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  4. A Methodological Framework for Managing the Alarms in Wind Turbine Control and Data Acquisition Systems for Failure Analysis
 
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A Methodological Framework for Managing the Alarms in Wind Turbine Control and Data Acquisition Systems for Failure Analysis

Journal
Machines
Date Issued
2024-09-01
Author(s)
Castillo-Navarro, Javier
Departamento de Industrias  
Kristjanpoller Rodriguez, Fredy Ariel  
Departamento de Industrias  
Mena, Rodrigo 
Departamento de Industrias  
Godoy, David R.
Departamento de Industrias  
Viveros, Pablo  
Departamento de Industrias  
DOI
10.3390/machines12090597
Abstract
<jats:p>Renewable energies have a fundamental role in sustainability, with wind power being one of the most important due to its low production costs. Modern wind turbines are becoming bigger and more complex, and their operation and maintenance must be as optimized as possible. In this context, supervisory control and data acquisition systems provide valuable information, but there is no precise methodology for their analysis. To overcome this need, a generalized methodology is proposed to determine the recognition of critical subsystems through alarm analysis and management. The proposed methodology defines each subsystem in a precise way, shows the indicators for the alarms, and presents a theoretical framework for its application using the quantity and activation times of alarms, along with the real downtime. It also considers the transition of states when the wind turbine is operationally inactive. To highlight the proposal’s novelty, the methodology is exemplified with a case study from the Southern Cone, applying the method through a data management and analysis tool. Four critical subsystems were found, with the alarms of wind vanes, anemometers, and emergency speeds being of relevance. The indicators and the graphical tools recommended helped guide the applied analysis.</jats:p>
Funding(s)
ANID/FONDEF
Subjects

availability

reliability

SCADA

wind energy

wind turbine generato...

File(s)
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Name

machines-12-00597 (1).pdf

Size

6.25 MB

Format

Adobe PDF

Checksum

(MD5):a0b426fa4eb62e904439ec3153e69bc6

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