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Analysis of Outage Frequency and Duration in Distribution Systems using Machine Learning

https://doi.org/10.1109/NAPS50074.2021.9449708

Lawanson, Tumininu ; Sharma, Vinayak ; Cecchi, Valentina ; Hong, Tao ( April 2021 , 2020 52nd North American Power Symposium)
null (Ed.)
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Big data analytics for future electricity grids

https://doi.org/10.1016/j.epsr.2020.106788

Kezunovic, Mladen ; Pinson, Pierre ; Obradovic, Zoran ; Grijalva, Santiago ; Hong, Tao ; Bessa, Ricardo ( December 2020 , Electric Power Systems Research)

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Loss Estimation and Visualization in Distribution Systems using AMI and Recloser Data

https://doi.org/10.1109/TD39804.2020.9299891

Schulz, Dominik ; Lawanson, Tumininu ; Ravikumar, Kiran ; Cecchi, Valentina ( October 2020 , 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D))
null (Ed.)
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Improving Power Distribution System Situational Awareness Using Visual Analytics

https://doi.org/10.1109/SECON.2018.8479204

Lawanson, Tumininu ; Karandeh, Roozbeh ; Cecchi, Valentina ; Wartell, Zachary ; Cho, Isaac ( April 2018 , IEEE SoutheastCon 2018)

Traditionally, distribution system operators had limited visibility beyond distribution system substations. It was not unusual for electric utilities to have insufficient information about the network and phase connectivity model for the distribution system. This resulted in limited situational awareness at the distribution system level. In this paper, a visual analytics approach to gleaning intelligence from the vast amounts of data accumulated in the distribution system is proposed. The web-based visual analytics interface integrates data from heterogeneous datasets such as AMI, GIS and SCADA. The interface is designed to enable distribution system operators visualize and analyze the state of the distribution system over time. This paper presents the use of the visual analytics system to identify mismatched meter-to-transformer associations and to visualize voltage violations in a real-world distribution network.
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