Zefan Tang

Jiang, Z., Tang, Z., Zhang, P., & Qin, Y. (2021). Programmable Adaptive Security Scanning for Networked Microgrids. Engineering, 7(8), 14. http://doi.org/10.1016/j.eng.2021.06.007

Jiang, Z., Tang, Z., Qin, Y., , & Zhang, P. (2021). <scp>Quantum</scp> internet for resilient electric grids. International Transactions on Electrical Energy Systems, 31(6). http://doi.org/10.1002/2050-7038.12911

Wang, J., Qin, Y., Tang, Z., & Zhang, P. (2021). Software-Defined Cyber–Energy Secure Underwater Wireless Power Transfer. IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2(1), 11. http://doi.org/10.1109/JESTIE.2020.3039107

Tang, Z., Qin, Y., Jiang, Z., Krawec, W. O., & Zhang, P. (2020). Quantum-Secure Networked Microgrids. In 2020 IEEE Power & Energy Society General Meeting (PESGM) (p. 5). http://doi.org/10.1109/PESGM41954.2020.9281884

Tang, Z., Qin, Y., Jiang, Z., Krawec, W., & Zhang, P. (2020). Quantum-Secure Microgrid. IEEE Transactions on Power Systems, 1. http://doi.org/10.1109/TPWRS.2020.3011071

Tang, Z., Debs, J. N., Manning, R., Mader, J., Zhang, P., Muto, K., … Ferrante, D. A. (2020). Extreme Photovoltaic Power Analytics for Electric Utilities. IEEE Transactions on Sustainable Energy, 11(1), 14. http://doi.org/10.1109/TSTE.2018.2884500

Wang, L., Qin, Y., Tang, Z., & Zhang, P. (2020). Software-Defined Microgrid Control: The Genesis of Decoupled Cyber-Physical Microgrids. IEEE Open Access Journal of Power and Energy, 7, 10. http://doi.org/10.1109/OAJPE.2020.2997665

Tang, Z., Zhang, P., Krawec, W. O., & Jiang, Z. (2020). Programmable Quantum Networked Microgrids. IEEE Transactions on Quantum Engineering, 1, 13. http://doi.org/10.1109/TQE.2020.3019738

Wang, H., Tang, Z., Li, Y., & Zhang, P. (2019). Reachability Analysis of Dual Active Bridge DC-DC Converters. In 2019 IEEE Energy Conversion Congress and Exposition (ECCE) (p. 6). http://doi.org/10.1109/ECCE.2019.8913224

Tang, Z., Jiao, J., Zhang, P., Yue, M., Chen, C., & Yan, J. (2019). Enabling Cyberattack-Resilient Load Forecasting through Adversarial Machine Learning. In 2019 IEEE Power & Energy Society General Meeting (PESGM) (p. 5). http://doi.org/10.1109/PESGM40551.2019.8974076