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Intelligent recognition of switch state based on computer vision is one of the important research contents of power intelligence. For solving the problem of low recognition rate under poor imaging environment, this paper presents an improved VGG19 network. There are 3 improved aspects: 1) in the first convolutional layer, the convolution features are extracted from the RGB image and the LBP image of the switch, and then the two convolution features are averaged as the output result of the first convolution layer, in order to enhance the adaptability of features to illumination changes and the ability to extract detailed features; 2) in the subsequent 15 convolution layers, the convolution feature output of each layer is averaged with the original input, and then convolved again, so as to extract detail features by secondary convolution; 3) we selects CReLU instead of ReLU as activation function for reducing the redundancy of the convolution kernel. Experiments show that the new method can greatly improve the performance of switch state recognition.

This paper uses the relevant knowledge of complex network theory to analyze the static characteristic parameters such as node degree, node degree distribution, network average length and node tightness of W city rail transit network, electric power distribution network and load network. The importance index of the nodes is analyzed, by taking W city rail transit network and electric power distribution network as the research object of the composite network. At the same time, this paper points out the important nodes in the rail transit network of W city. The energy efficiency coefficient changes of W city rail transit network and rail transit composite network under random attack and deliberate attack mode is simulated, which provides reference for the subsequent protection and planning of relevant departments.

Detecting workers and equipment through images/videos can assist in safety monitoring, quality control, and productivity management at construction sites. Currently, the dominant method for detecting is Deep Neural Networks (DNNs). To apply this method, the DNNs always need to be trained on image datasets that contain objects at the construction site. However, a large-scale and publicly available image dataset for detecting objects at construction sites is still absent, and this hinders research in this field. In this study, the Moving Objects in Construction Sites (MOCS) image dataset is presented. The dataset contains 41,668 images collected from 174 different construction sites. Thirteen categories of moving objects found in construction sites were annotated. Furthermore, the objects were precisely annotated using per-pixel segmentations to assist in precise object localization. A detailed statistical analysis was performed in this study. Finally, a benchmark containing 15 different DNN-based detectors was made using the MOCS dataset. The results show that all detectors trained on the dataset could detect objects at construction sites precisely and robustly.

Electric vehicle, as a kind of moving load, its behavior of charging and discharging will have a huge impact on the power grid. This article does some researches on coordination charging and discharging strategy of electric vehicles between different regions. Considering the load characteristics of the area and parking rules of electric vehicles, the paper proposes an optimal control strategy from different perspectives of the power grid side, intermediary side and user side. The power grid side considers load fluctuations; the intermediary side considers the benefits of charging and discharging, the user side considers charge-discharge cycles and user convenience. Together it establishes the corresponding multi-objective function and uses multi-objective genetic algorithm to calculate the result. Finally a demonstration is given to prove the validity of this method. Simulation result shows that the multi-objective optimization method can achieve the overall demand of the power grid side, intermediary side and user side.