DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter

Raziq Yaqub, Mohamed Ali and Hassan Ali
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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

Energies, 2021, vol. 14, issue 19, 1-17

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.

Keywords: artificial intelligence (AI); cloud; DC microgrid; phasor measurement unit (PMU); inverter; electric vehicles; synchronization; zero crossover distortion; slip management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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