Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements

Abstract

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Quantifying a wind turbine’s holistic, system-level power production efficiency in its commercial operating condition is one of the keys to reducing the levelized cost for energy of wind energy and thus contributing significantly to the Sustainable Development Goal 7.2: “By 2030, increase substantially the share of renewable energy in the global energy mix.” It is so important because designers and operators need an effective baseline quantification in order to be able to identify best practices or make operation and maintenance decisions that produce actual improvements. However, this task is highly challenging due to the stochastic nature of the wind and the complexity of wind turbine systems. It is imperative to carry out accurate, trust-worthy performance assessment and uncertainty quantification of wind turbine generators. This article provides a concise overview of the existing schools of thought in terms of wind turbine performance assessment and highlights a few important technical considerations for future research pursuit.

Keywords

• data driven
• field measurements
• machine learning
• power curve
• turbine performance analysis
• uncertainty quantification