Power and Energy

The dataset reflects a single household (home) power profile related to the grid. Households include typical appliances, two air-to-air heat pumps, a 3-phase 18 kW through-flow water heater, 6 kW solar panels and a 2,5 kW charger for the electric car.

For five-month (April_August) in 2022, every 0.2 sec took each 3-phase voltage and current measurement, calculate power and harmonics (up to 15th) for power profile registration.

Positive value reflects energy flow from the grid to a household, and negative values are energy flow to the grid.

The code synthesizes a transfer function with real poles from tabulated frequency response data. The reproducible run of this capsule will synthesize the impedance matrix of a 500-kV double-circuit overhead power transmission line and also that of a 250-kV dc submarine power cable.

Device fingerprinting is a technique for remote indirect identification or classification of a device of interest. This database is designated for device fingerprinting by current consumption; it includes current recordings for 22" computer displays from 40 devices - 20 Dell P2217H and 20 Dell E2214H. Two signals for each device were sampled independently and sequentially to provide independent train and test parts. Each sampled signal includes a 250-second recording at a 50kHz sampling frequency.

This dataset aims to provide more insight into magnetic core losses at high frequency (MHz range) with superimposed DC bias in ferrites. The tested MnZn ferrite has a nominal relative permeability of 1500 (PC50) and 800 (PC200), which was tested at frequency from 500 kHz to 3 MHz. The DC bias appears to create a shift in the Steinmetz parameters, in particular the ki and β are affected the most. Higher DC bias creates higher losses gain and higher operating frequency makes the relative losses increase lower.