Logan Blakely

Member of Technical Staff

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Member of Technical Staff

lblakel@sandia.gov

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(505) 845-7827

Biography

Logan Blakely received his Master of Computer Science degree, specializing in Machine Learning, from Portland State University in 2018. His research focus is in machine learning applied to power systems challenges, particularly in the intersection merging physics domain knowledge with machine learning techniques.

Education

  • Master of Computer Science, Portland State University

Publications

  • Jalving, J., Eydenberg, M.S., Blakely, L., Kilwein, Z., Skolfield, J.K., Castillo, A., Boukouvala, F., Laird, C., & Laird, C. (2024). Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow. International Journal of Electrical Power and Energy Systems, 157. https://doi.org/10.1016/j.ijepes.2023.109741 Publication ID: 122632
  • Blakely, L., Hossain-McKenzie, S., Fragkos, G., & Fragkos, G. (2024). Challenges and Opportunities in Data Fusion Analysis for Cyber-Physical Systems [Conference Presentation]. 10.2172/2563868 Publication ID: 150816
  • Blakely, L., Reno, M.J., Azzolini, J.A., & Azzolini, J.A. (2024). Data-Driven Hosting Capacity Analysis [Conference Presentation]. 10.2172/2563841 Publication ID: 150736
  • Fragkos, G., Hossain-McKenzie, S., Summers, A., Goes, C., Akramul Haque, K., Davis, K., Blakely, L., & Blakely, L. (2024). A Comparison Study of Feature Extraction and Data Fusion Techniques for Improving Cyber-Physical Situational Awareness [Conference Poster]. 10.2172/2563824 Publication ID: 150676
  • Reyna, A.A., Collins, T.J., Hossain-McKenzie, S., Blakely, L., Goes, C., Hubbell, C., Anderson, R., & Anderson, R. (2024). Towards the Design of Grid Cyber-Physical Integrated Security Operation Centers [Conference Presentation]. 10.2172/2563847 Publication ID: 150756
  • Blakely, L., Hossain-McKenzie, S., Fragkos, G., Goes, C., Haque Khandaker, A., Davis, K., & Davis, K. (2024). Multimodal Learning in Cyber-Physical System: A Deep Dive with WSCC 9-Bus System [Conference Poster]. 10.2172/2517910 Publication ID: 149372
  • Fragkos, G., Blakely, L., Hossain-McKenzie, S., Summers, A., Goes, C., & Goes, C. (2024). Cyber-Physical Data Fusion & Threat Detection with LSTM-Based Autoencoders in the Grid [Conference Paper]. 2024 IEEE Kansas Power and Energy Conference, KPEC 2024. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85205785637&origin=inward Publication ID: 152220
  • Reyna, A.A., Collins, T.J., Hossain-McKenzie, S., Blakely, L., Goes, C., Anderson, R., Hubbell, C., & Hubbell, C. (2024). Towards the Design of Grid Cyber-Physical Integrated Security Operations Center Visualizations [Conference Paper]. 2024 IEEE Kansas Power and Energy Conference, KPEC 2024. 10.1109/KPEC61529.2024.10676242 Publication ID: 150900
  • Blakely, L., Reno, M.J., Azzolini, J.A., Jones, C.B., Nordy, D., & Nordy, D. (2023). Applying Sensor-Based Phase Identification With AMI Voltage in Distribution Systems. IEEE Access, 12, pp. 1235-1249. https://doi.org/10.1109/access.2023.3346810 Publication ID: 122548
  • Blakely, L., Reno, M.J., & Reno, M.J. (2023). Distribution System Model Calibration for GMLC 3.3.3 “Incipient Failure Identification for Common Grid Asset Classes” – Project Summary. 10.2172/2430304 Publication ID: 148092
  • Kilwein, Z., Jalving, J., Blakely, L., Eydenberg, M.S., Skolfield, J.K., Laird, C., Boukouvala, F., & Boukouvala, F. (2023). Optimization with Neural Network Feasibility Surrogates: Formulations and Application to Security-Constrained Optimal Power Flow. Energies, 16(16). https://doi.org/10.3390/en16165913 Publication ID: 107044
