KEYWORDS: Power grids, Machine learning, Process modeling, Power supplies, Mathematical modeling, Education and training, Decision making, Performance modeling, Mathematical optimization, Computation time
Fault localization is more difficult during islanding black start in distribution grids containing distributed power sources due to the lack of local measurement devices and communication systems. This may lead to inability to accurately determine the fault location, thus affecting the restoration process. In this regard, this paper proposes a load partitioning self-recovery algorithm for islanded black-starting of distribution grids with distributed power sources. First, the distribution network black-start process is modeled as a Markov decision process, and the corresponding states, actions of intelligences and rewards are designed. On this basis, an algorithm for solving the self-healing problem of distribution network based on improved deep Q-network is proposed. The power system topology is connected to a graph convolutional network to capture the complex mechanism of power system black-start. The potential features generated in the grid topology are utilized to learn a control strategy for grid self-healing using deep reinforcement learning. Finally, the effectiveness and practicality of the proposed algorithm is verified by case studies.
The power system is constantly exposed to outdoor environments, which makes it susceptible to invasion by foreign body such as tree branches and garbage bags. Currently, most deep learning-based detection methods assume the presence of foreign body in the image, and there is still room for improvement in detection accuracy. In this paper, a foreign body detection method for the power system is proposed based on Inception-V3 and Trans-former. The method first classifies inspection images according to whether foreign bodies are present, and then detects foreign body that have invaded the power system. This method does not use pre-defined datasets and converts object detection into a direct bounding box prediction problem, which greatly optimizes existing detection methods. Experimental results on actual datasets show that our research effectively improves the accuracy and efficiency of foreign body detection compared to detection algorithms based on Faster R-CNN and YOLOv3.
To address the issues of low efficiency, insufficient accuracy, and high miss rate in traditional inspection methods for surface defects on ceramic insulators of transmission towers, this paper introduces a UAV-based intelligent inspection solution based on the deformable U-Net network to effectively detect and recognize surface defects on ceramic insulators in transmission towers. By using the deformable convolution operator to optimize the U-net network's convolution layer, the perceptual range of the convolution kernel is extended to improve the integrity of defect detail information. Meanwhile, the full-scale skip connection model is used to integrate high-dimensional and low-dimensional feature information to further improve the accuracy of ceramic insulator surface defect feature recognition. The experimental results show that the UAV-based intelligent inspection solution based on the deformable U-Net network can achieve an identification accuracy of 97.5%, an average precision of 95.55%, and an average intersection over union (IOU) of 91.67% in ceramic insulator surface defect detection. Compared with the traditional U-net method, the proposed solution in this study has improved the ceramic insulator surface defect inspection accuracy by 7.6%.
Pin defects can seriously affect the safety of transmission lines. Because the pin is small, it is difficult to detect the pin defects. Most existing methods detect pin defects by increasing the number of feature layers or cascade mechanisms. However, since there is too much redundant information in the high-resolution feature map, it is difficult for existing methods to achieve a balance between high-resolution feature maps and inference speed. In this paper, we proposed Sparse RetinaNet to effectively relieve the contradiction between high-resolution feature layer and slow inference speed. Specifically, we introduce high-resolution features in the prediction, and proposed a sparse mechanism to sparse the features in the high-resolution feature layer so as to make use of high-resolution features without seriously affecting the inference speed. Extensive experiments on our own pin defect detection dataset show that our proposed method can significantly improve training efficiency and performance.
The current conventional source-load intelligent tracking algorithm of distribution network mainly realizes active power control by calculating the regulation amount of output power, which leads to poor tracking effect due to the lack of intra-day scheduling optimization of distribution network. In this regard, the fuzzy prediction-based distribution grid source-load intelligent tracking algorithm is proposed. The multi-scenario technology is used to model the power output of distribution network power devices and the power load, and build the tracking scenario model; the intra-day optimization model is built, and the MPC control method is combined to realize the control of the power output and load situation of the distribution network; finally, the power fluctuation index is introduced to characterize the source-load tracking situation. In the experiments, the power control performance of the proposed method is verified. The experimental results show that the maximum power fluctuation value is low when the proposed method is used for source-load tracking, and it has a better power control performance.
The traditional energy-saving and load matching strategies for distribution networks have the problem of low accuracy in predicting the capacity of power equipment. Therefore, a new intelligent energy load matching strategy is proposed, which uses deep learning algorithms and K-means clustering algorithms to process and standardize power data, extract data features, and construct a capacity prediction model for energy storage devices in distribution networks. By finetuning the model structure network, the load condition of the distribution device is predicted, and the dynamic matching of source and load is achieved. Experimental verification shows that the matching effect of this strategy is superior to traditional methods, with a significant reduction in unit output and a high source load matching rate. This method has good application prospects in improving the energy utilization efficiency and reliability of distribution networks.
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