基于Fast R-CNN和DeepLabV3+的变电站仪表盘示数自动识别方法OA北大核心CSTPCD

With the continuous development of new energy systems,the automation level of substation has a crucial impact on the stable operation of the power grid and the maintenance of metering equipment.The accurate acquisition of meter panel readings is one of the key links of achieving substation automation,which is of great significance to the status monitoring and fault diagnosis of substation metering equipment.However,due to the complexity of meter panel readings and the impact of various environmental factors such as light and angle,the automatic recognition of meter panel readings presents significant challenges.In order to solve this problem,an automatic recognition method for substation meter panel readings based on Fast R-CNN(regional convolutional neural network)and DeepLabV3+was proposed.Firstly,the target detection technology based on Fast R-CNN was analyzed theoretically,and its training process was described in detail by using the data set of substation meter panel.Then,the semantic segmentation model of meter panel based on DeepLabV3+and the reading calculation method were designed.Finally,the experiments of automatic identification of substation meter panel readings were conducted to verify the effectiveness and accuracy of the proposed method.The experimental results showed that the proposed method could recognize the readings of substation meter panel efficiently and accurately,and had good robustness.The automatic identification method for meter panel readings based on Fast R-CNN and DeepLabV3+can improve the working efficiency,safety and reduce the operation and maintenance cost of substations,and further promote the intelligent process of power systems.