A Data-Driven Design for Fault Detection of Wind Turbines Using Random Forests and XGboost

TLDR

Wind energy has expanded rapidly, yet turbine availability and reliability—especially offshore—remain critical cost drivers, making fault detection essential. This study develops a data‑driven fault‑detection framework for wind turbines using random forests and XGBoost. The framework ranks sensor and derived features with random forests, then trains an XGBoost ensemble classifier on the top features, validated through FAST simulations on three turbine models under below‑ and above‑rated conditions. The approach is robust across turbine types and operating regimes, outperforms SVM on multidimensional data, and mitigates overfitting.

Abstract

Wind energy has seen great development during the past decade. However, wind turbine availability and reliability, especially for offshore sites, still need to be improved, which strongly affect the cost of wind energy. Wind turbine operational cost is closely depending on component failure and repair rate, while fault detection and isolation will be very helpful to improve the availability and reliability factors. In this paper, an efficient machine learning method, random forests (RFs) in combination with extreme gradient boosting (XGBoost), is used to establish the data-driven wind turbine fault detection framework. In the proposed design, RF is used to rank the features by importance, which are either direct sensor signals or constructed variables from prior knowledge. Then, based on the top-ranking features, XGBoost trains the ensemble classifier for each specific fault. In order to verify the effectiveness of the proposed approach, numerical simulations using the state-of-the-art wind turbine simulator FAST are conducted for three different types of wind turbines in both the below and above rated conditions. It is shown that the proposed approach is robust to various wind turbine models including offshore ones in different working conditions. Besides, the proposed ensemble classifier is able to protect against overfitting, and it achieves better wind turbine fault detection results than the support vector machine method when dealing with multidimensional data.