Smart Grid

This dataset originates from a wind farm and a photovoltaic (PV) power station located in a region of western Inner Mongolia. It includes meteorological and power output data from the entire year of 2022, with a temporal resolution of 15-minute intervals. The wind farm data comprises meteorological parameters such as wind speed, wind direction, temperature, and humidity, as well as the power output of the wind turbines. The PV station data includes irradiation, temperature, and other weather-related parameters relevant to solar power generation, along with the corresponding output data.

This repository contains the datasets produced using different data generation strategies to train data driven models (e.g., decision trees, gradient tree boosting, and deep neural networks), and to evaluate their performances. The data generation strategies are described, and the results are presented in the conference paper: "Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids" J. Cuenca, E. Aldea, E. Le Guern-Dall'o, R. Féraud, G. Camilleri, and A. Blavette. IEEE ISGT EU 2024, Dubrovnik, Croatia, Oct 2024.

This paper presents a method for modeling and simulating three-phase grid-connected solid-oxide fuel cell systems in the Matlab/Simulink environment. The approach is utilized to analyze the interaction between solid-oxide fuel cells and the electrical power system, focusing on understanding the dynamics of the electrochemical reaction and its impact on the performance of grid-connected fuel cell systems. The results highlight the effectiveness of the proposed method in accurately modeling and operating three-phase grid-connected solid-oxide fuel cell systems.

This dataset webpage contains datasets of existing and proposed models 

• 4cell.zip
• 6cell.zip
• 8cell.zip

Presented in my 3rd Speaker Presentation in Conference -  2025*.