Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version

A Self-Reconfigurable and Fault-Tolerant Induction Motor Control Architecture for Hybrid Electric Vehicles

International audienceThis paper describes an adaptive control system for an induction motor drive that propels a Hybrid Electrical Vehicle (HEV). It has been designed to comply with the major requirements of HEVs electric propulsion. The fault tolerant controller is based on a Field Oriented Control with 4 IP regulators, a speed sensor and two observers (Extended Kalman Filter (EKF) and an Adaptive Observer (AO)) to guarantee the best dynamic performances required by the application and also to improve the reliability in the event of sensor loss or sensor recovery. The tuning of the observers is based on extensive simulations, experimental results and optimization procedure within an open-loop type approach. The fault tolerant controller reorganization is based on a control decision block implemented with a Maximum Likelihood voting algorithm. The results of the control system show the effectiveness of the approach. Indeed experimental results of the EKF used in closed loop confirm the validity of the sensorless controller and the fault tolerant controller simulation results in the event of speed sensor loss and recovery are very promising even in case of stator resistance variation