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

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.

Resources

Name Format Description Link
0 Python code for injecting bad data into the dataset for testing. https://data.openei.org/files/8345/baddatainjection.py
57 Data for the project, including test/train data. https://data.openei.org/files/8345/data.zip
57 Results from the trained DNN-SE model run in CSV format. https://data.openei.org/files/8345/results.zip
57 Optimization files for the model. https://data.openei.org/files/8345/optimization%20files.zip
47 Text file containing information on how to install the required packages to run the notebook https://data.openei.org/files/8345/Manual.txt
47 Text file containing required packages to run the notebook. https://data.openei.org/files/8345/requirements.txt
0 Trained model data in HDF5 format. https://data.openei.org/files/8345/trained_model.h5
77 Video demo of "Integrated DNN-SE Model and COCPIT Code". https://data.openei.org/files/8345/Integrated%20DNN-SE%20and%20COCPIT%20Demo.mp4
57 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. https://data.openei.org/files/8345/dss%20files.zip
47 ReadMe file describing the resources, usage, and results. https://data.openei.org/files/8345/README%20%285%29.txt
37 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. https://data.openei.org/files/8345/Integrated_DNN-SE_COCPIT_Github_version.ipynb
0 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. https://data.openei.org/files/8345/BadDataDetectionAAEtestingonly.py

Tags

  • real-time
  • data
  • pv
  • photovoltaic
  • neural-network
  • artificial-intelligence
  • dnn
  • ml
  • power
  • dnn-se
  • ai
  • pmu
  • raw-data
  • machine-learning
  • ami
  • energy

Topics

Categories