Artificial Intelligence

Artificial Intelligence

Electric utilities have invested heavily in monitoring and measurement infrastructure in the past decade to enable improved grid visibility and enhanced control functionalities. This has made data the epicenter of modern decision-making in power systems. However, there remains a critical challenge on how to convert these data into information, and further process the information into actionable items. Artificial intelligence is an enabling technology that can help with this transformation while providing a solution to many of the existing power system problems.

Selected Publications

  1. Z. Hosseini, M. Mahoor, A. Khodaei, “AMI-Enabled Distribution Network Line Outage Identification via Multi-Label SVM,” IEEE Power Engineering Letters, vol. 9, no. 5, pp. 5470-5472, Sep. 2018.
  2. Z. S. Hosseini, A. Khodaei, A. Paaso, “AMI-Enabled Phase Identification through a Modified Clustering Algorithm,” IEEE Transactions on Power Systems, In press, 2020.
  3. R. Eskandarpour†, A. Khodaei, E. A. Paaso, and M. Abdullah, “Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events,” CIGRE Grid of the Future Symposium, Reston, VA, Oct. 2018.
  4. R. Eskandarpour†, and A. Khodaei, “Component Outage Estimation based on Support Vector Machine, IEEE PES General Meeting,” Chicago, IL, Jul. 2017.
  5. M. Mahoor†, and A. Khodaei, “Data Fusion and Machine Learning Integration forTransformer Loss of Life Estimation,” IEEE PES Transmission and Distribution Conference and Exposition, Denver, CO, Apr. 2018.
  6. M. Alanazi†, and A. Khodaei, “Day-Ahead Solar Forecasting Using Time Series Stationarization and Feed-Forward Neural Network,” North American Power Symposium, Denver, CO, September 2016.
  7. R. Eskandarpour†, A. Khodaei, J. Lin, “Event-Driven Security-Constrained Unit Commitment with Component Outage Estimation Based on Machine Learning Method,” North American Power Symposium, Denver, CO, September 2016.
  8. R. Eskandarpour, and A. Khodaei, “Leveraging Accuracy-Uncertainty Tradeoff in SVM to Achieve Highly Accurate Outage Predictions,” IEEE Power Engineering Letters, vol. 33, no. 1, pp. 1139-1141, Jan. 2018.
  9. M. Mahoor†, A. Majzoobi†, Z. Hosseini† and A. Khodaei, “Leveraging Sensory Data in Estimating Transformer Lifetime,” North American Power Symposium, Morgantown, WV, Sep. 2017.
  10. A. Majzoobi†, M. Mahoor†, and A. Khodaei, “Machine Learning Applications in Estimating Transformer Loss of Life,” IEEE PES General Meeting, Chicago, IL, Jul. 2017.
  11. R. Eskandarpour, A. Khodaei, “Machine Learning based Power Grid Outage Prediction in Response to Extreme Events,” IEEE Power Engineering Letters, vol. 32, no. 4, pp. 3315–3316, Nov. 2016.