Machine Learning Accelerated Real-Time Model Predictive Control for Power Systems

Ramij Raja Hossain

doi:10.1109/JAS.2023.123135

2023-04-01

PRE

Ramij Raja Hossain *

Ratnesh Kumar

DOI：10.1109/JAS.2023.123135

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摘要

En 中文

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 & PLUSMN;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.

Keyword:

Voltage measurement

Sensitivity

Static VAr compensators

Machine learning

Power system stability

Real-time systems

Trajectory

model predictive control (MPC)

neural network

perturbation control

voltage stabilization