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Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

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Název: Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems
Autoři: Brook W. Abegaz, Sina Zarrabian
Zdroj: International Transactions on Electrical Energy Systems. 2022:1-13
Informace o vydavateli: Wiley, 2022.
Rok vydání: 2022
Témata: Automatic Voltage Regulators, Electrical and Electronics, Embedded and Hardware Systems, Voltage Regulator, 0202 electrical engineering, electronic engineering, information engineering, Voltage, Power Systems, Power and Energy, 02 engineering and technology, VLSI and Circuits
Popis: The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.
Druh dokumentu: Article
Popis souboru: text/xhtml; application/pdf
Jazyk: English
ISSN: 2050-7038
DOI: 10.1155/2022/3047761
Rights: CC BY
Přístupové číslo: edsair.doi.dedup.....a46a59fec4f609e76e7444d94b1d81a1
Databáze: OpenAIRE
Popis
Abstrakt:The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.
ISSN:20507038
DOI:10.1155/2022/3047761