Fault Detection for Wind Energy using Multi-Scale Statistics

Abstract

The growing demand for renewable energy and the increased installation of wind turbines have brought challenges related to operational efficiency and predictive accuracy. In this thesis, we extend the method 'Narrowest Significance Pursuit' (NSP) to non-linear frameworks and explore the application of Non-Linear NSP to detect multiple change points in wind energy production data. By identifying structural shifts and anomalies, NSP can significantly enhance predictive models used in wind power forecasting. The study introduces two parametric as well as isotonic regression and S-shaped non-parametric models to handle non-linearity in wind turbine data.
The results demonstrate superior performance of isotonic regression over parametric approaches, especially in terms of detecting gradual changes and subtle anomalies. However, issues like model misspecification and computational inefficiency remain, prompting further optimization efforts. We also recommend expanding NSP methods to other renewable energy sectors, such as solar energy, to broaden the applicability of these models.