2022
DOI: 10.1016/j.ress.2022.108628
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A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation

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Cited by 17 publications
(7 citation statements)
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“…The range of 18-30 m/s is close to the 16-27 m/s reported to represent the range dominated by faults occurring from a complex set of factors [69]. Modeling this complexity with fragility functions is difficult, resulting in the predictions of machine-learning-based models being more accurate [69]. However, above this range, all the fault mechanisms become dominated by wind, and thus a single physics-based fragility function form is well suited to represent the dependency [69].…”
Section: Electricity System Vulnerabilitysupporting
confidence: 68%
See 1 more Smart Citation
“…The range of 18-30 m/s is close to the 16-27 m/s reported to represent the range dominated by faults occurring from a complex set of factors [69]. Modeling this complexity with fragility functions is difficult, resulting in the predictions of machine-learning-based models being more accurate [69]. However, above this range, all the fault mechanisms become dominated by wind, and thus a single physics-based fragility function form is well suited to represent the dependency [69].…”
Section: Electricity System Vulnerabilitysupporting
confidence: 68%
“…The wind gust speed of 18 m/s is when faults start to occur, and 30 m/s is close to the record of the windstorm for which the original function was fitted. The range of 18-30 m/s is close to the 16-27 m/s reported to represent the range dominated by faults occurring from a complex set of factors [69]. Modeling this complexity with fragility functions is difficult, resulting in the predictions of machine-learning-based models being more accurate [69].…”
Section: Electricity System Vulnerabilitysupporting
confidence: 51%
“…The actual outcome will help determine that. Second, the two methods can be blended to improve the prediction [15].…”
Section: Physics/data Fusionmentioning
confidence: 99%
“…(13b) For the parameter estimation method, matrices 𝚿 and 𝐘 0 are first obtained from 𝐀 and 𝐁 according to (11) and ( 12), and then 𝐙 and 𝐘 are obtained from 𝚿 and 𝐘 0 according to (13a) and (13b). Finally, resistive, inductive and capacitive matrices of the line are calculated as 𝐑 = Re{𝐙}, (14a) 𝐋 = Im{𝐙}/𝜔, (14b) 𝐂 = Im{𝐘}/𝜔, (14c) where 𝜔 is the angular frequency.…”
Section: B Parameter Estimation From the Reconstructed Modelmentioning
confidence: 99%
“…Second, the challenging grid conditions under the worldwide scenario of aging grid infrastructure [9] exposed to extreme weather conditions [10] require of models that are not only physics-based, but also data-driven [11] to account for variations of operational conditions of power components throughout their lifetime for applications such as fault detection, predictive maintenance, asset life-cycle management, etc. This is particularly true for overhead lines given their long extension and direct exposure to diverse environments and weather conditions.…”
Section: Introductionmentioning
confidence: 99%