{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Yang Weng", "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", "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", "format": "py", "mediaType": "application/octet-stream", "title": "Bad Data Detection AAE Testing Only Code.py"}, {"@type": "dcat:Distribution", "description": "Python code for injecting bad data into the dataset for testing.", "downloadURL": "https://data.openei.org/files/8345/baddatainjection.py", "format": "py", "mediaType": "application/octet-stream", "title": "Bad Data Injection Code.py"}, {"@type": "dcat:Distribution", "description": "Data for the project, including test/train data.", "downloadURL": "https://data.openei.org/files/8345/data.zip", "format": "zip", "mediaType": "application/zip", "title": "Data.zip"}, {"@type": "dcat:Distribution", "description": "Results from the trained DNN-SE model run in CSV format.", "downloadURL": "https://data.openei.org/files/8345/results.zip", "format": "zip", "mediaType": "application/zip", "title": "Results.zip"}, {"@type": "dcat:Distribution", "description": "Optimization files for the model.", "downloadURL": "https://data.openei.org/files/8345/optimization%20files.zip", "format": "zip", "mediaType": "application/zip", "title": "Optimization Files.zip"}, {"@type": "dcat:Distribution", "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", "format": "txt", "mediaType": "text/plain", "title": "Manual.txt"}, {"@type": "dcat:Distribution", "description": "Text file containing required packages to run the notebook.", "downloadURL": "https://data.openei.org/files/8345/requirements.txt", "format": "txt", "mediaType": "text/plain", "title": "Requirements.txt"}, {"@type": "dcat:Distribution", "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", "format": "ipynb", "mediaType": "application/octet-stream", "title": "Integrated DNN-SE and COCPIT Code.ipynb"}, {"@type": "dcat:Distribution", "description": "Trained model data in HDF5 format. ", "downloadURL": "https://data.openei.org/files/8345/trained_model.h5", "format": "h5", "mediaType": "application/octet-stream", "title": "Trained Model.h5"}, {"@type": "dcat:Distribution", "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", "format": "mp4", "mediaType": "application/octet-stream", "title": "Integrated DNN-SE and COCPIT Demo.mp4"}, {"@type": "dcat:Distribution", "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", "format": "zip", "mediaType": "application/zip", "title": "DSS Files.zip"}, {"@type": "dcat:Distribution", "description": "ReadMe file describing the resources, usage, and results.", "downloadURL": "https://data.openei.org/files/8345/README%20%285%29.txt", "format": "txt", "mediaType": "text/plain", "title": "README.txt"}], "identifier": "https://data.openei.org/submissions/8345", "issued": "2025-02-01T07:00:00Z", "keyword": ["energy", "power", "AI", "ML", "machine learning", "artificial intelligence", "AMI", "PMU", "PV", "photovoltaic", "real-time", "data", "raw data", "DNN", "neural network", "DNN-SE"], "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": {"@type": "org:Organization", "name": "Arizona State University"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-127.91233750000005,23.94630673589783],[-66.5318,23.94630673589783],[-66.5318,49.2637],[-127.91233750000005,49.2637],[-127.91233750000005,23.94630673589783]]]}", "title": "Artificial Intelligence for Robust Integration of AMI and Synchrophasor Data to Significantly Boost Solar Adoption"}