Power Performance Verification of a Wind Farm Using the Friedman's Test

Area of Science:

• Renewable Energy Engineering
• Statistical Analysis in Engineering
• Wind Turbine Technology

Background:

• Wind farm power performance verification is crucial for operational efficiency and financial returns.
• Existing methods may not fully capture the nuances of power output across multiple turbines.
• Supervisory Control and Data Acquisition (SCADA) systems provide rich data for performance analysis.

Purpose of the Study:

• To introduce a novel nonparametric statistical method for wind farm power performance verification.
• To assess whether a wind farm's power performance deviates significantly from expected benchmarks.
• To provide wind farm owners with a clear determination of 'perfect' or 'acceptable' power performance.

Main Methods:

• Utilizing Friedman's test, a nonparametric statistical inference technique.
• Leveraging data collected by the SCADA system from embedded wind turbine sensors.
• Treating the guaranteed power curve as an additional data point for comparison.
• Employing multiple comparison methods to identify inter-turbine performance differences.

Main Results:

• The proposed method was applied to verify the power performance of an actual wind farm.
• The analysis determined that the specific wind farm under assessment exhibited acceptable power performance.
• The method successfully identified significant differences in power performance between individual turbines.

Conclusions:

• The developed method offers a robust approach for wind farm power performance verification.
• Friedman's test provides a reliable nonparametric alternative for performance assessment.
• The findings confirm the acceptable operational status of the evaluated wind farm.