2019 conference paper
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
2019 American Control Conference (ACC), 2867–2872.
TL;DR:
An online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning is presented and the gradient support pursuit (GraSP) optimization algorithm is employed to impose sparsity constraints on the control gain matrix during learning.
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open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science; OpenAlex)
Source: ORCID
Added: April 29, 2020
In this paper we present an online wide-area oscillation damping control (WAC) design for uncertain models of power systems using ideas from reinforcement learning. We assume that the exact small-signal model of the power system at the onset of a contingency is not known to the operator and use the nominal model and online measurements of the generator states and control inputs to rapidly converge to a state-feedback controller that minimizes a given quadratic energy cost. However, unlike conventional linear quadratic regulators (LQR), we intend our controller to be sparse, so its implementation reduces the communication costs. We, therefore, employ the gradient support pursuit (GraSP) optimization algorithm to impose sparsity constraints on the control gain matrix during learning. The sparse controller is then implemented using distributed communication. The proposed method is validated using the IEEE 39-bus power system model with 1149 unknown parameters.