Logan Blakely / Sandia National Laboratories

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    • Title: Data-Driven Calibration of Electric Power Distribution System Models

    Date: September 30, 2020

    Bio: Logan Blakely is a Member of Technical Staff in the Electric Power Research Department at Sandia National Laboratories. His research focus is in machine learning applied to power systems challenges, particularly in the intersection of merging physics domain knowledge with machine learning techniques. This research area combines advances in machine learning and data analytics, the recent available of large quantities of new types of data, along with the reliability and interpretability of physics-based approaches.

    Abstract: Grid-edge sensing devices, including advanced metering infrastructure (AMI) devices, have enabled the development of a myriad of novel algorithms focused on calibrating distribution system models. Distribution system analysis tools are often severely limited in their effectiveness by the accuracy of the model details and parameters of the grid. This presentation will focus on using grid measurements and Big Data to provide more accurate feeder model phasing information, parameter estimation, better spatial and temporal load models, and to detect the presence of distributed energy resources (DER). Synthetic data is used to rigorously test algorithms under known conditions, and utility data is used to test the algorithms on actual U.S. utility distribution system models with field measurement data from SCADA, AMI, and other sources. We will also discuss strategies for managing issues found in utility data, such as missing data and measurement noise, as well as incorporating physical or domain knowledge into algorithms and algorithm development.

    Learning Materials: Talk Flyer Talk Slides

    Interacting Materials:

    1. Papers
      1. An Integrative Approach to Data-Driven Monitoring and Control of Electric Distribution Networks
      2. Data-Driven Power System Operation: Exploring the Balance Between Cost and Risk
      3. Data-driven Analysis of Power Distribution Synchrophasors with Applications to Situational Awareness, Load Modeling, and Reliability
      4. A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
      5. Leveraging AMI Data for Distribution System Model Calibration and Situational Awareness
    2. Videos
      1. InfluxData Engineering Town Hall
      2. Calibration of a Rubber Material Model for Abaqus
      3. Search and Navigation Analysis
      4. Model 9155 Frequency Response Tutorial
      5. XSEOS: Calculations with thermodynamic models made easy
    3. Data


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