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Award ID contains: 1932611

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  1. (, 2023 62nd IEEE Conference on Decision and Control (CDC))
    Full Text Available 
  2. (, 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton))
    Full Text Available 
  3. ; ; (, Electric Power Systems Research)
    Molzahn, Daniel; Milano, Federico (Ed.)
    Full Text Available 
  4. ; ; ; (, 2022 IEEE Power & Energy Society General Meeting (PESGM))
    Full Text Available 
  5. (, 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022))
    First we present an approach to formulate unbalanced three-phase power flow problems for general networks that explicitly separates device models and network models. A device model consists of (i) an internal model and (ii) a conversion rule. The conversion rule relates the internal variables (voltage, current, and power) of a device to its terminal variables through a conversion matrix Γ and these terminal variables are related by network equations. Second we apply this approach to balanced three-phase networks to formalize per-phase analysis and prove its validity for general networks using the spectral property of the conversion matrix Γ. 
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    Accepted Manuscript Full Text Available
  6. ; ; ; ; (, 2022 American Control Conference (ACC))
    Full Text Available 
  7. ; ; ; ; (, IEEE Transactions on Smart Grid)
    Full Text Available 
  8. ; ; ; ; ; ; (, Proceedings of the ACM on Measurement and Analysis of Computing Systems)
    We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances consistency, which measures the competitive ratio when predictions are accurate, and robustness, which bounds the competitive ratio when predictions are inaccurate. 
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  9. ; ; ; (, IEEE Transactions on Smart Grid)
  10. ; ; (, IEEE Electrification Magazine)
    Full Text Available