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Volume 10 Issue 4
Apr.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(4): 916-930
R. R. Hossain and R. Kumar, “Machine learning accelerated real-time model predictive control for power systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 916–930, Apr. 2023. doi: 10.1109/JAS.2023.123135
Citation: R. R. Hossain and R. Kumar, “Machine learning accelerated real-time model predictive control for power systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 916–930, Apr. 2023. doi: 10.1109/JAS.2023.123135
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Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems

doi: 10.1109/JAS.2023.123135

  • Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA

Funds:  This work was supported in part by the National Science Foundation (NSF-CSSI-2004766, NSF-PFI-2141084)
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    Abstract
  • This paper presents a machine-learning-based speed-up strategy for real-time implementation of model-predictive-control (MPC) in emergency voltage stabilization of power systems. Despite success in various applications, real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems, and in power systems, the computation time exceeds the available decision time used in practice by a large extent. This long-standing problem is addressed here by developing a novel MPC-based framework that i) computes an optimal strategy for nominal loads in an offline setting and adapts it for real-time scenarios by successive online control corrections at each control instant utilizing the latest measurements, and ii) employs a machine-learning based approach for the prediction of voltage trajectory and its sensitivity to control inputs, thereby accelerating the overall control computation by multiple times. Additionally, a realistic control coordination scheme among static var compensators (SVC), load-shedding (LS), and load tap-changers (LTC) is presented that incorporates the practical delayed actions of the LTCs. The performance of the proposed scheme is validated for IEEE 9-bus and 39-bus systems, with ±20% variations in nominal loading conditions together with contingencies. We show that our proposed methodology speeds up the online computation by 20-fold, bringing it down to a practically feasible value (fraction of a second), making the MPC real-time and feasible for power system control for the first time.

     

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    Highlights

    • A novel machine learning accelerated perturbation control based Model Predictive Control (MPC) method is presented for power systems
    • Despite success in various applications, real-time implementation of MPC in power systems has not been successful due to the online control computation time required for large-sized complex systems, and in power systems, the computation time exceeds the available decision time by a large extent
    • This long-standing problem is addressed here by developing a novel MPC-based control framework that (i) adapts the nominal offline computed control, by successive control corrections, at each control decision point using the latest measurements, (ii) utilizes a machine learning approach for the prediction of voltage trajectory and its sensitivity with respect to control using trained neural networks (NNs) to save on computation time
    • A control scheme involving realistic coordination among SVC, LS, and LTC is proposed, where the slow-acting LTCs are formulated to have a delayed control effect to appropriately reflect their real-world behavior

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