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.


PV Solar Power System Under Partial Shading Irradiance Conditions

Operation of a photovoltaic (PV) solar array connected to a variable DC source to plot the l-V and P-V characteristics under partial shading irradiance conditions.


The Excel file contains samples of a laboratory-generated noisy voltage signal with a dc component under nonideal sampling.

This test signal is generated in a laboratory for assessing power frequency estimation algorithms.

The first column represents the sample time.

The second column represents the voltage signal samples.

The reference fundamental frequency is 46 Hz.

The nominal voltage amplitude is 10 V.

The actual sampling rate varies in the range of 9.99834~10.01027 kHz.



The synthetic dataset was developed in the Power Electronics and Electrical Drive Laboratory (Laboratório de Eletrônica de Potência e Acionamento Elétrico – LEPAC, in portuguese) at the Federal University of Espírito Santo (UFES), Brazil. The dataset was generated from a computer simulation of a three-phase squirrel cage induction motor model with the insertion of faults in the rotor, specifically broken bars. The induction motor mathematical model with broken bar fault is based on reference [1].


An experimental study was conducted on a high-voltage glass-type disc (LD-160) to investigate the effect of string arrangements on pollution and icing flashover characteristics. Two Artificial Neural Network (ANN) applications were developed to simulate and calculate the flashover voltage based on the experimental results. The test results showed that the inverted T-type arrangement can improve the pollution flashover voltage and increase the icing flashover voltage of insulator strings compared to the traditional arrangement of the I-string.


The dataset contains integrating renewable energy sources into power grids, emphasizing the need for advanced data-driven optimization models for optimal power flow problems. The dataset, which includes comprehensive details on both load and generator buses, covering active and reactive power measurements and voltage magnitudes and angles for the modified IEEE 39 bus system with wind power integration, is ideally suited for data-driven power system analysis studies. The dataset was generated for a part of the experiments.


Due to the diversity of battery types and capacities, wireless power transfer (WPT) systems for autonomous underwater vehicles (AUVs) have large variations in charging voltages and currents. Conventional solutions of using DC/DC converters to adapt to different specifications may cause increased cost and volumeand reduced overall efficiency. To address this issue, this video presents a new AUV WPT system with DC-link series/parallel AC-link parallel rectifiers for different charging voltages and currents


This study presents an automated approach for the generation of graphs from hand-drawn electrical circuit diagrams, aiming to streamline the digitization process and enhance the efficiency of traditional circuit design methods. Leveraging image processing, computer vision algorithms, and machine learning techniques, the system accurately identifies and extracts circuit components, capturing spatial relationships and diverse drawing styles.