  • Kilwein, Z., Eydenberg, M.S., Blakely, L., Skolfield, J.K., Boukouvala, F., & Boukouvala, F. (2022). Structured Physics Informed Neural Networks for Surrogate Based Feasibility [Conference Poster]. 10.2172/2006133 Publication ID: 121252
  • Bradley, W., Kim, J., Kilwein, Z., Blakely, L., Eydenberg, M.S., Jalvin, J., Laird, C., Boukouvala, F., & Boukouvala, F. (2022). Perspectives on the integration between first-principles and data-driven modeling. Computers and Chemical Engineering, 166. 10.1016/j.compchemeng.2022.107898 Publication ID: 80249
  • Eydenberg, M.S., Batsch-Smith, L., Bice, C., Blakely, L., Bynum, M.L., Boukouvala, F., Castillo, A., Haddad, J., Hart, W.E., Jalving, J., Kilwein, Z., Laird, C., Skolfield, J.K., & Skolfield, J.K. (2022). Resilience Enhancements through Deep Learning Yields. 10.2172/1890044 Publication ID: 80293
  • Reno, M.J., Blakely, L., Trevizan, R.D., Pena, B., Lave, M., Azzolini, J.A., Yusuf, J., Jones, C.B., Furlani Bastos, A., Chalamala, R., Korkali, M., Sun, C., Donadee, J., Stewart, E.M., Donde, V., Peppanen, J., Hernandez, M., Deboever, J., Rocha, C., … Pinney, D. (2022). IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments. 10.2172/1888157 Publication ID: 80226
  • Azzolini, J.A., Talkington, S., Reno, M.J., Grijalva, S., Blakely, L., Pinney, D., McHann, S., & McHann, S. (2022). Improving Behind-the-Meter PV Impact Studies with Data-Driven Modeling and Analysis [Conference Proceeding]. https://doi.org/10.1109/PVSC48317.2022.9938462 Publication ID: 113068
  • Kilwein, Z., Jalving, J.H., Blakely, L., Eydenberg, M.S., Laird, C., Boukouvla, F., & Boukouvla, F. (2022). Deep Neural Networks as Surrogates for Intractable Constraints and Problem Dimension Reduction: SC ACOPF [Conference Presentation]. 10.2172/2001964 Publication ID: 108856
  • Reno, M.J., Blakely, L., & Blakely, L. (2022). AI-Based Protective Relays for Electric Grid Resiliency. 10.2172/1844320 Publication ID: 80397
  • Haddad, J., Bynum, M.L., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Laird, C.D., Jalving, J., & Jalving, J. (2022). Verification of Neural Network Surrogates [Conference Presentation]. Computer Aided Chemical Engineering. 10.2172/2003604 Publication ID: 113732
  • Azzolini, J.A., Talkington, S., Reno, M.J., Grijalva, S., Blakely, L., Pinney, D., McHann, S., & McHann, S. (2022). Improving Behind-the-Meter PV Impact Studies with Data-Driven Modeling and Analysis [Conference Presentation]. Conference Record of the IEEE Photovoltaic Specialists Conference. 10.2172/2003529 Publication ID: 113432
  • Pena, B.D., Blakely, L., Reno, M.J., & Reno, M.J. (2022). Data-Driven Detection of Phase Changes in Evolving Distribution Systems [Conference Presentation]. 2022 IEEE Texas Power and Energy Conference, TPEC 2022. 10.2172/2001812 Publication ID: 108308
  • Pena, B.D., Blakely, L., Reno, M.J., & Reno, M.J. (2021). Data-Driven Detection of Phase Changes in Evolving Distribution Systems [Conference Paper]. 10.1109/TPEC54980.2022.9750748 Publication ID: 107700
  • Reno, M.J., Blakely, L., Trevizan, R.D., Pena, B.D., Lave, M., Azzolini, J.A., Yusuf, J., Jones, C.B., Furlani Bastos, A., Chalamala, R., Korkali, M., Sun, C., Donadee, J., Stewart, E.M., Donde, V., Peppanen, J., Hernandez, M., Deboever, J., Rocha, C., … Glass, J. (2021). IMoFi – Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report). 10.2172/1855058 Publication ID: 79926
