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• W4367354911 abstract "Power transformers are a vital component in microgrids, as they play a crucial role in energy transformation, transmission, and distribution. With the ongoing digital transition in the energy sector and the emergence of the concept of the smart grid in power systems, power transformers must also adapt to this shift towards a more intelligent state. This includes the integration of features such as autonomous diagnostics and reliability, smart sensor integration, online monitoring, prognostics, and cybercommunication. This study provides an overview of the current level of smartness in power transformers and presents an approach for integrating them into a larger smart energy management system in a typical microgrid. This approach utilizes a combination of multi-agent theory, machine learning, automation, SCADA, and dispatching systems, with the goal of designing a Multi-Agent Reinforcement Learning algorithm for smarter diagnostic of power transformers. This algorithm utilizes multiple types of agents that work together to detect critical failures in power transformers and to assess their health and prognostic management. The algorithm delivers the global state to a smart energy management system for load management and power factor adjustment in a microgrid." @default.
• W4367354911 created "2023-04-30" @default.
• W4367354911 creator A5019772342 @default.
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• W4367354911 date "2023-01-01" @default.
• W4367354911 modified "2023-10-03" @default.
• W4367354911 title "Toward Smarter Power Transformers in Microgrids: A Multi-agent Reinforcement Learning for Diagnostic" @default.
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• W4367354911 doi "https://doi.org/10.1007/978-3-031-29860-8_65" @default.
• W4367354911 hasPublicationYear "2023" @default.
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