录, Top, 学生二作, 影响因子8.6)

11. Z. Yang, L. Li, X. Liu, et al. "Efficient deposition of Pt nanoparticles on TiO2 nanosheets by regulating the defect concentration: Strengthening MSI for enhancing dehydrogenation of dodecahydro-N-ethylcarbazole,". Chemical Engineering Journal, vol. 474, no. 15, pp. 145896, Oct. 2023, (SCI收录, Top, 学生二作, 影响因子13.4)

12. 刘晓明,刘俊,姚宏伟,等.基于VSG的风光水火储系统频率调节深度强化学习方法.电力系统自动化, 2025,49(09):114-124. (EI卓越期刊, 一作)