Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, e...

Full description

Bibliographic Details
Main Authors: Pere Marti-Puig, Alejandro Blanco-M., Moisès Serra-Serra, Jordi Solé-Casals
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Applied Sciences
Subjects:
wind turbine
fault diagnosis
renewable energy
Extreme Learning Machines (ELM)
feature engineering
normal behaviour models
Online Access:https://www.mdpi.com/2076-3417/11/2/590

Internet

https://www.mdpi.com/2076-3417/11/2/590

Similar Items