Constrained Kalman filter based detection and isolation of sensor faults in a wind turbine

Abstract

Reliability, Survivability, Cost efficiency and high performance are required for modern wind turbines to be competitive within the energy market. In this paper, a viable model-based fault detection and isolation (FDI) technique is designed to detect different sensor fault scenarios of the benchmark model of horizontal-axis standard modern wind turbines. A model-based fault detection and isolation (FDI) technique is developed with deeper insight into the process behavior by using a set of constrained Kalman filters, and then estimating the effectiveness factor for the faulty sensor in the presence of system disturbances and random noise. The effectiveness of the proposed scheme has justified by simulation result on a 4.8 MW, variable-speed, variable-pitch wind turbine model.