Fault Classification in Wind Turbines Using Principal Component Analysis Technique In this study, dimensionality reduction and feature extraction techniques are applied to fault classification for wind turbine systems using principal component analysis (PCA). Experimentations are performed in terms of two topologies of faulty datasets which are respectively actuator effectiveness loss and sinusoidal fault, occurring in a 4.8MW wind turbine benchmark system. In order to evaluate the PCA technique, additive white Gaussian noise (AWGN) signals are introduced to the aforementioned two faulty conditions respectively. The experimental results demonstrate that PCA not only can extract the significant features but also distinguish different types of fault effectively in this wind turbine benchmark system.

Timeseries Deep Learning Fault Detection with the Application of Wind Turbine Benchmark In this paper, a deep learning fault detection approach is proposed based on the convolutional neural network in order to cope with one class of faults in wind turbine systems. Fault detection is very vital in nowadays industries due to the fact that instantly detection can prevent waste of cost and time. Deep learning as one of the powerful approaches in machine learning is a promising method to identify and classify the intrigued problems, which are hard to solve by classical methods. In this case, less than 5% performance reduction in generator torque along with sensor noise, which is challenging to identify by an operator or classical diagnosis methods is studied. The proposed algorithm, which is evolved from convolutional neural network idea, is evaluated in simulation based on a 4.8 MW wind turbine benchmark and the accuracy of the results confirms the persuasive performance of the suggested approach.