TY - JOUR
T1 - Reinforcement learning based backstepping control of power system oscillations
AU - Karimi, Ali
AU - Eftekharnejad, Sara
AU - Feliachi, Ali
N1 - Funding Information:
This research is sponsored in part by a US DoE EPSCoR WV State Implementation Award, in part by grant from the US DEPSCoR and ONR (DOD/ONR N000 14-031-0660), and in part by the Department of Energy under Award Number DE-FC26-06NT42793.
PY - 2009/11
Y1 - 2009/11
N2 - This paper proposes a reinforcement learning based backstepping control technique for damping oscillations in electric power systems using the generators excitation systems. Decentralized controllers are first designed using the backstepping technique. Then, reinforcement learning is used to tune the gains of these controllers to adapt to various operating conditions. Simulation results for a two area power system show that the proposed control technique provides better damping than (i) conventional power system stabilizers and (ii) backstepping fixed gain controllers.
AB - This paper proposes a reinforcement learning based backstepping control technique for damping oscillations in electric power systems using the generators excitation systems. Decentralized controllers are first designed using the backstepping technique. Then, reinforcement learning is used to tune the gains of these controllers to adapt to various operating conditions. Simulation results for a two area power system show that the proposed control technique provides better damping than (i) conventional power system stabilizers and (ii) backstepping fixed gain controllers.
KW - Backstepping control
KW - Power system dynamics
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=68549130486&partnerID=8YFLogxK
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U2 - 10.1016/j.epsr.2009.05.005
DO - 10.1016/j.epsr.2009.05.005
M3 - Article
AN - SCOPUS:68549130486
SN - 0378-7796
VL - 79
SP - 1511
EP - 1520
JO - Electric Power Systems Research
JF - Electric Power Systems Research
IS - 11
ER -