Abstract
Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.
Highlights
Wind energy offers many advantages, as it is an inexhaustible clean fuel source
Support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work
In this work, we propose a strategy to detect and classify multiple wind turbines (WTs) faults using only conventional Supervisory Control and Data Acquisition (SCADA) data with an additional, but feasible, high-frequency sampling from the sensors and without the added cost of retrofitting additional sensors to the turbine
Summary
Wind energy offers many advantages, as it is an inexhaustible clean fuel source. This explains why it is one of the fastest-growing renewable sources against greenhouse effects. The more recent trend in this type of literature review is to focus on a specific WT sub-assembly: the bearings and planetary gearbox [3,4], the generator and power converter [5,6], the blades [7,8], etc Most of these methods, which focus on a specific part of the WT, require the choice of the most appropriate sensors, their advisable position in the sub-assembly, and the most convenient strategy to extract as much information as possible from the obtained data. The only requirement is to increase the frequency rate in the SCADA data from the already available sensors Following this idea, in this work, we propose a strategy to detect and classify (through SVM) multiple WT faults using only.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
