Data Analytics
for Wind Farms
At Nabla Wind Hub, we transform wind turbine operational data into quantified technical diagnostics through the structured integration of multiple data sources, enabling the transition from conventional monitoring to an advanced tool for risk management and asset optimization:
- SCADA data and CMS (Condition Monitoring Systems).
- O&M records and work orders.
- Structural inspection results.
- Aeroelastic models and FEM analyses.
- Environmental and wind resource data.
The correlation of these data with physical models enables the identification of deviations, anticipation of degradation, and prioritization of technical decisions aimed at maximizing availability and extending service life in a controlled manner.
Eder Murga
Head of Operations
Data Analytics
Advanced operational and pre-construction data processing capabilities enable:
Structured Data Integration
Consolidation of information from SCADA, CMS systems, O&M records, structural inspections, aeroelastic/FEM models, and environmental data into a coherent and traceable data architecture.
Correlation with Physical Models
Linking real operational data with structural models to interpret loads, damage accumulation, and deviations from expected behavior.
Quantified Technical Diagnosis
Identification of anomalies, degradation trends, and structural risk through the combined analysis of historical data and physical modeling.
Methodological Protection
The integration and correlation process is internally developed, based on validated physical models and protected under a proprietary methodological framework.
Predictive Diagnosis Based on Real Data
Nabla Wind Hub’s Advanced Data Analytics service integrates wind farm operational data with validated physical models to generate a quantified technical diagnosis of structural behavior.
The analysis combines SCADA information, CMS systems, O&M records, and structural inspections with results derived from aeroelastic and finite element models, enabling evaluation of the actual behavior of each turbine compared to its design conditions and expected operation.
This approach enables the identification of deviations, degradation trends, and structural risks based on defined technical criteria.
Eder Murga
Head of Operations
Data-Driven Diagnosis Based on
Comparative Analysis and
Structural Insight
The Advanced Data Analytics service analyzes wind and operational data on a turbine-by-turbine basis, integrating SCADA records, alarms, O&M history, and structural information.
This analysis makes it possible to characterize the actual operational behavior of each turbine in comparison with expected conditions and industry benchmarks.
From this evaluation, the following technical indicators are obtained, among others:
- Power curves and power coefficient (Cp).
- Generator and rotor speed.
- Pitch angle and turbulence estimation.
- Wind speed distribution.
- Identification of misalignments during production and idling.
- Classification of shutdowns based on alarm records.
- Analysis of reported failures and associated corrective actions.
Technical Application of Results
The results enable classification of turbine operational behavior and identification of significant deviations from expected performance, establishing technical criteria for prioritizing inspections and structural monitoring.
The analysis is consolidated into a technical report that can serve as a basis for subsequent structural integrity or life extension assessments.
SCADA as the Basis for Diagnosis
and Operational Control
Through structured analysis of SCADA data, a technical evaluation of each turbine’s operational behavior is obtained, enabling:
- Characterization of each turbine’s individual operating regime.
- Detection of performance deviations and anomalous behavior.
- Identification of yaw misalignment in both production and idling conditions.
- Quantification of turbine-specific wind conditions, including speed distribution and turbulence estimation.
- Classification of shutdowns and events based on alarm records, distinguishing routine and non-routine events.
- Categorization of units according to operational behavior to define technical inspection and monitoring criteria.
This service is carried out fully independently from the OEM and without interfering with FSA or O&M contracts, enabling an objective technical evaluation based exclusively on real data and validated physical models.
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