Work

Virtual active power sensor for eolic self-consumption installations based on wind-related variables

Year: 2024

Type: article

Abstract: Green energy production is expanding in individual and large-scale electricity grids, driven by the imperative to reduce greenhouse gas emissions. This research performs a comparative analysis of several linear and non-linear regression models, intending to identify the most effective method to estimate the active power produced for a mini wind turbine using meteorological variables, looking for a reliable virtual sensor. The modeling process followed a feature selection step before applying eight machine learning techniques whose results were statistically analysed to determine the best performance. The implemented virtual sensor accurately estimated the active power, being an interesting tool for anomaly detection, maintenance management or decision-making.

Source: Logic Journal of IGPL

DOI: 10.1093/jigpal/jzae109

Authors: Antonio Díaz-Longueira, Paula Arcano-Bea, Roberto Casado-Vara, Andrés-José Piñón-Pazos, Esteban Jove

Institutions: Universidade da Coruña, Universidad de Burgos

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $4,533