Publications

Google Citation Page

Peer-Reviewed Journal Papers:

  1. G. Jiang, P. Xie, Y. Tang, and H. He, “A Multi-Level-Denoising Autoencoders Approach for Wind Turbines Fault Detection,” in Sustainable Energy, IEEE Transactions on, 2017. (Under revision)
  2. C. Mu, Y. Tang, and H. He, “Improved Sliding Mode Design for Load Frequency Control of Power System Integrated an Adaptive Learning Strategy,” in Industrial Electronics, IEEE Transactions on, 2017. (In press)
  3. G. Jiang, H. He, P. Xie, and Y. Tang, “Stacked Multi-Level-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis,” in Instrumentation & Measurement, IEEE Transactions on, 2017. (In press)
  4. Y. Tang, C. Luo, J. Yang, and H. He, “A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration,” in IEEE/CAA Journal of Automatica Sinica, 2017. (In press)
  5. Y. Guo, X. Li, Y. Tang, and J. Li, “Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks,” in Mathematical Problems in Engineering Journal, vol. 2017, Article ID 4143638, 12 pages, 2017.
  6. G. Weng, F. Huang, Y. Tang, J. Yan, and H. He, “Fault-tolerant Location of Transient Voltage Disturbance Source for DG Integrated Smart Grid,” in Electric Power Systems Research Journal, Volume 144, Pages 13-22, March 2017.
  7. J. Yan, H. He, X. Zhong, and Y. Tang, “Q-learning Based Vulnerability Analysis of Smart Grid against Sequential Topology Attacks,” in Information Forensics and Security, IEEE Transactions on, vol. 12, no. 1, pp. 200-210, Jan. 2017.
  8. L. Dong, Y. Tang, C. Sun, and H. He, “An Event-Triggered Approach for Load Frequency Control with Supplementary ADP,” in Power Systems, IEEE Transactions on, vol. 32, no. 1, pp. 581-589, Jan. 2017.
  9. Y. Tang, C. Mu, and H. He, “SMES Based Damping Controller Design Using Fuzzy-GrHDP Considering Transmission Delay,” in Applied Superconductivity, IEEE Transactions on, vol. 26, no. 7, pp. 1-6, Oct. 2016.
  10. L. He, J. Yang, J. Yan, Y. Tang, and H. He, “A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles,” in Applied Energy, Volume 168, Pages 179-192, 15 April 2016.
  11. Y. Tang, Z. Ni, X. Zhong, H. He, D. Zhao, and X. Xu, “Fuzzy-based goal representation adaptive dynamic programming,” in Fuzzy Systems, IEEE Transactions on, vol. 24, no. 5, pp. 1159-1175, Oct. 2016.
  12. Y. Tang, H. He, Z. Ni, and J. Wen, “Adaptive dynamic modulation for DFIG and STATCOM with HVDC transmission” in Neural Networks and Learning Systems, IEEE Transactions on, vol. 27, no. 8, pp. 1762-1772, Aug. 2016.
  13. Z. Ni, Y. Tang, X. Sui, H. He, and J. Wen, “An adaptive neuro-control approach for multi-machine power systems,” in International Journal of Electrical Power Energy Systems, vol. 75, pp. 108-116, Feb. 2016.
  14. Y. Tang, J. Yang, J. Yan, and H. He, “Intelligent load frequency controller using GrADP for island smart grid with electric vehicles and renewable resources,” in Neurocomputing, vol. 170, no. 1, Dec. 2015.
  15. Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Joint substation-transmission line vulnerability assessment against the smart grid,” in Information Forensics and Security, IEEE Transactions on, vol. 10, no. 5, pp. 1010-1024, May 2015.
  16. Y. Tang, H. He, J. Wen and J. Liu, “Power system stability control for a wind farm based on adaptive dynamic programming,” in Smart Grid, IEEE Transactions on, vol. 6, no. 1, pp. 166-177, Jan. 2015. (Ranked No. 8 in citation from Web of Science for all the papers published in IEEE TSG since 2015)
  17. J. Yan, Y. Tang, H. He, and Y. Sun, “Cascading failure risk assessment with DC power flow model and transient stability analysis,” in Power Systems, IEEE Transactions on, vol. 30, no. 1, pp. 285-297, Jan. 2015. (Ranked No. 13 in citation from Web of Science for all the papers published in IEEE TPS since 2015)
  18. J. Yang, F. Xin, Y. Tang, J. Yan, H. He, and C. Luo, “A power system optimal dispatch strategy considering the flow of carbon emissions and large consumers,” in Energies, vol. 8, no. 9, pp. 9087-9106, 2015.
  19. J. Yang, L. Gong, Y. Tang, J. Yan, H. He, L. Zhang, and G. Li, “An improved SVM-based cognitive diagnosis algorithm for operation states of distribution grid,” in Cognitive Computation, vol. 7, no. 5, pp. 582-593, 2015.
  20. J. Yang, Z. Zeng, Y. Tang, J. Yan, H. He, and Y. Wu, “Load frequency control in isolated micro-grids with electrical vehicles based on multi-variable generalized predictive theory,” in Energies, vol. 8, no. 3, pp. 2145-2164, 2015.
  21. Y. Tang, H. He, Z. Ni, J. Wen, and X. Sui, “Reactive power control of grid-connected wind farm based on adaptive dynamic programming,” in Neurocomputing, vol. 125, no. 1, pp. 125-133, 2014.
  22. X. Sui, Y. Tang, H. He, and J.Wen, “Energy-storage-based low-frequency oscillation damping control using particle swarm optimization and heuristic dynamic programming,” in Power Systems, IEEE Transactions on, vol. 29, no. 5, pp. 2539-2548, Sept. 2014.
  23. Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H He, “Resilience analysis of power grids under the sequential attack,” in Information Forensics and Security, IEEE Transactions on, vol. 9, no. 12, pp. 2340-2354, Dec. 2014.
  24. Y. Tang, P. Ju, H. He, C. Qin, and F.Wu, “Optimized control of DFIG-based wind generation using sensitivity analysis and particle swarm optimization,” in Smart Grid, IEEE Transactions on, vol. 4, no. 1, pp. 509-520, March 2013.

