A hybrid multiagent framework with Q-learning for power grid systems restoration

IEEE Transactions on Power Systems

Volumn 26, Issue 4, 2011, Pages 2434-2441

  Ye, Dayong ^a   Zhang, Minjie ^a   Sutanto, Danny ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

Hybrid solution; multiagent systems; Q learning

Indexed keywords

CASCADING FAILURES; CATASTROPHIC DISTURBANCES; DECENTRALIZED APPROACH; DECENTRALIZED ARCHITECTURE; GLOBAL REWARD SIGNALS; HYBRID FRAMEWORK; HYBRID SOLUTION; MULTIAGENT FRAMEWORK; POWER GRID SYSTEM; Q-LEARNING; Q-LEARNING ALGORITHMS; QUICK RESPONSE; SIMULATION RESULT; SINGLE POINT;

DECISION MAKING; ELECTRIC POWER DISTRIBUTION; MULTI AGENT SYSTEMS; RESTORATION;

LEARNING ALGORITHMS;

EID: 80054890038     PISSN: 08858950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRS.2011.2157180     Document Type: Article

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