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DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter

Author

Listed:
  • Raziq Yaqub

    (Department of Electrical Engineering and Computer Science, Alabama A&M University, Huntsville, AL 35762, USA)

  • Mohamed Ali

    (Department of Electrical Engineering, The City College of New York, New York, NY 10031, USA)

  • Hassan Ali

    (Department of Electrical Engineering, College of the North Atlantic-Qatar, P.O. Box Doha 24449, Qatar)

Abstract

Community microgrids are set to change the landscape of future energy markets. The technology is being deployed in many cities around the globe. However, a wide-scale deployment faces three major issues: initial synchronization of microgrids with the utility grids, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. To validate the concept, a unique intelligent DC microgrid architecture that employs the proposed Phasor Measurement Unit (PMU) Assisted Inverter (PAI) is also presented, alongside the cloud-based Artificial Intelligence (AI), which harnesses energy from community shared resources, such as batteries and the community’s rooftop solar resources. The results show that the proposed system produces quality output and is 98.5% efficient.

Suggested Citation

  • Raziq Yaqub & Mohamed Ali & Hassan Ali, 2021. "DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter," Energies, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6086-:d:642333
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    References listed on IDEAS

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    1. Unamuno, Eneko & Barrena, Jon Andoni, 2015. "Hybrid ac/dc microgrids—Part I: Review and classification of topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1251-1259.
    2. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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