Uniform blade pitch misalignment in wind turbines: a learning-based detection and classification approach

Abstract

Maintaining wind turbines in efficient and optimal working conditions is crucial to maximize energy production and reduce unexpected downtime, especially in remote or offshore installations. Pitch misalignment is one of the most common issues affecting wind turbine performance. Our previous studies addressed the automatic detection of such a fault using signals from mechanical moments collected from the fixed and rotating reference frames. Specifically, the introduced approaches involve applying machine learning techniques to ad-hoc-designed physics-based indicators, extracted from the mentioned signals, to detect the misalignment and localize the fault. Despite the fact that these approaches work effectively in the case of both single and multiple blades misaligned simultaneously, conditions in which all blades are misaligned by the same quantity have not been taken into account. Unlike individual blade misalignments, this fault presents unique challenges in its detection due to the symmetrical nature of the fault, which minimizes immediate operational disruptions but gradually impacts turbine performance and energy efficiency. To also account for this condition, in this paper, we present an innovative methodology to identify and classify uniform pitch misalignment across all wind turbine blades. This issue has been scarcely explored in existing literature, leaving a critical gap in the understanding and diagnosis of uniform pitch misalignment. Extensive results conducted with linear and turbulent wind conditions prove the effectiveness of our approach at identifying and quantifying the entity of the misalignment, thus paving the way for more efficient and reliable wind turbine diagnostics.