Power and Energy

The dataset contains fundamental approaches regarding modeling individual photovoltaic (PV) solar cells, panels and combines into array and how to use experimental test data as typical curves to generate a mathematical model for a PV solar panel or array.



The work starts with a short overview of grid requirements for photovoltaic (PV) systems and control structures of grid-connected PV power systems. Advanced control strategies for PV power systems are presented next, to enhance the integration of this technology. The aim of this work is to investigate the response of the three-phase PV systems during symmetrical and asymmetrical grid faults.


The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.


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.


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.


Detailed 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.