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Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids.

, , , , and . SmartGridComm, page 233-238. IEEE, (2021)

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Risk Mitigation for Dynamic State Estimation Against Cyber Attacks and Unknown Inputs., , , and . CoRR, (2015)Generator Parameter Estimation by Q-Learning Based on PMU Measurements., , and . CoRR, (2020)Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids., , , , and . SmartGridComm, page 233-238. IEEE, (2021)Characterizing the Nonlinearity of Power System Generator Models., , and . ACC, page 1936-1941. IEEE, (2019)Generator Parameter Estimation by Q-Learning Based on PMU Measurements., , and . ISGT, page 1-5. IEEE, (2021)Multi-Layer Interaction Graph for Analysis and Mitigation of Cascading Outages., , and . IEEE J. Emerg. Sel. Topics Circuits Syst., 7 (2): 239-249 (2017)Generator Parameter Calibration by Adaptive Approximate Bayesian Computation With Sequential Monte Carlo Sampler., and . IEEE Trans. Smart Grid, 12 (5): 4327-4338 (2021)Parameter Subset Selection for Power System Model Calibration Using Both Sensitivity and Identifiability., and . IEEE Access, (2024)Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems., , , , , and . IEEE Trans Autom. Sci. Eng., 20 (4): 2581-2593 (October 2023)State-Constrained Grid-Forming Inverter Control for Robust Operation of AC Microgrids., , and . ECC, page 471-474. IEEE, (2020)