Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

Abstract:

This work addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained and projected into the baseline PCA model. When both sets of data are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some fault. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large wind turbine in the presence of wind turbulence and realistic fault scenarios.

• Periodical: Advances in Science and Technology (Volume 101)
• Pages: 45-54
• DOI: https://doi.org/10.4028/www.scientific.net/AST.101.45
• Online since: October 2016
• Authors: Francesc Pozo*, Yolanda Vidal
• Keywords: FAST (Fatigue, Aerodynamics, Structures and Turbulence), Fault Detection, Principal Component Analysis (PCA), Statistical Hypothesis Test, Wind Turbines

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