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The power operation site typically faces high voltages, strong currents, and other environmental hazards. Safety fence as a boundary for power operation is of great importance to ensure the safety of operators. The preparation of operation fence is temporary and random, and the risk identification is a complex problem that involves a dynamic interaction between individuals and fence. The existing methods of determining the safety area based on prior knowledge are insufficient. Therefore, this paper proposes a risk identification method based on neighborhood correlation networks and vector calculation. In this method, firstly, the key targets of fence operation are detected by the target detection method. Then, considering that the fence operation target has the characteristics of flat shape and small size; the region proposal network by guided anchoring (GA-RPN) and neighborhood-related network module is introduced to improve the detection accuracy. Finally, the vector calculation criterion is used to judge whether the operator is in the safety fence area, to realize the risk identification of the operation fence overrun. Experiments are conducted on available power fence operation datasets, and the obtained results showed that the proposed method presents better performance than the classical target detection methods.

The contradiction between the demand of massive intelligent scene caused by the interconnection of things in power system and the bottleneck of the chip itself is becoming more and more serious. How to realize the self-controllable and domestic substitution of power chips has been widely concerned in the power field. Therefore, this paper analyzes the demand for power chips in power system and discusses the scene specificity of power chips. Then, it combs the design architecture of power chips and discusses the application scenarios and research focuses of power chips. Finally, the key scientific problems and major technical bottlenecks of power chips are prospected.

The icing monitoring of transmission lines is of great significance to prevent freezing disasters of transmission lines and ensure the security and stability of power system. Measurement methods based on image recognition is gradually applied for icing monitoring but fails to distinguish the icing thickness under the extreme meteorological conditions. Therefore, this article built an ice monitoring system based on edge intelligence to achieve the ice thickness identification at the icing monitoring terminal and proposed a lightweight vision identification method based on discriminative-driven channel pruning for icing monitoring terminals with limited computing resources. In this method, the lightweight convolutional neural network (CNN) MobileNetV3 is utilized for feature extraction, and the multiscale target detection network SSD is used to extract the high-dimensional feature information for ice monitoring. Then, the channel pruning driven by discrimination is used to further compress model, so as to obtain a lightweight identification method suitable for ice monitoring terminals with limited resources. The effectiveness and superiority of the proposed method are verified in comparison with other CNN-based target detection methods and traditional ice monitoring methods. The experimental results showed that the proposed method has 74.5% recognition accuracy for the ice images collected under extreme meteorological conditions. Moreover, the model size is only 11 MB, which can be deployed in ice thickness monitoring terminals with limited computing resources.

The Unmanned Aerial Vehicle (UAV) inspection mode has been gradually implemented in the power system. The UAV inspection image is checked by the target detection technology, but there is no high-precision target detection algorithm as the technical support. In this regard, this paper proposes a target detection algorithm based on the improved RetinaNet which is suitable for transmission lines defect detection. In this algorithm, the shortcomings of the RetinaNet anchor frame extraction mechanism based on Apriori are corrected. At the same time, the number and size of anchor frames are redesigned by using the improved K-means + + algorithm, so that the anchor frame of the improved algorithm gets the highest average IoU (Intersection over Union) value, which matches the actual size of the transmission line defects. Then, in RetinaNet, the feature pyramid network based on DenseNet is built as the backbone network to improve the model accuracy and make the model lighter. The improved model is trained and tested by using the data set of transmission line defects for validation. The results show that the proposed method has advantages and effectiveness in the detection of transmission line defects, and meets the requirements of intelligent inspection in terms of accuracy.

The quality of the camera image directly determines the accuracy of the defect identification of the transmission line equipment. However, complex external factors such as haze can seriously affect the image quality of the aircraft. The traditional image dehazing methods are difficult to meet the needs of enhanced image inspection in complex environments. In this paper, the image enhancement technology in haze environment is studied, and an image dehazing method of transmission line based on densely connection pyramid network is proposed. The method uses an improved pyramid network for transmittance map calculation and uses an improved U-net network for atmospheric light value calculation. Then, the transmittance map, atmospheric light value, and dehazed image are jointly optimized to obtain image dehazing model. The method proposed in this paper can improve image brightness and contrast, increase image detail information, and can generate more realistic deblur images than traditional methods.

Polluted insulators seriously threaten the safe and stable operation of power grids, which attaches great significance to insulator contamination perception. Among the present methods, the non-contact approaches based on infrared images have gradually been widely used, as they are much more safe and are of low cost. However, the thermal effect of insulators is largely affected by meteorological conditions, which makes the infrared image-based methods less accurate. To solve the above problem, we take infrared image and meteorological parameters including humidity and temperature as input, and propose a feature fusion model to perceive insulator contamination in different weather conditions. Firstly, different feature extraction networks are used to perform feature extraction on the two types of data; secondly, the two features are concatenated to fuse together; thirdly, further feature extraction is performed and contamination is classified according to the pollution severity. Case studies show that the proposed method can better explore the relationship between humidity, temperature and pollution level of the insulators, thus can better separate the contamination grades and outperform the conventional infrared image based methods.