469 research outputs found
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
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