Power System Control & Protection Lab

Data Science

Paper in Journals

  1. M. Alqudah, M. Kezunovic, Z. Obradovic, “Automated Power System Fault Prediction and Precursor Discovery Using Multi-modal Data” in IEEE Access, vol. 11, pp. 7283-7296, December 2022, doi: 10.1109/ACCESS.2022.3233219
  2. M. Alqudah, M. Pavlovski, T. Dokic, M. Kezunovic, Y. Hu, Z. Obradovic, “Convolution-based Fault Detection Utilizing Timeseries Synchrophasor Data from Phasor Measurement Units,” in IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3434-3442, September 2022, DOI:  10.1109/TPWRS.2021.3135336
  3. M. Pavlovski, M. Alqudah, T. Dokic, A. Abdel Hai, M. Kezunovic, Z. Obradovic, “ Hierarchical Convolutional Neural Networks for Event Classification on PMU Data,” in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-13, 2021, Art no. 2514813, September 2021, DOI: 10.1109/TIM.2021.3115583
  4. A. Abdel Hai, T. Dokic, M. Pavlovski, T. Mohamed, D. Saranovic, M. Alqudah, M. Kezunovic, Z. Obradovic, “Transfer Learning for Event Detection from PMU Measurements with Scarce Labels” in IEEE Access, Vol. 9, pp. 127420 – 127432, 10 September 2021, DOI: 10.1109/ACCESS.2021.3111727.
  5. M. Kezunovic, P. Pinson, Z. Obradovic, S. Grijalva, T. Hong, and R. Bessa, “Big Data Analytics for Future Electricity Grids,” Electric Power Systems Research, Vol. 189, No., pp. 106788, 2020, DOI: 10.1016/j.epsr.2020.106788.
  6. P. Dehghanian, B. Zhang, T. Dokic and M. Kezunovic, “Predictive Risk Analytics for Weather-Resilient Operation of Electric Power Systems,” IEEE Transactions on Sustainable Energy, Vol. 10, No., pp. 3-15, January 2019, DOI: 10.1109/TSTE.2018.2825780.
  7. T. Dokic, M. Kezunovic, “Predictive Risk Management for Dynamic Tree Trimming Scheduling for Distribution Networks,” IEEE Transactions on Smart Grid, Vol. 10, No. 5, pp. 4776-4785, September 2018.
  8. C. Qian and M. Kezunovic, “A Power Waveform Classification Method for Adaptive Synchrophasor Estimation,”  IEEE Transactions on Instrumentation and Measurement, Vol. 67, No. 7, pp.1646-1658, July 2018, DOI: 10.1109/TIM.2018.2803938.
  9. V. Malbasa, C. Zheng, P.-C. Chen, T. Popovic, and M. Kezunovic, “Voltage Stability Prediction Using Active Machine Learning,” IEEE Transactions on Smart Grid, Vol. 8, No. 6, pp. 3117-3124, November 2017.

Paper in Proceedings of Refereed Conferences

  1. H. Otudi, T. Mohamed, M. Kezunovic, Y. Hu, Z. Obradovic, “Training Machine Learning Models with Simulated Data for Improved Line Fault Events Classification From 3-Phase PMU Field Recordings,” The Hawaii International Conference on System Sciences (HICSS-56), Hawaii, USA, January 2023.
  2. M. Kezunovic, R. Baembitov, and M. Khoshjahan, “Data-driven State of Risk Prediction and Mitigation in Support of the Net-zero Carbon Electric Grid,” in 11th Bulk Power Systems Dynamics and Control Symposium – IREP’2022, Banff, Canada, 2022, doi:10.48550/arXiv.2207.03472.
  3. A. Abdel Hai, T. Mohamed, M. Pavlovski, M. Kezunovic, Z. Obradovic, “Transfer Learning on Phasor Measurement Data from a Power System to Detect Events in Another System”, in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Bahamas, December 2022.
  4. T. Mohamed, M. Kezunovic, Z. Obradovic, Y. Hu and Z. Cheng, “Application of Machine Learning to Oscillation Detection using PMU Data based on Prony Analysis,” 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Novi Sad, Serbia, 2022, pp. 1-5, doi: 10.1109/ISGT-Europe54678.2022.9960589.
  5. M. Kezunovic, Z. Obradovic, Y. HU, “Use of Machine Learning on PMU Data for Transmission System Fault Analysis,” CIGRE General Session, Paper # 191, Paris, France, August 2022.
  6. M. Kezunovic, Z. Obradovic, Y. Hu, “Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data,” CIGRE General Session, Paper # 224, Paris, France, August 2022.
  7. T. Dokic, R. Baembitov, A. A. Hai, Z. Cheng, Y. Hu, M. Kezunovic and Z. Obradovic., “Machine Learning Using a Simple Feature for Detecting Multiple Types of Events from PMU Data,” in 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), 24-26 May 2022 2022, pp. 1-6, doi: 10.1109/SGSMA51733.2022.9806000.
  8. H. Otudi, T. Dokic, T. Mohamed, M. Kezunovic, Y. Hu, Z. Obradovic, “Line Faults Classification Using Machine Learning on Three Phase Voltages Extracted from Large Dataset of PMU Measurements,” HICSS-55 Conference, Hawaii, USA, January 2022, DOI: 10.24251/HICSS.2022.425.
  9. R. Baembitov, M. Kezunovic, Z. Obradovic, “Graph Embeddings for Outage Prediction,” The 53rd NAPS, College Station, USA, Nov, 2021, DOI: 10.1109/NAPS52732.2021.9654696.
  10. R. Baembitov, T. Dokic, M. Kezunovic and Z. Obradovic,  “Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset Using Big Data Analytics,” HICSS-54 Conference, Hawaii, USA, January 2021, DOI: 10.24251/HICSS.2021.389.
  11. M. Kezunovic, T. Dokic, Z. Obradovic, M. Pavlovski, R. Said “Big Data Analytics for Predictive Lightning Outage Management Using Spatially Aware Logistic Regression Model”, CIGRE General Session, Aug. 2020, Paris, France. (online)
  12. M. Kezunovic, P. Pinson, Z. Obradovic,  S. Grijalva, T. Hong, R. Bessa, “Big Data Analytics for Future Electricity Grids”, Power Systems Computation Conference, Porto, Portugal, July, 2020 (online), DOI: 10.1016/j.epsr.2020.106788.
  13. M. Kezunovic, T. Dokic, R. Said, “Optimal Placement of Line Surge Arresters Based on Predictive Risk Framework Using Spatiotemporally Correlated Big Data,” at CIGRE General Session, Paris, France, Aug. 2018.