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With the continuous development of energy infrastructure, a large number of distributed new energy, distributed energy storage, and various power electronic equipment are connected to distribution communities. The access to a large amount of equipment not only increases the workload of power operators, but also leads to a complex field operation environment, which will threaten the security of field operators. Traditional monitoring strategy for distribution community operation site relies on manual operation, which is tedious, labor-intensive, and error-prone. To solve this problem, this paper proposes a security monitoring strategy for distribution community operation sites based on an intelligent image processing method. Firstly, a power image enhancement method based on multi-filtering algorithm is proposed. The filter operators are used to smooth and denoise the image, enhance the edge and detail information, and expand the security monitoring data set of operation sites by merging the filtered images. Secondly, an object detection method for personnel and security protection tools based on the improved Faster R-CNN algorithm is proposed, and a pyramid structure is constructed to improve the detection and localization accuracy of small objects such as safety helmets and safety belts. To simplify the object detection task and the complexity of the labeling system, it is disassembled into four single tasks: operators, non-operators, safety helmets, and safety belts. Finally, the detection frame labels are fused by calculating the overlap of the object detection boxes to describe the security of the operator. Experiments show that the method proposed in this paper can effectively locate the object and accurately describe the security of the operator.

To solve the problem that the active management strategy is not considered in the current active distribution network expansion planning research, we propose a new active distribution network expansion planning method considering active management strategy. Combining active management with distribution network extend planning and DG locating and sizing. Considering the impact of DG active management on the distribution network, the two-layer planning model is used to optimize the line structure of the distribution network and the locating and sizing of the distributed generation. The upper model is optimized by the binary particle swarm optimization algorithm, and the lower model is optimized by the particle swarm optimization algorithm. The analysis of the example shows that the proposed method can reduce the economic cost and improve the reliability of the distribution network.