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Title: SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in modern distribution power grids. Faced with uncertain or even unknown distribution grid models and fast changing power injections, model-free deep reinforcement learning (DRL) algorithms have been proposed to find the reactive power injections for inverters while optimizing the voltage profiles. However, such data-driven controllers can not guarantee the satisfaction of the hard operational constraints, such as maintaining voltage profiles within a certain range of the nominal value. To this end, we propose SAVER: SAfe Voltage Regulator, which is composed of an RL learner and a specifically designed, computationally efficient safety projection layer. SAVER provides a plug-and-play interface for a set of DRL algorithms that guarantees the system voltages are within safe bounds. Numerical simulations on real-world data validate the performance of the proposed algorithm.  more » « less
Award ID(s):
2200692
NSF-PAR ID:
10398944
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2022 IEEE Power & Energy Society General Meeting (PESGM)
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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