基于深度神经网络的电厂跑冒滴漏智能识别方法研究OACSTPCD

The complex equipment of power plant is prone to the problem of leakage.There are some problems in manual inspection,such as delayed discovery,human negligence,and unable to convey the abnormal situation in real time.Based on the deep learning con-volutional neural network,transfer learning and few-shot learning technology,an intelligent recognition and alarm system for abnormal state of power plants is designed,deep learning model is used to detect the field pictures captured by the monitoring system,common equipment leakage is identified and warnings are given accurately and timely,so as to improve the safety supervision of the power system and the ability to respond to accidents.The relatively mature YOLOv5 is adopted as the basic framework of target detection network.Ai-ming at the problem of sparse leakage data,the network structure is optimized and the transfer learning and few-shot learning methods are adopted to improve the accuracy of network recognition.The results show that the power plant abnormal state intelligent recognition and alarm system based on deep learning convolutional neural network can keep the accuracy and real-time recognition of power system abnormal state.The system can realize autonomous all-weather intelligent detection,timely push alarm information,reduce the possible omissions because of the use of human attention monitoring equipment to check abnormal status,reduce the operation and maintenance costs of the power system,and improve the power system safety supervision and emergency ability.