  • Haddad, J., Bynum, M.L., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Carl, L., Jalving, J.H., & Jalving, J.H. (2021). Verification of Neural Network Surrogates [Conference Paper]. 10.1016/B978-0-323-95879-0.50098-9 Publication ID: 107720
  • Jalving, J.H., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Laird, C., & Laird, C. (2021). Physics-Informed Machine Learning Surrogates with Optimization-Based Guarantees: Applications to AC Power Flow [Conference Presentation]. 10.2172/1897922 Publication ID: 76791
  • Laird, C., Jalving, J.H., Blakely, L., Eydenberg, M.S., Boukouvala, F., Kilwein, Z., & Kilwein, Z. (2021). Integration of Optimization and Machine Learning for Improving Electrical Grid Operation [Conference Presentation]. 10.2172/1896366 Publication ID: 76560
  • Gomez-Peces, C., Grijalva, S., Reno, M.J., Blakely, L., & Blakely, L. (2021). Estimation of PV Location based on Voltage Sensitivities in Distribution Systems with Discrete Voltage Regulation Equipment [Conference Paper]. 2021 IEEE Madrid PowerTech, PowerTech 2021 – Conference Proceedings. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85112364753&origin=inward Publication ID: 71669
  • Blakely, L., Pena, B.D., Reno, M.J., & Reno, M.J. (2021). Parameter Tuning Analysis for Phase Identification Algorithms in Distribution System Model Calibration [Conference Paper]. https://www.osti.gov/biblio/1863873 Publication ID: 78140
  • Blakely, L., Pena, B.D., Reno, M.J., & Reno, M.J. (2021). Parameter Tuning Analysis for Phase Identification Algorithms in Distribution System Model Calibration [Conference Presentation]. 10.2172/1864135 Publication ID: 78163
  • Blakely, L., Reno, M.J., & Reno, M.J. (2021). Identification and Correction of Errors in Pairing AMI Meters and Transformers [Conference Paper]. 2021 IEEE Power and Energy Conference at Illinois, PECI 2021. 10.1109/PECI51586.2021.9435274 Publication ID: 77343
  • Blakely, L., Reno, M.J., Jones, C.B., Furlani Bastos, A., Nordy, D., & Nordy, D. (2021). Leveraging Additional Sensors for Phase Identification in Systems with Voltage Regulators [Conference Paper]. 2021 IEEE Power and Energy Conference at Illinois, PECI 2021. 10.1109/PECI51586.2021.9435242 Publication ID: 77342
  • Blakely, L. (2021). Leveraging Additional Sensors for Phase Identification in Systems with Voltage Regulators [Conference Presentation]. 10.2172/1860606 Publication ID: 77827
  • Blakely, L. (2021). Identification and Correction of Errors in Pairing AMI Meters and Transformers [Conference Presentation]. 10.2172/1860605 Publication ID: 77826
  • Broderick, R.J., Reno, M.J., Lave, M., Azzolini, J.A., Blakely, L., Galtieri, J., Mather, B., Weekley, A., Hunsberger, R., Chamana, M., Li, Q., Zhang, W., Latif, A., Zhu, X., Grijalva, S., Zhang, X., Deboever, J., Qureshi, M.U., Therrien, F., … Dugan, R. (2021). Rapid QSTS Simulations for High-Resolution Comprehensive Assessment of Distributed PV. 10.2172/1773234 Publication ID: 77507
  • Kilwein, Z., Boukouvala, F., Laird, C., Castillo, A., Blakely, L., Eydenberg, M.S., Jalving, J.H., Batsch-Smith, L., & Batsch-Smith, L. (2021). AC-Optimal Power Flow Solutions with Security Constraints from Deep Neural Network Models [Conference Paper]. Computer Aided Chemical Engineering. 10.1016/B978-0-323-88506-5.50142-X Publication ID: 79597
  • Reno, M.J., Blakely, L., & Blakely, L. (2020). Data-Driven Calibration of Electric Power Distribution System Models [Presentation]. https://www.osti.gov/biblio/1824735 Publication ID: 71107
  • Blakely, L., Reno, M.J., & Reno, M.J. (2020). Identifying errors in service transformer connections [Conference Poster]. IEEE Power and Energy Society General Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099126919&origin=inward Publication ID: 66178
