Fault detection and isolation of floating wind turbine pitch system based on Kalman filter and multi-attention 1DCNN
Yu-Cheng Wang, Chuanbo Wen, Xianbin Wu
Abstract
In this paper, the fault detection and isolate problem is investigated for the pitch system of floating wind turbine. In the addressed system model, the system noises and measurement noises are correlated, and the measurement is affected by the missing phenomena. A Kalman filter is designed to handle the correlated noises and estimate the pitch angle, and a residual of the measurement of the pitch system is constructed to detection the faults. Then the fault isolation algorithm is presented based on a multi-attention mechanism one-dimensional convolutional neural network, which is employed to accurately isolate the faults. The simulation results show that the proposed method can significantly improve the accuracy of fault detection and isolation, which the fault isolation accuracy of the simulation results reaches 99.15%.