Published June 22, 2023
| Version v1
Publication
Data Fault Detection for Digital Twin Learning Action Decision of a Wind Turbine
Contributors
Others:
- Universidad de Sevilla. Departamento de Matemática Aplicada II (ETSI)
- Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática
- Universidad de Sevilla. TEP116: Automática y Robótica Industrial
- Spanish Ministry of Science, Innovation and Universities under grant PID2019- 104149RB-I00
- European Union's Horizon 2020 grant agreement no. 958339
Description
This paper presents the design of a classifier of
variable failures in a wind turbine system. The classifier is based
on a structure formed by several TS fuzzy inference systems, with
projections of the data onto components of a principal component
analysis. The classifier is part of a discrepancy evaluator for
triggering the learning mechanism of the digital twin of the wind
turbine.
Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/147398
- URN
- urn:oai:idus.us.es:11441/147398