Peer-Reviewed Conference Papers:

  1. Y. Tang, and J. Yang, “Dynamic Event Monitoring Using Unsupervised Feature Learning Towards Smart Grid Big Data,” in International Joint Conference on Neural Networks (IJCNN), 2017.
  2. Y. Tang, C. Mu, and H. He, “Near-Space Aerospace Vehicles Attitude Control Based on Adaptive Dynamic Programming and Sliding Mode Control,” in International Joint Conference on Neural Networks (IJCNN), 2017.
  3. Y. Tang and H. He, “Inter-Connected Power System Frequency Stability with Wind Penetration by Using Fuzzy-GrHDP,” in Power and Energy Society General Meeting (PESGM), 2017. (Nominated for Best Paper Award)
  4. J. Yan, Y. Tang, B. Tang, H. He, and Y. Sun “Power Grid Resilience Against False Data Injection Attacks,” in Power and Energy Society General Meeting (PESGM), 2016.
  5. C. Mu, Y. Tang, and H. He, “Observer-Based Sliding Mode Frequency Control for Micro-Grid with Photovoltaic Energy Integration,” in Power and Energy Society General Meeting (PESGM), 2016.
  6. C. Luo, J. Yang, Y. Tang, H. He, and M. Liu, “Chance constraint based risk-aware optimal power flow for cascading failure prevention,” in Power and Energy Society Transmission & Distribution Conference and Exposition (PES T&D), 2016.
  7. Y. Tang, H. He, and J. Wen, “Optimal operation for energy storage with wind power generation using adaptive dynamic programming,” in Power and Energy Society General Meeting (PESGM), 2015.
  8. Y. Tang, C. Mu, and H. He, “Superconducting magnetic energy storage based power system control using adaptive dynamic programming,” Proceedings of 2015 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2015.
  9. J. Yan, Y. Tang, Y. Zhu, Y. Sun, and H. He, “Smart grid vulnerability under cascade-based sequential line-switching attacks,” in Global Communications Conference (GLOBECOM), 2015 IEEE, Dec. 2015.
  10. Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Diversities of cascading failure processes in electric grids,” in Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, 18-20 Feb. 2015.
  11. Y. Tang, X. Zhong, Z. Ni, J. Yan, and H. He, “Impact of signal transmission delays on power system damping control using heuristic dynamic programming,” in Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, 9-12 Dec. 2014.
  12. Z. Ni, Y. Tang, H. He, and J. Wen, “Multi-machine power system control based on dual heuristic dynamic programming,” in Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, 9-12 Dec. 2014.
  13. X. Zhong, Z. Ni, Y. Tang, and H. He, “Data-driven partially observable dynamic processes using adaptive dynamic programming,” in Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014 IEEE Symposium on, 9-12 Dec. 2014.
  14. Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “Coordinated attacks against substations and transmission lines in power grids,” in Global Communications Conference (GLOBECOM), 2014 IEEE, pp. 655-661, 8-12 Dec. 2014.
  15. Y. Su, J. Liu, S. Liao, Y. Tang, J. Fang, J.Wen, and H. He, “Transient over-voltage control for a wind farm based on goal representation adaptive dynamic programming,” in Power System Technology (POWERCON), 2014 International Conference on, pp. 705-712, 20-22 Oct. 2014.
  16. Y. Tang, J. Yang, J. Yan, Z. Zeng, and H. He, “Frequency control using on-line learning method for island smart grid with EVs and PVs,” in Neural Networks (IJCNN), 2014 International Joint Conference on, pp. 1440-1446, 6-11 July 2014.
  17. Y. Zhu, J. Yan, Y. Tang, Y. Sun, and H. He, “The sequential attack against power grid networks,” in IEEE International Conference on Communications (ICC), Sydney, Australia, Jun. 10-14, 2014. (Best Paper Award)
  18. Y. Tang, H. He, and J. Wen, “Optimized control of DFIG based wind generation using swarm intelligence,” Power and Energy Society General Meeting (PES), 2013 IEEE, 21-25 July 2013.
  19. Y. Tang, H. He, and J. Wen, “Comparative study between HDP and PSS on DFIG damping control,” Computational Intelligence Applications In Smart Grid (CIASG), 2013 IEEE Symposium on, pp.59-65, 16-19 April 2013.
  20. Y. Tang, H. He, and J. Wen, “Adaptive control for an HVDC transmission link with FACTS and a wind farm,” Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES, 24-27 Feb. 2013.
  21. Y. Tang, S. Fu, B. Tang, and H. He. “A modified PSO based particle filter algorithm for object tracking,” In SPIE Defense, Security, and Sensing, pp. 87500S-87500S. International Society for Optics and Photonics, 2013.
  22. B. Tang, S. Fu, Y. Tang and H. He, “Robust multiple objects tracking: particle filter with ePSO,” International Conference on Cognitive and Neural Systems (ICCNS), Boston, 2013.
  23. X. Fang, H. He, Z. Ni, and Y. Tang, “Learning and control in virtual reality for machine intelligence,” Intelligent Control and Information Processing (ICICIP), 2012 Third International Conference on, pp.63-67, 15-17 July 2012.
  24. Y. Tang, H. He, and J. Wen, “Power system stabilization with high wind power penetration using hierarchical ADP control,” International Conference on Cognitive and Neural Systems (ICCNS), Boston, 2012.