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• W2943989421 abstract "Variability in power generation at multiple consumer-owned renewable energy sources can cause random voltage fluctuations, which may result in voltage violations at multiple nodes in a power distribution system. Traditional methods of voltage control are ineffective to address fast voltage variations. New dynamic methods of voltage control using end-user devices can significantly benefit from the knowledge of the dominant influencer of voltage fluctuation (DIVF). This paper introduces information theoretic metrics to identify DIVF for every node in the power distribution system. A node can be DIVF due to its location in the distribution system and its capacity to generate and change its power. Use of traditional power flow algorithms to identify the DIVF is computationally complex, which limits their use in real-time applications. In this paper, information theoretic indicators of the DIVF are computed analytically in a computationally efficient manner, and their effectiveness to identify the DIVF is tested using the IEEE 69 node test system. From simulations, it is shown that differential entropy, Kullback-Leibler distance, and Frechet distance are effective indicators of the DIVF and can successfully identify the most influential nodes providing the foundation for intelligent voltage control." @default.
• W2943989421 created "2019-05-16" @default.
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• W2943989421 date "2019-11-01" @default.
• W2943989421 modified "2023-10-16" @default.
• W2943989421 title "The Dominant Influencer of Voltage Fluctuation (DIVF) for Power Distribution System" @default.
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• W2943989421 doi "https://doi.org/10.1109/tpwrs.2019.2915566" @default.
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