Fault Recognition under Weak Grid Condition Based on Doubly Fed Induction Generator With Customized Deep Learning Network
ZHANG Tianchi1，YU Zinan2 *
1.Software Collage ,Southeast University，Nanjing 214135，China；2.Jilin Power Supply Company, State Grid Jilin Electric Power Co., Ltd., Jilin City 132000, China
Abstract: In view of the problem of fault detection under the condition of doubly-fed wind turbine access to the weak AC grid at present, a fault recognition and detection algorithm under the framework of customized deep learning network is proposed. Firstly, the actual system data is used and normalized to zero-mean standard data, and then the data set is trained by customized deep learning network to form a pre-training network. At the same time, abnormal data is simulated and input to the previously pre-trained network to output prediction data. The threshold is calculated according to the prediction data and simulated abnormal data. When the absolute value of the difference between the prediction data and the simulated abnormal data exceeds the threshold, the system data is judged to be abnormal. The simulation results show that the proposed method can solve the fault recognition problem more accurately and meet the requirements of fault detection in a weak grid containing doubly-fed wind turbines.
张天驰, 于紫南. 基于自定义深度学习网络双馈风机接入弱电网故障识别[J]. 吉林化工学院学报, 2024, 41(5): 54-59.
ZHANG Tianchi, YU Zinan. Fault Recognition under Weak Grid Condition Based on Doubly Fed Induction Generator With Customized Deep Learning Network. Journal of Jilin Institute of Chemical Technology, 2024, 41(5): 54-59.
