Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning

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 thereafter implemented using distributed communication. Using the IEEE 39-bus power system model with 1149 unknown parameters, it is demonstrated that the proposed learning method provides reliable LQR performance while the controller matched to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure

The FNAL injector upgrade

The present FNAL H- injector has been operational since the 1970s and consists of two magnetron H- sources and two 750 keV Cockcroft-Walton Accelerators. In the upgrade, both slit-type magnetron sources will be replaced with circular aperture sources, and the Cockcroft-Waltons with a 200 MHz RFQ (radio frequency quadrupole). Operational experience at BNL (Brookhaven National Laboratory) has shown that the upgraded source and RFQ will be more reliable, improve beam quality and require less manpower than the present system.Comment: 3 pp. Particle Accelerator, 24th Conference (PAC'11) 2011. 28 Mar - 1 Apr 2011. New York, US