Wind Turbine Multi-Fault Detection and Classification Based on SCADA Data

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.

A Survey on Power Grid Faults and Their Origins: A Contribution to Improving Power Grid Resilience

“…Another application of SCADA systems is fault detection in renewable sources, for example in wind turbines [109]. In [110], a SCADA system was used to detect and classify faults in wind turbines by using high-frequency sampling from SCADA sensors. Also, using real-time SCADA data from two wind power plants, a fault prediction and diagnosis methodology for wind turbine-based generators was accomplished [111].…”

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

“…In this study, we propose an SHM strategy to detect and classify structural changes using two‐step data integration (type unfolding and the so‐called mean‐centered group scaling (MCGS), data transformation using principal component analysis (PCA), and two‐step data reduction combining PCA and ‐SNE. PCA is a common technique that is mainly used for dimensionality reduction or feature extraction in the field of pattern recognition; moreover, it can also be applied to detect and classify structural changes or faults . In our study, the PCA model will help to detect different types of damage, not only the healthy structures.…”

Feature Selection Algorithms for Wind Turbine Failure Prediction

“…On the SVM side, authors such as Vidal et al [33] focus on using a multiclass SVM classifier to detect different failures. They use a pre-analysis of the contribution of each variable by the means of PCA.…”

“…In many papers of the state of the art research we can see that the selection of the variables is done manually by an expert, or based on the perception of the author according to the subsystem to analyze. Some authors, such as [29,33,42,43], include some type of reduction stage by correlations or PCA, but do not make a comparison of selection methods, or this comparison does not contain methods that include the interaction of more than two variables such as those presented in this paper.…”