Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Jorge Maldonado-Correa, Sergio Martín-Martínez, Estefanía Artigao and Emilio Gómez-Lázaro
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Jorge Maldonado-Correa: Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
Sergio Martín-Martínez: Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
Estefanía Artigao: Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
Emilio Gómez-Lázaro: Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain

Energies, 2020, vol. 13, issue 12, 1-21

Abstract: Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

Keywords: condition monitoring; wind turbine; SCADA data; artificial intelligence; fault prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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