Blade icing detection of wind turbine based on multi-feature and multi-classifier fusion

Abstract

Wind farms are usually located in high altitude areas with a high probability of ice occurrence. Blade icing has the potential to result in unexpected mechanical failures and downtimes. In order to avoid these problems, the priority we need to do is to detect blade icing accurately. For this purpose, a novel icing detection method based on multi-feature and multi-classifier fusion is proposed in this paper. Firstly, multi-feature composed of basic features and statistical features are extracted from the operational data. Significant features are then extracted by utilizing Light Gradient Boosting Machine. Secondly, a multi-classifier fusion approach is employed to build an fusion model, which aims to obtain a much more accurate estimation for the icing state. Overall, the proposed method in this paper can achieve more accurate detection on blade icing, compared with other models. This will minimize false alarms, helping wind farms manage the operations more efficiently.

A Review of Wind Turbine Icing and Anti/De-Icing Technologies

“…Based on rotor rotation speed measurement, a turbine model and a data-driven algorithm, Stotsky et al [85] estimated the prediction error and the turbine moment of inertia by explicitly solving the discretized single-mass turbine model, and improved the accuracy of icing monitoring from the data processing. A multi-feature composed of basic features and statistical features was constructed to characterize the blade icing relevant information [86], and a multi-classifier fusion method was used to build an icing detection model and identify the icing status. The method considers the influence of various features on the icing of turbine blades to improve the accuracy of the icing monitoring.…”