TY - GEN
T1 - Stability enhancement through reinforcement learning
T2 - 2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability
AU - Eftekharnejad, Sara
AU - Feliachi, Ali
PY - 2007
Y1 - 2007
N2 - A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).
AB - A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).
UR - http://www.scopus.com/inward/record.url?scp=47949128403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47949128403&partnerID=8YFLogxK
U2 - 10.1109/IREP.2007.4410552
DO - 10.1109/IREP.2007.4410552
M3 - Conference contribution
AN - SCOPUS:47949128403
SN - 1424415195
SN - 9781424415199
T3 - 2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability
BT - 2007 iREP Symposium- Bulk Power System Dynamics and Control - VII, Revitalizing Operational Reliability
Y2 - 19 August 2007 through 24 August 2007
ER -