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

Published by Arizona State University | Department of Energy | Catalog Last Checked: March 23, 2026 at 09:43 PM | Dataset Last Updated: April 16, 2025 at 09:08 PM
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.

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Complete Metadata
@type dcat:Dataset
accessLevel public
bureauCode 
[
  "019:20"
]
contactPoint 
{
  "fn": "Yang Weng",
  "@type": "vcard:Contact",
  "hasEmail": "mailto:yweng2@asu.edu"
}
dataQuality 
true
description 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.
distribution 
[
  {
    "@type": "dcat:Distribution",
    "title": "DSS Files.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "DSS (OpenDSS) files containing power system models and simulation settings for analyzing the impact of high PV penetration and integrating state estimation and voltage regulation algorithms.",
    "downloadURL": "https://data.openei.org/files/8345/dss%20files.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Data.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "Data for the project, including test/train data.",
    "downloadURL": "https://data.openei.org/files/8345/data.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Integrated DNN-SE and COCPIT Code.ipynb",
    "format": "ipynb",
    "mediaType": "application/octet-stream",
    "description": "Jupyter Notebook containing the code for the 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.",
    "downloadURL": "https://data.openei.org/files/8345/Integrated_DNN-SE_COCPIT_Github_version.ipynb"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Optimization Files.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "Optimization files for the model.",
    "downloadURL": "https://data.openei.org/files/8345/optimization%20files.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Bad Data Injection Code.py",
    "format": "py",
    "mediaType": "application/octet-stream",
    "description": "Python code for injecting bad data into the dataset for testing.",
    "downloadURL": "https://data.openei.org/files/8345/baddatainjection.py"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Bad Data Detection AAE Testing Only Code.py",
    "format": "py",
    "mediaType": "application/octet-stream",
    "description": "Python code integrating the DNN-SE model with bad data detection testing and a voltage regulation control algorithm to manage over-voltage issues in the J-1 Feeder with high PV penetration. It uses an autoencoder (Encoder-Decoder) architecture to detect and correct synchronization issues in PMU data, identifying and replacing bad data. ",
    "downloadURL": "https://data.openei.org/files/8345/BadDataDetectionAAEtestingonly.py"
  },
  {
    "@type": "dcat:Distribution",
    "title": "README.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "description": "ReadMe file describing the resources, usage, and results.",
    "downloadURL": "https://data.openei.org/files/8345/README%20%285%29.txt"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Results.zip",
    "format": "zip",
    "mediaType": "application/zip",
    "description": "Results from the trained DNN-SE model run in CSV format.",
    "downloadURL": "https://data.openei.org/files/8345/results.zip"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Manual.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "description": "Text file containing information on how to install the required packages to run the notebook",
    "downloadURL": "https://data.openei.org/files/8345/Manual.txt"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Requirements.txt",
    "format": "txt",
    "mediaType": "text/plain",
    "description": "Text file containing required packages to run the notebook.",
    "downloadURL": "https://data.openei.org/files/8345/requirements.txt"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Trained Model.h5",
    "format": "h5",
    "mediaType": "application/octet-stream",
    "description": "Trained model data in HDF5 format. ",
    "downloadURL": "https://data.openei.org/files/8345/trained_model.h5"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Integrated DNN-SE and COCPIT Demo.mp4",
    "format": "mp4",
    "mediaType": "application/octet-stream",
    "description": "Video demo of "Integrated DNN-SE Model and COCPIT Code".",
    "downloadURL": "https://data.openei.org/files/8345/Integrated%20DNN-SE%20and%20COCPIT%20Demo.mp4"
  }
]
identifier https://data.openei.org/submissions/8345
issued 2025-02-01T07:00:00Z
keyword 
[
  "AI",
  "AMI",
  "DNN",
  "DNN-SE",
  "ML",
  "PMU",
  "PV",
  "artificial intelligence",
  "data",
  "energy",
  "machine learning",
  "neural network",
  "photovoltaic",
  "power",
  "raw data",
  "real-time"
]
landingPage https://data.openei.org/submissions/8345
license https://creativecommons.org/licenses/by/4.0/
modified 2025-04-16T21:08:50Z
programCode 
[
  "019:000",
  "019:008"
]
projectNumber EE0009355
projectTitle Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption
publisher 
{
  "name": "Arizona State University",
  "@type": "org:Organization"
}
spatial 
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title Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption

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