Ren Chao (IGS)
Lab Position: Ph.D. Students (2017, Fall)Email: renc0003@e.ntu.edu.sg
Office: Singtel Cognitive and Artificial Intelligence Lab (SCALE@NTU)
Research Topic: Machine Learning, Security Assessment, Robustness Verification, and Interpretability.
Brief Biography:
Received the B.E. degree from the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2017. He is currently working toward the Ph.D. degree supervised by Assoc Prof. Xu Yan (EEE), Asst Prof. Arijit Khan (SCSE), and Assoc Prof. Teoh Eam Khwang from the Interdisciplinary Graduate School, Nanyang Technological University, Singapore. His research interests include machine learning, data-analytics, optimization, security assessment and its applications to smart grid.
Education:2013 - 2017: BSc, School of Computer Science, Nanjing University of Aeronautics & Astronautics, China
2017 - 2021: PhD, Interdisciplinary Graduate School, Nanyang Technological University, Singapore
Competitions:
[1] Golden Awards (Ranking: Top 300 among 2.28 million projects, award rate around 0.01%), The 7th China International College Students' “Internet+” Innovation and Entrepreneurship Competition, Oct. 2021. (Sponsors: Ministry of Education, the United Front Work Department of CPC Central Committee, Office of the Central Cyberspace Affairs Commission, National Development and Reform Commission, Ministry of Industry and Information Technology, Chinese Academy of Sciences, Chinese Academy of Engineering, National Intellectual Property Administration)
[2] Winnner of Open-Submission Award (Top 5) of 2020 NeurIPS Competition, 'L2RPN in a sustainable world' Challenge, Nov. 2020
[3] Robustness Track (Ranking: 9/209); Adaptability Track (Ranking: 7/147) of 2020 NeurIPS Competition, 'L2RPN in a sustainable world' Challenge, Nov. 2020
[4] Champion Outstanding Winner (Ranking: 1/5254), 8th Chinese Software Cup, July. 2019. (¥ 80,000 CNY) (The Organizing Committee of 'Chinese Software Cup' Software Design Competition for College Students which is co-hosted by Chinese Ministry of Industry and Information Technology, and Chinese Ministry of Education)
[5] Silver Awards (Ranking: 7/419) of 1st Industrial APP Design Competition, Jun. 2019. (¥ 8,000 CNY)
[6] Golden Awards (Ranking: 8/4093), 5th Chinese Software Cup, Aug. 2016 (¥ 10,000 CNY)
[7] Golden Awards of Information Security Competition, May. 2016
[8] Silver Awards o of Computer Design Competition, Apr. 2016
[9] Silver Awards of 10th Programming Competition, He Rong Cup, Jan. 2016
[10] Excellence Award of 10th Information Retrieval Contest, Wan Fang Data Cup, May. 2015
Publications:
[1] C. Ren, and Y. Xu, "Robustness Verification for Machine Learning-based Power System Dynamic Security Assessment Models under Adversarial Examples," IEEE Transactions on Control of Network Systems, accepted in Dec 2021.
[2] C. Ren, X. Du, Y. Xu, Q. Song, Y. Liu and R. Tan, "Vulnerability Analysis, Robustness Verification, and Mitigation Strategy for Machine Learning-based Power System Stability Assessment Model under Adversarial Examples," IEEE Transactions on Smart Grid, accepted in Oct 2021.
[3] C. Ren, Y. Xu, and R. Zhang, "An Interpretable Deep Learning Method for Power System Transient Stability Assessment via Tree Regularization," IEEE Transactions on Power Systems, accepted in Oct 2021.
[4] C. Ren, Y. Xu, B. Dai, and R. Zhang, "An Integrated Transfer Learning Method for Power System Dynamic Security Assessment for Unlearned Faults with Missing Data," IEEE Transactions on Power Systems, accepted in May 2021.
[5] Q. Song, R. Tan, C. Ren and Y. Xu, "Understanding Credibility of Adversarial Examples against Smart Grid: A Case Study for Voltage Stability Assessment," The 12th ACM International Conference on Future Energy Systems (e-Energy), June 28 - July 2, 2021, Torino, Italy. (Acceptance ratio: 17/75=22%).
[6] C. Ren, Y. Xu, J. Zhao, R. Zhang and T. Wan "A Super-Resolution Perception-based Incremental Learning Approach for Power System Voltage Stability Assessment with Missing PMU Measurements," CSEE Journal of Power and Energy Systems, accepted in Mar. 2021.
[7] W. Liu, C. Ren, and Y. Xu, "PV Generation Forecasting with Missing Data: A Super-Resolution Perception Approach," IEEE Trans. Sustainable Energy, accepted in Aug. 2020.
[8] C. Ren, R. Zhang, Y. Zhang, and Z.Y. Dong, "Hybrid Randomized Learning-based Probabilistic Data-Driven Method for Fault-Induced Delayed Voltage Recovery Assessment of Power Systems," IET Gen., Transm., Distrib., 2020.
[9] Q. Li, Y. Xu, and C. Ren, "A Hierarchical Data-Driven Method for Event-based Load Shedding Against Fault-Induced Delayed Voltage Recovery in Power Systems," IEEE Transactions on Industrial Informatics, 2020.
[10] C. Ren, and Y. Xu, "An Incremental Broad Learning Method for Real-Time Updating of Data-Driven Power System Dynamic Security Assessment," IET Gen., Transm., Distrib., 2020.
[11] C. Ren, and Y. Xu, "Transfer Learning-based Power System Online Dynamic Security Assessment: Using One Model to Assess Many Unlearned Faults," IEEE Transactions on Power Systems, 2019.
[12] C. Ren, Y. Xu, Y. Zhang, and R. Zhang, "A Hybrid Randomized Learning System for Temporal-Adaptive Voltage Stability Assessment of Power Systems," IEEE Transactions on Industrial Informatics, 2019.
[13] C. Ren, Y. Xu, "A Fully Data-Driven Method based on Generative Adversarial Networks for Power System Dynamic Security Assessment with Missing Data," IEEE Transactions on Power Systems, 2019.
[14] Q. Li, Y. Xu, C. Ren, and J. Zhao, "A Hybrid Data-Driven Method for Online Power System Dynamic Security Assessment with Incomplete PMU Measurements," in Proc.2019 IEEE Power & Energy Society General Meeting (PESGM), USA, Aug, 2019.
[15] C. Ren, Y. Xu, and Y. Zhang, "Post-disturbance transient stability assessment of power systems towards optimal accuracy-speed tradeoff," Protection and Control of Modern Power Systems, 2018.
[16] C. Ren, Y. Xu, Y. Zhang, C. Hu, "A multiple randomized learning based ensemble model for power system dynamic security assessment, " Proc. 2018 IEEE PES General Meeting (PESGM), Portland, OR, USA, Aug, 2018.