Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption
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 -
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: 2025-05-23}
}
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
Research Areas
Keywords
energy, power, AI, ML, machine learning, artificial intelligence, AMI, PMU, PV, photovoltaic, real-time, data, raw data, DNN, neural network, DNN-SEDOE Project Details
Project Name Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption
Project Number EE0009355
