Power and Energy
Database of energy consumption (Eihop) and Transmission Power P0, resulting from the manipulation of the variables: Nb (Number of bits per frame), i (Number of hops to the destination) and d (Distance between origin and destination) in Tmote Sky device Ultra-low power IEEE 802.15.4 (Moteiv). DataSet used in the learning process, via Machine Learning, of the transmission behavior of this device.
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The sensorless control system of the interior permanent magnet machine (IPMSM) is considered in this paper. The control system is based on classical linear controllers. In IPMSM, there occurs non-sinusoidal distribution of rotor flux together with the slot harmonics, these are treated as control system disturbances. In this case, the classical observer structure in the (d-q) is unstable for the low range of rotor speed resulting in disturbances.
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This multimedium file demonstrates the structure of a proposed 360-degree folded spatial unipolar coil structure.
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This paper proposes a transformer DC bias magnetic compensation (DBMC) technology is based on a hybrid power transformer (HPT) with a integrated auxiliary DC/DC converter (IADDC). Attached is the experimental results of DC magnetic bias compensation.
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This dataset accounts for the cross-dependency between renewable generation and spot prices of 200 coupled scenarios for the three-dimensional time series comprising the spot prices, wind generation, and solar generation for every hour. To do that, we used the commercial hydrothermal dispatch model SDDP (from PSR Consulting) to simulate Brazilian spot prices based on the scenarios for the main renewable spots of the Brazilian system. We selected the year 2025 as the target year for the contracting horizon.
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Simulation parameters
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This is a multi-winding transformer insulation test for the auxiliary power supply of solid state transformers. The multi-winding transformer consists of two UU80 cores. 7 PCB-based MV-side windings are placed within the epoxy layer. A 50-Hz AC voltage with an RMS value of 20 kV was applied between the MV-side and LV-side of the transformer. The insulation of the transformer performed well.
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This document provides the data for the case studies of the work “Computational Performance Enhancement Strategies for Risk-Averse Two-Stage Stochastic Generation and Transmission Network Expansion Planning”.
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The data is for safe deep reinforcement learning in Microgrid. Temporal data with wind, photovoltaic, temperature, and inflexible power demand are designed for 52 days (4 seasons).
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The data is for safe deep reinforcement learning in Microgrid. Temporal data with wind, photovoltaic, temperature, and inflexible power demand are designed for 52 days (4 seasons).
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