机械与电气工程学院
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| 导师代码： |
21517
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| 导师姓名： |
曹迪
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| 性 别： |
男 |
| 特 称： |
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| 职 称： |
副教授
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| 学 位： |
工学博士学位
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| 属 性： |
专职 |
| 电子邮件： |
caodi@uestc.edu.cn
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| 学术经历：
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2025.03-至今 电子科技大学，特聘副教授；
2022.01-2025.02 电子科技大学，师资博士后（特聘副研究员）；
2016.09-2021.12 电子科技大学，控制科学与工程，博士；
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| 个人简介：
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从事配电网状态感知及运行优化关键技术研究，四川省“天府青城计划”青年科技人才，获得四川省首批“博士后创新人才支持项目”(全省共25人)，连续两年入选斯坦福全球前2%顶尖科学家，荣获中国电机工程学会“青年人才托举计划”，主持国家自然科学基金青年基金、中国博士后科学基金面上基金、粤莞联合基金青年基金以及国家重点实验室开放基金多项。近五年，以第一/通信作者录用/发表顶级IEEE Trans论文22篇；Google引用量3700余次，h指数32，4篇ESI高被引论文；获得2024年度中国仪器仪表学会科技进步二等奖（排第二）、2023年度电力建设科学技术进步奖一等奖；以第二作者身份出版教材《人工智能在电气工程中的应用》（入选科学出版社“十四五”普通高等教育规划教材）；担任SCI期刊Renewable Energy以及IET Renewable Power Generation专刊客座编辑以及多个顶级期刊审稿人。
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| 科研项目：
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近三年，主持国家自然科学基金青年基金、四川省首批“博士后创新人才支持项目”、中国博士后科学基金面上基金、粤莞联合基金青年基金以及国家重点实验室开放基金多项：
1. 国家自然科学基金青年基金，极端高温天气电动汽车规模化接入的城市电网韧性评估与提升策略研究，2025-2027，主持；
2. 四川省博士后创新人才支持项目，计及车-网-荷耦合关联的配电网低碳运行方法研究，2022-2024，主持；
3. 中国博士后科学基金面上项目，规模化电动汽车接入场景下配电-交通耦合网络低碳运行方法研究，2023-2025，主持；
4. 粤莞联合基金青年基金，“路-车-源-荷”多源随机配电-交通耦合网络碳排放流分析与优化方法研究，2023-2026，主持；
5. 新能源与储能运行控制国家重点实验开放基金，考虑时空关联特性的风光储联合优化调度深度强化学习方法研究，2022-2023，主持。
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| 研究成果：
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近五年，以第一/通信作者身份发表/录用的期刊论文如下（*为通信作者）：
[1] Di Cao, Junbo Zhao, Weihao Hu*, et al. Topology change aware data-driven probabilistic distribution state estimation based on Gaussian process[J]. IEEE Transactions on Smart Grid, 2023, 14(2): 1317-1320.
[2] Di Cao, et al. Physics-informed graphical learning and Bayesian averaging for robust distribution state estimation. IEEE Transactions on Power Systems, 2024, 39(2): 2879-2892.
[3] Di Cao, et al. Decentralized graphical-representation-enabled multi-agent deep reinforcement learning for robust control of cyber-physical systems. IEEE Transactions on Reliability, 2024. Doi: 10.1109/TR.2024.3354938.
[4] Di Cao, et al. Physics-informed graphical representation-enabled deep reinforcement learning for robust distribution system voltage control. IEEE Transactions on Smart Grid, 2024, 15(1): 233-246. (ESI 高被引论文)
[5] Di Cao, et al. A multi-agent deep reinforcement learning based voltage regulation using coordinated PV inverters. IEEE Transactions on Power Systems, 2020, 35(5): 4120-4123.
[6] Di Cao, et al. Deep reinforcement learning enabled physical-model-free two-timescale voltage control method for active distribution systems. IEEE Transactions on Smart Grid, 2022, 13(1): 149-165.
[7] Di Cao, et al. Attention enabled multi-agent DRL for decentralized volt-var control of active distribution system using PV inverters and SVCs. IEEE Transactions on Sustainable Energy, 2021, 12(3): 1582-1592.
[8] Di Cao, et al. Data-driven multi-agent deep reinforcement learning for distribution system decentralized voltage control with high penetration of PVs. IEEE Transactions on Smart Grid, 2021, 12(5): 4137-4150.
[9] Di Cao, et al. Robust deep Gaussian process-based probabilistic electrical load forecasting against anomalous events. IEEE Transactions on Industrial Informatics, 2022, 18(2): 1142-1153.
[10] Di Cao, et al. Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning. Applied Energy, 2022, 306: 117982.
[11] Di Cao, et al. Deep reinforcement learning based approach for optimal power flow of distribution networks embedded with renewable energy and storage devices. Journal of Modern Power Systems and Clean Energy, 2021, 9(5): 1101-1110.
