Implementation of Fault Diagnosis of Wind Turbine Based on Signal Analysis with NN Algorithm

The most effective method to ensure the stable operation of wind turbines, reduce maintenance costs, through the remote monitoring system to monitor and analyze real-time operating state of its real-time operation. Firstly, the remote monitoring system based on PC is constructed by using the Ethernet gateway of wireless sensor network to overcome the environment of the position constraint. Then, we collect the measured signal data of a distributed node to install the wireless sensor network in wind turbine farms, and extract feature information through empirical mode decomposition (EMD) analysis to classify the fault and normal signal pattern. In the experiment, the EMD learning using the following fault signal as an example of the back propagation (BP) neural network algorithm with the generator vibration, the rotor imbalance, and the bearing fault. In this paper, a fault diagnosis method based on signal analysis and recognition is presented, and the validity of the method is demonstrated by simulation.