DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter
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 operati...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/19/6086 |
| _version_ | 1797516686164754432 |
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| author | Raziq Yaqub Mohamed Ali Hassan Ali |
| author_facet | Raziq Yaqub Mohamed Ali Hassan Ali |
| author_sort | Raziq Yaqub |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:04:20Z |
| format | Article |
| id | doaj.art-f8d59791c1f544128c83ee2e93182800 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T07:04:20Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f8d59791c1f544128c83ee2e931828002023-11-22T15:59:11ZengMDPI AGEnergies1996-10732021-09-011419608610.3390/en14196086DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted InverterRaziq Yaqub0Mohamed Ali1Hassan Ali2Department of Electrical Engineering and Computer Science, Alabama A&M University, Huntsville, AL 35762, USADepartment of Electrical Engineering, The City College of New York, New York, NY 10031, USADepartment of Electrical Engineering, College of the North Atlantic-Qatar, P.O. Box Doha 24449, QatarCommunity 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.https://www.mdpi.com/1996-1073/14/19/6086artificial intelligence (AI)cloudDC microgridphasor measurement unit (PMU)inverterelectric vehicles |
| spellingShingle | Raziq Yaqub Mohamed Ali Hassan Ali DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter Energies artificial intelligence (AI) cloud DC microgrid phasor measurement unit (PMU) inverter electric vehicles |
| title | DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter |
| title_full | DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter |
| title_fullStr | DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter |
| title_full_unstemmed | DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter |
| title_short | DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter |
| title_sort | dc microgrid utilizing artificial intelligence and phasor measurement unit assisted inverter |
| topic | artificial intelligence (AI) cloud DC microgrid phasor measurement unit (PMU) inverter electric vehicles |
| url | https://www.mdpi.com/1996-1073/14/19/6086 |
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