School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
The fault diagnosis method of wind turbine based on vibration signal is one of the focuses in the field of safe operation and maintenance of wind power. There are few faults in the main bearing of wind turbine, which makes it difficult to use data mining method to determine the fault type. To solve this problem, a data enhancement method for fault diagnosis of wind turbine main bearing is proposed. By improving the adaptability of auxiliary classification generative adversarial network (ACGAN), introducing gradient penalty, an improved ACGAN framework is constructed to improve its learning stability, and a pooling layer is introduced into the discriminator network to enhance its ability to extract data features in multi classification scenarios. The simulation results show that the improved ACGAN framework can effectively learn the distribution characteristics of the original data, has strong anti-noise interference, is more stable than the original framework in the training process, and has higher quality of generated data; it can effectively balance the fault vibration data of the main bearing of wind turbine, and further improves the accuracy of fault diagnosis of the main bearing of wind turbine.
LU Jinling, ZHANG Xiangguo, ZHANG Wei, et al. Fault Diagnosis of Main Bearings of Wind Turbine Based on Improved Auxiliary Classifier Generative Adversarial Network[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200415002.