  • Blakely, L., Reno, M.J., & Reno, M.J. (2020). Phase identification using co-association matrix ensemble clustering. IET Smart Grid, 3(4), pp. 490-499. https://doi.org/10.1049/iet-stg.2019.0280 Publication ID: 73605
  • Grijalva, S., Khan, A.U., Reno, M.J., Blakely, L., & Blakely, L. (2020). Estimation of PV Location in Distribution Systems based on Voltage Sensitivities [Conference Poster]. https://www.osti.gov/biblio/1798058 Publication ID: 73808
  • Ashok, K., Reno, M.J., Blakely, L., Divan, D., & Divan, D. (2019). Systematic Study of Data Requirements and AMI Capabilities for Smart Meter Analytics [Conference Poster]. Proceedings of 2019 the 7th International Conference on Smart Energy Grid Engineering, SEGE 2019. 10.1109/SEGE.2019.8859916 Publication ID: 68664
  • Blakely, L., Reno, M.J., Peppanen, J., & Peppanen, J. (2019). Identifying Common Errors in Distribution System Models [Conference Poster]. Conference Record of the IEEE Photovoltaic Specialists Conference. 10.1109/PVSC40753.2019.8980833 Publication ID: 69178
  • Blakely, L., Reno, M.J., Ashok, K., & Ashok, K. (2019). AMI Data Quality and Collection Method Considerations for Improving the Accuracy of Distribution Models [Conference Poster]. Conference Record of the IEEE Photovoltaic Specialists Conference. 10.1109/PVSC40753.2019.8981211 Publication ID: 69119
  • Blakely, L., Reno, M.J., Ashok, K., & Ashok, K. (2019). AMI Data Quality and Collection Method Considerations for Improving the Accuracy of Distribution Models [Conference Poster]. https://doi.org/10.1109/PVSC40753.2019.8981211 Publication ID: 69118
  • Blakely, L., Reno, M.J., Peppanen, J., & Peppanen, J. (2019). Identifying Common Errors in Distribution System Models [Conference Poster]. https://doi.org/10.1109/PVSC40753.2019.8980833 Publication ID: 69112
  • Blakely, L., Reno, M.J., Feng, W., & Feng, W. (2019). Spectral Clustering for Customer Phase Identification Using AMI Voltage Timeseries [Conference Poster]. 2019 IEEE Power and Energy Conference at Illinois, PECI 2019. 10.1109/PECI.2019.8698780 Publication ID: 67140
  • Blakely, L., Reno, M.J., & Reno, M.J. (2019). Spectral Clustering for Customer Phase Identification Using AMI Voltage Timeseries Presentation [Conference Poster]. https://www.osti.gov/biblio/1602947 Publication ID: 67213
  • Blakely, L. (2018). Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Timeseries. https://www.osti.gov/biblio/1489536 Publication ID: 60637
  • Blakely, L., Reno, M.J., & Reno, M.J. (2018). Spectral Clustering for Phase Identification [Presentation]. https://www.osti.gov/biblio/1592348 Publication ID: 59986
  • Blakely, L., Reno, M.J., Feng, W., & Feng, W. (2018). Spectral Clustering for Identification of Electrical Phase Using Advanced Metering Infrastructure Voltage Time-series [Conference Poster]. 10.15760/etd.6567 Publication ID: 60268
  • Blakely, L., Reno, M.J., Broderick, R.J., & Broderick, R.J. (2018). Decision tree ensemble machine learning for rapid QSTS simulations [Conference Poster]. 2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2018. 10.1109/ISGT.2018.8403323 Publication ID: 60733
  • Blakely, L., Reno, M.J., Broderick, R.J., & Broderick, R.J. (2018). Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations. 10.2172/1734485 Publication ID: 101084
  • Blakely, L., Reno, M.J., Broderick, R.J., & Broderick, R.J. (2017). Decision Tree Ensemble Machine Learning for Rapid QSTS Simulations [Conference Poster]. 10.1109/ISGT.2018.8403323 Publication ID: 53134
Showing 10 of 52 publications.

Patents & Trademarks