TY - GEN
T1 - Q-Learning based Protection Scheme for Microgrid using Multi-Agent System
AU - Satuyeva, Botazhan
AU - Sultankulov, Bekbol
AU - Nunna, H. S.V.S.Kumar
AU - Kalakova, Aidana
AU - Doolla, Suryanarayana
PY - 2019/9
Y1 - 2019/9
N2 - Distributed Energy Resources (DERs) such as Distributed Generators (DGs) or storage systems can be integrated with the central power distribution system. However, one of the severe challenges posed by the penetration of DGs to the utility grid system is the bi-directional power flows in the feeders. The bi-directional energy flows cause issues pertaining to the failures of protection systems because usually relays are designed to protect the network under unidirectional power flow case. Therefore, it is essential to have a robust protection scheme in support of distributed generators to protect the system from various faults. This paper proposes a novel protection scheme based on Q-learning and multi-agent system that identifies and isolates the different types of failure. The Q-learning algorithm is built to teach agents for making responsible decisions in fault identification and clearing. Furthermore, decentralized Blockchain based connections were adopted for exchanging information between agents. The system was simulated in MATLAB and JADE platforms and the results have shown that system is capable to identify different type of faults based on various states.
AB - Distributed Energy Resources (DERs) such as Distributed Generators (DGs) or storage systems can be integrated with the central power distribution system. However, one of the severe challenges posed by the penetration of DGs to the utility grid system is the bi-directional power flows in the feeders. The bi-directional energy flows cause issues pertaining to the failures of protection systems because usually relays are designed to protect the network under unidirectional power flow case. Therefore, it is essential to have a robust protection scheme in support of distributed generators to protect the system from various faults. This paper proposes a novel protection scheme based on Q-learning and multi-agent system that identifies and isolates the different types of failure. The Q-learning algorithm is built to teach agents for making responsible decisions in fault identification and clearing. Furthermore, decentralized Blockchain based connections were adopted for exchanging information between agents. The system was simulated in MATLAB and JADE platforms and the results have shown that system is capable to identify different type of faults based on various states.
KW - Blockchain
KW - Microgrid
KW - Multi-Agent System (MAS)
KW - Protection systems
KW - Q-learning process
UR - http://www.scopus.com/inward/record.url?scp=85073331303&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073331303&partnerID=8YFLogxK
U2 - 10.1109/SEST.2019.8849088
DO - 10.1109/SEST.2019.8849088
M3 - Conference contribution
AN - SCOPUS:85073331303
T3 - SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
BT - SEST 2019 - 2nd International Conference on Smart Energy Systems and Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Smart Energy Systems and Technologies, SEST 2019
Y2 - 9 September 2019 through 11 September 2019
ER -