个人简介
崔秋实, IEEE电力与能源协会大数据分析委员会网络论坛任务组副主席，大数据辅导系列论坛创始主席。硕士和博士于2012和2017年分别毕业于美国伊利诺伊理工大学（IIT）和加拿大麦吉尔大学（McGill），在加拿大欧泊实时仿真公司（OPAL-RT）和美国亚利桑那州立大学（ASU）担任过研发工程师和博士后研究员。他的研究紧密结合人工智能，大数据，电力系统和新能源并网，主要研究方向包括电力系统人工智能，电力系统保护与控制，综合能源系统，电动车并网，电网实时仿真与建模等。
崔秋实是加拿大自然科学与工程技术研究(NSERC)博士后基金获得者，加拿大魁北克省自然科技基金(FRQNT)博士后基金获得者。2016-2019年，崔博士分别在英国、美国、中国举办的三个国际会议（第13届IET电力系统保护发展会议，第51届北美电力研讨会，和2019 IEEE可持续能源和电力会议）获得了最佳论文奖，排名均为第一。近五年主持加拿大自然科学基金项目等6项，领导并参与了十多个国家级和省级项目，包括美国自然科学基金，美国能源部，美国能源部先进研究计划署，美国电力系统工程研究中心，美国国家电力科学研究院，美国亚利桑那州盐河水力和电力供应项目，和加拿大魁北克省自然科技基金等。
崔博士拥有丰富的工业及工程经验，为OPAL-RT公司提出了人工智能技术应用于电网保护的通用框架，打通了人工智能算法与硬件在环测试之间的技术壁垒，通过Python API接口编程实现了多场景仿真的自动运行，开发了世界首个Hypersim微电网系统以及基于Matlab Simulink的继电保护模块库，公司该产品的销售额达到每年3000多万美元。崔博士领导的创业团队，开发了新能源电动车充电桩并网规划与运营云计算和分析工具，从全球几百个项目中脱颖而出，入选中国教育部举办的第13届“春晖杯”海外留学生创新创业大赛决赛能源组前六名，获得第三届南通创业创新大赛技术创新奖。
现为李文沅院士团队核心成员，招收博士、硕士等，课题组（AI for Power Systems）拥有良好的科研平台和国际化背景，欢迎新同学加入！
部分代表性工作：
期刊论文：
[1] Y. Weng, Q. Cui*, and M. Guo, “Transform Waveforms into Signature Vectors for General-purpose Incipient Fault Detection,” IEEE Transactions on Power Delivery, doi: 10.1109/TPWRD.2022.3151110.
[2] Q. Cui, G. Kim, and Y. Weng, “Twin-Delayed Deep Deterministic Policy Gradient for Low Frequency Oscillation Damping Control,” Energies, 14(20): 6695, 2021.
[3] T. Chen, C. Gao, H. Hui, Q. Cui, H. Long, “A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis,” Transactions of the Institute of Measurement and Control, Nov. 2021.
[4] S. Phommixay, M.L. Doumbia and Q. Cui, “Comparative analysis of continuous and hybrid binary-continuous particle swarm optimization for optimal economic operation of a microgrid,” Process Integration and Optimization for Sustainability (2021).
[5] A. Arif, K. Imran, Q. Cui and Y. Weng, “Islanding Detection for Inverter-Based Distributed Generation Using Unsupervised Anomaly Detection,” IEEE Access, 2021, 9: 90947-90963.
[6] S. Phommixay, M.L. Doumbia and Q. Cui, “A Two-layer Optimization Approach for Economic Operation of a Microgrid Under a Planned Outage,” Sustainable Cities and Society, Volume 66, Mar. 2021.
[7] T. Chen, Q. Cui, C. Gao, Q. Hu, K. Lai, J. Yang, R. Lyu, H. Zhang, and J. Zhang, “Optimal demand response strategy of commercial building based virtual power plant using reinforcement learning,” IET Generation, Transmission & Distribution, Vol. 15, Issue 16, pp. 2309-2318, Aug. 2021.
[8] Q. Cui, and Y. Weng, “An Environment-adaptive Protection Scheme with Long-term Reward for Distribution Networks,” International Journal of Electrical Power and Energy Systems, 124, p.106350.
[9] Q. Cui, S. M. Yousaf, Y. Weng, and M. Dyer, “Reinforcement Learning Based Recloser Control for Distribution Cables with Degraded Insulation Level,” IEEE Transactions on Power Delivery, vol. 36, no. 2, pp. 1118-1127, April 2021.
[10] Y. Tan, B. Jin, Q. Cui, X. Yue, and A. Sangiovanni-Vincentelli, “Generalizing Fault Detection Against Domain Shifts Using Stratification-Aware Cross-Validation,” arXiv preprint arXiv:2008.08713, 2020.
[11] Q. Cui, and Y. Weng, “Enhance High Impedance Fault Detection and Location Accuracy via µ-PMUs,” IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 797-809, Jan. 2020.
[12] Q. Cui, Y. Weng, and C. W. Tan, “Electric Vehicle Charging Station Placement Method for Urban Areas,” IEEE Transactions on Smart Grid, vol. 10, no. 6, pp. 6552-6565, Nov. 2019.
[13] Q. Cui, K. El-Arroudi, and Y. Weng, “A Feature Selection Method for High Impedance Fault Detection,” IEEE Transactions on Power Delivery, vol. 34, no. 3, pp. 1203-1215, Jun. 2019.
[14] Q. Cui, K. El-Arroudi, and G. Joos, “Islanding Detection of Hybrid Distributed Generation Under Reduced Non-Detection Zone,” IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 5027-5037, Sep. 2018.
[15] Q. Cui, K. El-Arroudi, and G. Joos, “Real-time Hardware-in-the-loop Simulation for Islanding Detection Schemes in Hybrid Distributed Generation Systems,” IET Generation, Transmission & Distribution, vol. 11, no. 12, pp. 3050-3056, Aug. 2017.