[12] Di Cao, et al. Bidding strategy for trading wind energy and purchasing reserve of wind power producer–A DRL based approach. International Journal of Electrical Power & Energy Systems, 2020, 117: 105648.
[13] Di Cao, et al. Reinforcement learning and its applications in modern power and energy systems: a review. Journal of Modern Power Systems and Clean Energy, 2020, 8(6): 1029-1042.
[14] Pengfei Zhao, Di Cao*, Yanbo Wang, et al. Gaussian Process-aided transfer learning for probabilistic load forecasting against anomalous events[J]. IEEE Transactions on Power Systems, 2023, 38(3): 2962-2965.
[15] Sichen Li, Weihao Hu, Di Cao*, et al. EV charging strategy considering transformer lifetime via evolutionary curriculum learning-based multiagent deep reinforcement learning[J]. IEEE Transactions on Smart Grid, 2022, 13(4): 2774-2787.
[16] Pengfei Zhao, Di Cao*, Weihao Hu, et al. Geometric loss-enabled complex neural network for multi-energy load forecasting in integrated energy systems. IEEE Transactions on Power Systems, 2024, 39(4): 5659-5671.
[17] Jiaxiang Hu, Weihao Hu, Di Cao*, et al. Feature graph-enabled graphical learning for robust DSSE with inaccurate topology information. IEEE Transactions on Power Systems, 2024, 39(4): 6091-6094.
[18] Sichen Li, Weihao Hu, Di Cao*, et al. A multiagent deep reinforcement learning based approach for the optimization of transformer life using coordinated electric vehicles[J]. IEEE Transactions on Industrial Informatics, 2022, 18(11): 7639-7652.
[19] P. Zhao, Weihao Hu, Di Cao*, et al., Causal mechanism-enabled zero-label learning for power generation forecasting of newly-built PV sites. IEEE Transactions on Sustainable Energy, doi: 10.1109/TSTE.2024.3459415.
[20] Jiaxiang Hu, Weihao Hu, Di Cao*, et al. Robust multiarea distribution system state estimation based on structure-informed graphic network and multitask Gaussian process. IEEE Transactions on Industrial Informatics, 2024, 20(8): 10599-10612.
[21] Sichen Li, Weihao Hu, Di Cao*, et al. Energy management of multiple microgrids considering missing measurements: A novel MADRL approach, IEEE Transactions on Smart Grid, 2023, 14(5): 4133-4136.
[22] Pengfei Zhao, Weihao Hu, Di Cao*, et al. Probabilistic multi-energy load forecasting based on hybrid attention-enabled transformer network and Gaussian process-aided residual learning. IEEE Transactions on Industrial Informatics, 2024, 20(6): 8379-8393.
[23] Sichen Li, Weihao Hu, Di Cao*, et al. A novel MADRL with spatial-temporal pattern capturing ability for robust decentralized control of multiple microgrids under anomalous measurements. IEEE Transactions on Sustainable Energy, 2024, 15(3): 1872-1884.
[24] Sichen Li, Weihao Hu, Di Cao*, et al. Coordinated operation of multiple microgrids with heat-electricity energy based on a graph surrogate model-enabled robust multi-agent deep reinforcement learning. IEEE Transactions on Industrial Informatics,已录用.
[25] Yinfan. Wang, Weihao Hu, Di Cao*, et al., Local distribution voltage control using large-scale coordinated PV inverters: A novel multi-agent deep reinforcement learning-based approach," IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2025.3533958.
[26] Jiaxiang. Hu, Weihao Hu, Di Cao*, et al., An adaptive noise-resistant learning method for DSSE considering inaccurate label data, IEEE Transactions on Power Systems, vol. 40, no. 2, pp. 1989-1992, March 2025.
[27] Jiaxiang Hu, Weihao Hu, Di Cao*, et al. Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms. Applied Energy, 2024, 355: 122185.
[28] Sichen Li, Weihao Hu, Di Cao*, et al. Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning. Applied Energy, 2023, 346: 121359.
[29] Jiaxiang Hu, Weihao Hu, Di Cao*, et al. Robust distribution system state estimation considering anomalous real-time measurements and topology change, Journal of Modern Power Systems and Clean Energy, doi: 10.35833/MPCE.2024.000683.
[30] 胡家祥,曹迪*,胡维昊,黄琦,陈哲.融入拓扑知识的配电网抗差状态估计图神经网络方法[J].电力系统自动化，2023, 47(10): 84-97.
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| 专业研究方向：
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| 专业名称 |
研究领域/方向 |
招生类别 |
| 080800电气工程 |
01电力系统及其自动化 |
硕士学术学位 |
| 085400电子信息 |
01电气工程，02不区分研究方向（非全） |
硕士专业学位 |
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