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Creators/Authors contains: "Rimkus, Mantautas"

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  1. ; ; ; ; (, Scandinavian Journal of Statistics)
    Abstract Localization of faults in a large power system is one of the most important and difficult tasks of power systems monitoring. A fault, typically a shorted line, can be seen almost instantaneously by all measurement devices throughout the system, but determining its location in a geographically vast and topologically complex system is difficult. The task becomes even more difficult if measurements devices are placed only at some network nodes. We show that regression graph neural networks we construct, combined with a suitable statistical methodology, can solve this task very well. A chief advance of our methods is that we construct networks that produce localization without having being trained on data that contain fault localization information. We show that a synergy of statistics and deep learning can produce results that none of these approaches applied separately can achieve. 
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    Free, publicly-accessible full text available June 1, 2026
  2. ; ; ; ; (, Austrian Journal of Statistics)
    The structure of power flows in transmission grids is evolving and is likelyto change significantly in the coming years due to the rapid growth ofrenewable energy generation that introduces randomness and bidirectionalpower flows. Another transformative aspect is the increasing penetrationof various smart-meter technologies. Inexpensive measurement devicescan be placed at practically any component of the grid. Using modeldata reflecting smart-meter measurements,we propose a two-stage procedure for detecting a fault in a regional powergrid. In the first stage, a fault is detected in real time. In the second stage,the faulted line is identified with a negligible delay. The approach uses onlythe voltage modulus measured at buses (nodes of the grid) as the input.Our method does not require prior knowledge of thefault type. The method is fully implemented in  R.Pseudo code and complete mathematical formulas are provided. 
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    Full Text Available 
  3. ; ; ; (, Communications in Statistics: Case Studies, Data Analysis and Applications)
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