Wind Turbine Performance Monitoring and Optimization
Summary
Wind turbine performance monitoring and optimization encompasses the continuous assessment of energy conversion efficiency, mechanical health and operational reliability of onshore and offshore turbines. Central to this field is the acquisition of high-resolution operational data—typically via Supervisory Control and Data Acquisition (SCADA) systems—and the interpretation of power curves and operational curves to identify deviations from expected behaviour. Modern approaches integrate data-driven machine learning algorithms with physical modelling to detect incipient faults, quantify performance decline with age, and evaluate retrofitting interventions under real-world wind conditions. Key optimisation strategies include enhanced blade aerodynamics, active pitch and torque control, predictive maintenance scheduling and component repowering. By reducing unscheduled downtime, improving energy yield and extending service life, performance monitoring and optimisation play a pivotal role in lowering levelised cost of energy and advancing the integration of wind power into a renewable-based energy system.
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Wind Turbine Performance Monitoring and Optimization publication trend
The graph below shows the total number of articles in wind turbine performance monitoring and optimization across all publications each year (not limited to Nature Index journals).
Technical terms
SCADA (Supervisory Control and Data Acquisition): A centralised system for real-time collection and analysis of turbine operational and environmental data.
Power Curve: The empirical relationship between wind speed at hub height and electrical output, used as a benchmark for normal performance.
Capacity Factor: The ratio of actual energy produced over a period to the theoretical maximum output if a turbine operated at full rated power continuously.
Gaussian Process Regression: A non-parametric, probabilistic machine learning method for modelling complex nonlinear relationships and uncertainties.
Binning: A standard technique for constructing power curves whereby wind speed measurements are grouped into discrete intervals to compute average power output.
Condition Monitoring: The practice of continuously evaluating turbine health indicators—such as vibration, temperature and power deviations—to predict failures and schedule maintenance.
References
- What drives the change of capacity factor of wind turbine in the United States?. Environmental Research Letters (2023).
- Wind Turbine Technology Trends. Applied Sciences (2022).
- Predicting Underwater Noise Spectra Dominated by Wind Turbine Contributions. IEEE Journal of Oceanic Engineering (2024).
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