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., 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
  • 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]. https://doi.org/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. https://doi.org/10.1016/j.compchemeng.2022.107898 Publication ID: 80249
  • 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. https://doi.org/10.2172/1888157 Publication ID: 80226
  • Eydenberg, M.S., Batsch-Smith, L., Bice, C., Blakely, L., Bynum, M., 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. https://doi.org/10.2172/1890044 Publication ID: 80293
  • 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 Presenation]. https://doi.org/10.2172/2001964 Publication ID: 108856
  • Reno, M.J., Blakely, L., & Blakely, L. (2022). AI-Based Protective Relays for Electric Grid Resiliency. https://doi.org/10.2172/1844320 Publication ID: 80397
  • Pena, B., Blakely, L., Reno, M.J., & Reno, M.J. (2022). Data-Driven Detection of Phase Changes in Evolving Distribution Systems [Conference Presenation]. https://doi.org/10.2172/2001812 Publication ID: 108308
  • Pena, B., Blakely, L., Reno, M.J., & Reno, M.J. (2022). Data-Driven Detection of Phase Changes in Evolving Distribution Systems [Conference Paper]. 2022 IEEE Texas Power and Energy Conference, TPEC 2022. https://doi.org/10.1109/TPEC54980.2022.9750748 Publication ID: 107700
  • 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 Presenation]. Conference Record of the IEEE Photovoltaic Specialists Conference. https://doi.org/10.2172/2003529 Publication ID: 113432
  • Haddad, J., Bynum, M., Eydenberg, M.S., Blakely, L., Kilwein, Z., Boukouvala, F., Laird, C.D., Jalving, J., & Jalving, J. (2022). Verification of Neural Network Surrogates [Conference Presenation]. Computer Aided Chemical Engineering. https://doi.org/10.2172/2003604 Publication ID: 113732
  • 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., … Glass, J. (2022). IMoFi – Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report). https://doi.org/10.2172/1855058 Publication ID: 79926
  • Haddad, J., Bynum, M., 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]. https://doi.org/10.1016/B978-0-323-95879-0.50098-9 Publication ID: 107720
  • 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 Presenation]. https://doi.org/10.2172/1896366 Publication ID: 76560
  • 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 Presenation]. https://doi.org/10.2172/1897922 Publication ID: 76791
  • 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., 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. https://doi.org/10.1109/PECI51586.2021.9435242 Publication ID: 77342
  • 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. https://doi.org/10.1109/PECI51586.2021.9435274 Publication ID: 77343
  • Blakely, L., Pena, B., Reno, M.J., & Reno, M.J. (2021). Parameter Tuning Analysis for Phase Identification Algorithms in Distribution System Model Calibration [Conference Presenation]. https://doi.org/10.2172/1864135 Publication ID: 78163
  • Blakely, L., Pena, B., 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. (2021). Identification and Correction of Errors in Pairing AMI Meters and Transformers [Conference Presenation]. https://doi.org/10.2172/1860605 Publication ID: 77826
  • Blakely, L. (2021). Leveraging Additional Sensors for Phase Identification in Systems with Voltage Regulators [Conference Presenation]. https://doi.org/10.2172/1860606 Publication ID: 77827
  • 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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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. https://doi.org/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., Feng, W., & Feng, W. (2018). Spectral Clustering for Identification of Electrical Phase Using Advanced Metering Infrastructure Voltage Time-series [Conference Poster]. https://doi.org/10.15760/etd.6567 Publication ID: 60268
  • 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., 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. https://doi.org/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. https://doi.org/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]. https://doi.org/10.1109/ISGT.2018.8403323 Publication ID: 53134
Showing 10 of 44 publications.

Patents & Trademarks