Long-Term Operational Data Analysis of an In-Service Wind Turbine DFIG

[EN] While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors...

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Detalles Bibliográficos
Autores: Artigao, Estefania, Honrubia-Escribano, Andrés, Gómez-Lázaro, Emilio, Sapena-Bano, Angel|||0000-0002-3888-6498, Martinez-Roman, Javier|||0000-0001-7544-8481, Puche-Panadero, Rubén|||0000-0003-2090-1941
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/141971
Acceso en línea:https://riunet.upv.es/handle/10251/141971
Access Level:acceso abierto
Palabra clave:Doubly fed induction generators
Harmonic analysis
Stators
Maintenance engineering
Rotors
Current measurement
Wind turbines
Condition monitoring
Current signature analysis
DFIG
HOTA
Wind turbine
INGENIERIA ELECTRICA
Descripción
Sumario:[EN] While wind turbine (WT) power capacities continue to increase and new offshore developments are being deployed, operation and maintenance (O&M) costs continue to rise, becoming the center of attention in the wind energy sector. The electric generator is among the top three contributors to failure rates and downtime of WTs, where the doubly fed induction generator (DFIG) is the dominant technology among variable speed WTs. Thus, the early detection of generator faults, which can be achieved through predictive maintenance, is vital in order to reduce O&M costs. The goal of this paper is to analyze a long-term monitoring campaign of an in-service WT equipped with a DFIG. A novel method named the harmonic order tracking analysis is used with two main objectives: first, to facilitate the data interpretation for non-trained maintenance personnel, and second, to reduce the amount of data that must be stored and transferred for the diagnosis of the DFIG. This method is applied and validated for the first time on an operating WT.