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Wind turbine database for intelligent operation and maintenance strategies
Pere Marti-Puig1, Alejandro Blanco-M2, Jordi Cusidó2,3
1Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500, Vic, Catalonia, Spain. pere.marti@uvic.cat.
Scientific Data
|February 29, 2024
Summary
Researchers can now access a 3-year Supervisory Control and Data Acquisition (SCADA) database from five wind turbines. This dataset aids in developing intelligent operation and maintenance strategies for wind energy systems.
Area of Science:
- Renewable Energy Engineering
- Data Science for Industrial Applications
Background:
- Intelligent operation and maintenance strategies are crucial for optimizing wind turbine performance and longevity.
- Existing datasets may not capture the comprehensive, high-resolution data needed for advanced analytics.
Purpose of the Study:
- To present an extensive 3-year Supervisory Control and Data Acquisition (SCADA) database from five Fuhrländer FL2500 2.5 MW wind turbines.
- To provide researchers with a valuable resource for developing intelligent operation and maintenance strategies for wind energy systems.
Main Methods:
- Collected 312 analogous variables from 78 sensors at 5-minute intervals over three years.
- Recorded minimum, maximum, mean, and standard deviation for each sensor.
- Included alarm events with system, subsystem, and descriptions.
Main Results:
- A comprehensive SCADA database is now available.
- Functions for downloading specific data subsets in Matlab, R, and Python are provided.
- An example demonstrates detecting rotor malfunction using gearbox variables and normality modeling.
Conclusions:
- The presented database is a significant resource for wind energy research.
- The normality modeling approach shows promise for early detection of wind turbine malfunctions.
- Accessible data and tools facilitate advancements in intelligent wind turbine operation and maintenance.
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