Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems
Accurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10373039/ |
| _version_ | 1797365940472512512 |
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| author | Logan Blakely Matthew J. Reno Joseph A. Azzolini C. Birk Jones David Nordy |
| author_facet | Logan Blakely Matthew J. Reno Joseph A. Azzolini C. Birk Jones David Nordy |
| author_sort | Logan Blakely |
| collection | DOAJ |
| description | Accurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that leverages advanced metering infrastructure (AMI) data and additional data streams from sensors (relays in this case) placed throughout the medium-voltage sector of distribution system feeders. Intuitive confidence metrics are employed to increase the credibility of the algorithm predictions and reduce the incidence of false-positive predictions. The method is first demonstrated on a synthetic dataset under known conditions for robustness testing with measurement noise, meter bias, and missing data. Then, four utility feeders are tested, and the algorithm’s predictions are proven to be accurate through field validation by the utility. Lastly, the ability of the method to increase the accuracy of simulated voltages using the corrected model compared to actual measured voltages is demonstrated through quasi-static time-series (QSTS) simulations. The proposed methodology is a good candidate for widespread implementation because it is accurate on both the synthetic and utility test cases and is robust to measurement noise and other issues. |
| first_indexed | 2024-03-08T16:57:07Z |
| format | Article |
| id | doaj.art-ada188dface54daf907926dc33ed97be |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T16:57:07Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ada188dface54daf907926dc33ed97be2024-01-05T00:03:06ZengIEEEIEEE Access2169-35362024-01-01121235124910.1109/ACCESS.2023.334681010373039Applying Sensor-Based Phase Identification With AMI Voltage in Distribution SystemsLogan Blakely0https://orcid.org/0000-0001-9256-4994Matthew J. Reno1https://orcid.org/0000-0002-4885-0480Joseph A. Azzolini2https://orcid.org/0000-0001-9580-4156C. Birk Jones3https://orcid.org/0000-0003-4395-7964David Nordy4Sandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USAEPB Chattanooga, Chattanooga, TN, USAAccurate distribution system models are becoming increasingly critical for grid modernization tasks, and inaccurate phase labels are one type of modeling error that can have broad impacts on analyses using the distribution system models. This work demonstrates a phase identification methodology that leverages advanced metering infrastructure (AMI) data and additional data streams from sensors (relays in this case) placed throughout the medium-voltage sector of distribution system feeders. Intuitive confidence metrics are employed to increase the credibility of the algorithm predictions and reduce the incidence of false-positive predictions. The method is first demonstrated on a synthetic dataset under known conditions for robustness testing with measurement noise, meter bias, and missing data. Then, four utility feeders are tested, and the algorithm’s predictions are proven to be accurate through field validation by the utility. Lastly, the ability of the method to increase the accuracy of simulated voltages using the corrected model compared to actual measured voltages is demonstrated through quasi-static time-series (QSTS) simulations. The proposed methodology is a good candidate for widespread implementation because it is accurate on both the synthetic and utility test cases and is robust to measurement noise and other issues.https://ieeexplore.ieee.org/document/10373039/Advanced metering infrastructure (AMI)correlationsdistribution systemphase identificationsensor |
| spellingShingle | Logan Blakely Matthew J. Reno Joseph A. Azzolini C. Birk Jones David Nordy Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems IEEE Access Advanced metering infrastructure (AMI) correlations distribution system phase identification sensor |
| title | Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems |
| title_full | Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems |
| title_fullStr | Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems |
| title_full_unstemmed | Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems |
| title_short | Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems |
| title_sort | applying sensor based phase identification with ami voltage in distribution systems |
| topic | Advanced metering infrastructure (AMI) correlations distribution system phase identification sensor |
| url | https://ieeexplore.ieee.org/document/10373039/ |
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