Autonomous wind turbine performance curve modeling based on SCADA data: A vision intelligence powered method

A central question in efficient wind farm big-data analytics is how to design an algorithm for autonomously extracting performance curves of wind turbines based on data collected via wind farm supervisory control and data acquisition (SCADA) systems. This paper investigates this question systematically, focusing on a challenging setting: the end-to-end autonomous analytics for directly generating mathematical functions of wind turbine performance curves from raw SCADA data. We propose a vision generative modeling (VGM) paradigm for autonomous development of wind turbine performance curve models. We discover that, compared with prevalently discussed numerical fitting-based performance curve modeling (NFM) methods, VGM directly working on raw data without any data preprocessing and model parameter tuning offers more generalizable and accurate results in deriving performance curves as well as their mathematical forms. The success of VGM is achieved by three computational steps developed in this study. By comparing with a set of state-of-the-art NFM benchmarks in multiple performance curve modeling tasks, we observe that VGM consistently performs more advantageously by achieving a 75.1% accuracy improvement in wind power curve modeling with insufficient SCADA data and an 84.3% improvement in modeling the rotor speed curve based on faulty field data. This work presents a milestone in autonomous wind turbine SCADA data analytics, which possesses a great potential of spanning to autonomous analytics of measured data of other industrial systems. © 2025 The Author(s)