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.
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- 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.
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- M. Mahoor†, A. Majzoobi†, Z. Hosseini† and A. Khodaei, “Leveraging Sensory Data in Estimating Transformer Lifetime,” North American Power Symposium, Morgantown, WV, Sep. 2017.
- R. Eskandarpour†, and A. Khodaei, “Component Outage Estimation based on Support Vector Machine, IEEE PES General Meeting,” Chicago, IL, Jul. 2017.
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- 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.
- 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.