Power and Energy

Due to the range of battery capacities in different electric vehicles (EVs), wireless power transfer (WPT) systems need to have a large variation in the output power. Since conventional systems are usually designed for a custom specification, the power and efficiency may be significantly reduced due to the need for interoperability between ground assembly and vehicle assembly with different power ratings. To address this issue this paper proposes a DC-link parallel AC-link series (DPAS) multi-inverter multi-rectifier (MIMR) architecture for high-power WPT systems.

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.

Design information of a real 50 MVA power transformer is available in this dataport. In addition, transient measurements of 4 different configurations are included.

This material has been shared with the authorization of Prof. Alvaro Portillo (Task force leader of JWG A2/C4.52) and the WEG transformer factory. 

We express our deepest gratitude to Prof. Alvaro Portillo and WEG for allowing the use of this information for academic and research purposes.

Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters’ fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults.

The authors show parts of the data obtained from the simulation model and the used MATLAB program. The detailed steps are shown as follows:

Step 1: Import data into the MATLAB software.

Step 2: Run the program named ‘fft_algorithm’.

Step 3: Run the program named ‘Solve_XX’ based on the fault type of data.

This paper proposes two new monitoring methods capable of detecting electrical disturbances in low voltage grids. Both approaches rely on machine learning techniques that classify voltage signals in the frequency domain. The first technique here proposed uses the Fourier Transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a Multilayer neural network with Multi-Valued Neurons (MLMVN). In this case, the structure of the classifier has three layers and a small number of neurons in the hidden layer.

This vedio recorded the high voltage test of a medium voltage (MV) medium frequency transformer (MFT). The voltage was 12.4 kVrms with a frequency of 50 Hz, which lasted for 60 seconds. The transformer passed the test.

The presented dataset is a hybrid solution between the simulated datasets and the ones based on real data. Furthermore, the main advantages and uniquenesses of the proposed work are: i) data variability, in terms of the operating states of the electrical loads and the adoption of appropriate consumption models; ii) the total number of available electrical parameters (433 in our case) enormously larger than the above-cited datasets, as the full analysis of frequency behaviors and harmonic computations; iii) the seasonality of the data.

According to recent statistics released by the California Department of Forestry and Fire Protection (CAL FIRE), in the year 2020, 8112 fire incidents were reported, resulting in damaging approximately 1.4 million acres of land in California

The failure of some electrical equipment is not due to high temperature but particular heat distribution and characteristics.