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Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

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The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) in order to accommodate extreme levels of PV. For this goal, we aim at creating a highly efficient AI framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse AMI devices and PMUs. The files contain the integrated bad data detection with a pre-trained Deep Neural Network-based State Estimation (DNN-SE) model with a voltage regulation control algorithm to manage over-voltage issues in J-1 Feeder with high PV penetration.

Citation Formats

TY - DATA AB - The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) in order to accommodate extreme levels of PV. For this goal, we aim at creating a highly efficient AI framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse AMI devices and PMUs. The files contain the integrated bad data detection with a pre-trained Deep Neural Network-based State Estimation (DNN-SE) model with a voltage regulation control algorithm to manage over-voltage issues in J-1 Feeder with high PV penetration. AU - Ayyanar, Raja A2 - Pal, Anamitra A3 - Mai, Lihao A4 - Moshtagh, Shiva A5 - Dalal, Dhaval A6 - Farantatos, Evangelos DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - energy KW - power KW - AI KW - ML KW - machine learning KW - artificial intelligence KW - AMI KW - PMU KW - PV KW - photovoltaic KW - real-time KW - data KW - raw data KW - DNN KW - neural network KW - DNN-SE LA - English DA - 2025/02/01 PY - 2025 PB - Arizona State University T1 - Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption UR - https://data.openei.org/submissions/8345 ER -
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Ayyanar, Raja, et al. Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption. Arizona State University, 1 February, 2025, Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8345.
Ayyanar, R., Pal, A., Mai, L., Moshtagh, S., Dalal, D., & Farantatos, E. (2025). Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption. [Data set]. Open Energy Data Initiative (OEDI). Arizona State University. https://data.openei.org/submissions/8345
Ayyanar, Raja, Anamitra Pal, Lihao Mai, Shiva Moshtagh, Dhaval Dalal, and Evangelos Farantatos. Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption. Arizona State University, February, 1, 2025. Distributed by Open Energy Data Initiative (OEDI). https://data.openei.org/submissions/8345
@misc{OEDI_Dataset_8345, title = {Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption}, author = {Ayyanar, Raja and Pal, Anamitra and Mai, Lihao and Moshtagh, Shiva and Dalal, Dhaval and Farantatos, Evangelos}, abstractNote = {The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse advanced metering infrastructure (AMI) devices and phasor measurement units (PMUs) in order to accommodate extreme levels of PV. For this goal, we aim at creating a highly efficient AI framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from diverse AMI devices and PMUs. The files contain the integrated bad data detection with a pre-trained Deep Neural Network-based State Estimation (DNN-SE) model with a voltage regulation control algorithm to manage over-voltage issues in J-1 Feeder with high PV penetration.}, url = {https://data.openei.org/submissions/8345}, year = {2025}, howpublished = {Open Energy Data Initiative (OEDI), Arizona State University, https://data.openei.org/submissions/8345}, note = {Accessed: 2025-05-07} }

Details

Data from Feb 1, 2025

Last updated Apr 16, 2025

Submitted Feb 17, 2025

Organization

Arizona State University

Contact

Yang Weng

650.924.3618

Authors

Raja Ayyanar

Arizona State University

Anamitra Pal

Arizona State University

Lihao Mai

Arizona State University

Shiva Moshtagh

Arizona State University

Dhaval Dalal

Arizona State University

Evangelos Farantatos

Electric Power Research Institute

DOE Project Details

Project Name Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

Project Number EE0009355

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