optimization
The rapid growth of interconnected IoT devices has introduced complexities in their monitoring and management. Autonomous and intelligent management systems are essential for addressing these challenges and achieving self-healing, self-configuring, and self-managing networks. Intelligent agents have emerged as a powerful solution for autonomous network design, but their dynamic and intelligent management requires processing large volumes of data for training network function agents.
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This dataset contains the benchmark files that used in "Reclaimer Scheduling in Dry Bulk Terminals", IEEE Acccess, 2020.
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This work focuses on using the full potential of PV inverters in order to improve the efficiency of low voltage networks. More specifically, the independent per-phase control capability of PV three-phase four-wire inverters, which are able to inject different active and reactive powers in each phase, in order to reduce the system phase unbalance is considered. This new operational procedure is analyzed by raising an optimization problem which uses a very accurate modelling of European low voltage networks.
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This report outlines the derivation of the first-, second-, and third-order Taylor series expansions of the power flow solution; it is the Electronic Companion of the following paper:
R. A. Jabr, “High-order approximate power flow solutions and circular arithmetic applications,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5053-5062, November 2019.
The derivation is carried out in complex variables via the use of Wirtinger calculus.
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1. Figure S1 shows the plasma frequency profile of the two-layer analytical model of the ionosphere, see Eq. (9) of the main text.
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These datasets include the results from the comparison of different traffic-free path planning strategies presented in the work entitled "A primitive comparison for traffic-free path planning", Antonio Artuñedo, Jorge Godoy, Jorge Villagra. https://doi.org/10.1109/ACCESS.2018.2839884
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