Real-time monitoring and fault diagnosis of new energy power generation system based on deep convolution neural network

Abstract

With the increasing demand for clean energy in society, the stable operation and efficient management of new energy power generation system(NEPGS) has become a key issue. This research focuses on the real-time monitoring and fault diagnosis o fNEPGS based on deep convolutional neural network (DCNN), aiming at improving the reliability of the system, reducing downtime and improving maintenance efficiency. First of all, by constructing DCNN model, the realtime monitoring of NEPGS is realized in this study. The model can extract key features and dynamically adapt to the changes of system operation state by learning large-scale monitoring data. The experimental results show that DCNN performs well under normal working conditions and successfully captures the characteristic patterns under different working conditions. Secondly, this study applies DCNN to fault diagnosis, covering a variety of potential fault modes, such as broken teeth, tooth surface wear and so on. By introducing various fault samples into the training set, DCNN can identify and classify different fault types with high accuracy. This provides feasibility for providing real-time and accurate diagnosis when faults occur. The experimental results show that the system in this study has achieved ideal performance on the verification set, all samples have been correctly classified, and the diagnostic accuracy rate has reached 100%. This provides an advanced and feasible solution for health management and real-time monitoring of NEPGS.