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Cited by 183Machine Fault Diagnosis TechniquesWind Energy Research and DevelopmentFault Detection and Control Systems

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

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Maldonado-Correa, Jorge, Martín-Martínez, Sergio, Artigao, Estefanía, Gómez-Lázaro, Emilio·2020·Energies
DOI 10.3390/en13123132
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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.

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  • Many SCADA solutions moving to IIoT for SHM and aerodynamic blade monitoring are still bulky, expensive, and require to be powered by the wind turbine itself, which often makes deployment impossible.

    “Although many SCADA solutions are moving to the IIoT field for both the Structural Health Monitoring (SHM) and aerodynamic blade monitoring, today, they are still bulky, expensive, and require to be powered by the wind turbine itself, which often makes this deployment impossible [9].”

    From Aerosense: A Self-Sustainable and Long-Range Bluetooth Wireless Sensor Node for Aerodynamic and Aeroacoustic Monitoring on Wind Turbines (2023) · cites this paper as [9]

    The Conclusions (page not available in our copy) say that the cost and complexity of installing additional wind-turbine equipment have limited its use, supporting the general deployment concern. They do not say that SCADA solutions are moving to IIoT for structural-health or aerodynamic blade monitoring, or that they are bulky, turbine-powered, or often impossible to deploy. (LLM, conf=0.88)

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Cited by

Sorted by citations

Authors
Maldonado-Correa, J., Martín-Martínez, S., Artigao, E., & Gómez-Lázaro, E.
Affiliations
  • Universidad Nacional de Loja (EC)
  • Renewable Energy Research Institute (IIER), University of Castilla-La Mancha, 02071 Albacete, Spain
  • University of Castilla-La Mancha (ES)
Corresponding author
Maldonado-Correa, Jorge
ORCID
Venue
Energies
Publisher
MDPI AG
Published
2020-06-17
Volume
13
Issue
12
Pages
3132
ISSN
1996-1073 (ISSN-L)
DOI
10.3390/en13123132
OpenAlex
W3036673944
Cited by
183
References
0
Citations by year
2026: 37 · 2025: 36 · 2024: 33 · 2023: 26 · 2022: 20 · 2021: 29 · 2020: 2
FWCI
2.65
Type
journal article
Language
en
Open access
Licence
Locations
  • Energies (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • RUIdeRA - Institutional University Repository (University of Castilla-La Mancha) (submittedVersion, other-oa)
Funders
  • European Commission (ENE2016-78214-C2-1-R)
  • Ministerio de Economía y Competitividad (ENE2016-78214-C2-1-R, ENE2016)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Aerospace Engineering › Wind Energy Research and Development
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
wind turbine condition monitoring, SCADA data, artificial intelligence, early fault detection, wind turbine failures, operation and maintenance costs, wind turbine components

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