The flashover performance of insulators is the foundation of the calculation of precise lightning protection. The material and structure of an insulator affect its flashover performance but the research on the differences in the flashover performance of different insulators and the analysis of the reason for these differences have not been clear. In this paper, lightning impulse flashover tests of two types of insulators (composite and glass insulators) were carried out in the National Engineering Laboratory (Kunming) for Ultrahigh-voltage Engineering Technology. In these tests, the insulators were hung in a 110 kV double-circuit transmission tower and a standard lightning impulse (1.2/50 µs) was applied to the insulators. The discharge path and the 50% impulse flashover voltage of the insulators were recorded. At the same time, the electric fields in the vicinity of different insulators were calculated using the finite element method with COMSOL Multiphysics. The electric field distribution and the uneven coefficient were analyzed. Combined with the flashover test data and the electric field simulation, the relationship between the flashover performance (discharge path and 50% impulse flashover voltage) and the electric field (electric field distribution and uneven coefficient) is found. The simulation results are in accordance with the test data and the conclusions can provide useful references for the external insulation design of transmission lines and the optimization of insulators.

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In recent years, data have shown that transmission line icing is the main problem affecting the operation of power grids in bad weather; it greatly increases operating costs and affects people’s lives. Therefore, the development of a calculation method to predict the risk of ice on transmission lines is of great importance for the stability of the power grid. In this study, we propose a maximum mutual information coefficient (MIC) and grid search optimization extreme gradient boosting (GS-XGBoost) transmission line ice risk prediction method. First, the MICs between the ice thickness and the precipitation, wind speed, wind direction, relative humidity, slope, aspect, and elevation characteristic factors are calculated to filter out the effective features. Second, a grid search method is used to adjust the hyperparameters of XGBoost. The resulting GS-XGBoost model builds a prediction system based on the best parameters using a training set (70% of the data). Finally, the performance of GS-XGBoost is evaluated using a test set (30% of the data). For multiline, cross-regional icing data, our experimental results show that GS-XGBoost outperforms other machine learning methods in terms of accuracy, precision, recall, and F 1 score.

With the steady progress of the intelligent development of power systems, as well as the higher demand for power supply reliability. It is essential to achieve the effective monitoring of substations 24 h a day. The vigorous development of deep learning network brings strong theoretical and technical support to the unmanned and intelligent construction of the substation. To identify the on/off state of the isolation switch in the substation robot inspection image, this paper proposes a method for identifying the isolation switch state of YOLOv4 (You Only Look Once V4) network based on transfer learning. Firstly, for the insufficient number of samples, transfer learning is introduced, and the network feature extraction layer is pre-trained by using public data sets. Secondly, images of isolation switch are obtained by a fixed camera and inspection robot in the substation, and data set of isolation switch is constructed. Finally, the isolation switch data set is used to train the YOLOv4 network. The test results show that compared with YOLOv3 and YOLOv4, the network can improve the identification precision of the isolation switch.

Insulators play an important role in the operation of outdoor high-voltage transmission lines. However, insulators are installed in outdoor environments for long periods and thus failures are inevitable. It is necessary to conduct timely insulator inspection and maintenance. In this paper, an improved Yolov3 target detection network (Yolov3-CK) is proposed in order to achieve higher detection accuracy and speed. First, Yolov3-CK uses the CIOU loss function instead of the mean square error loss function from Yolov3. Second, the Yolov3-CK model uses cluster analysis of the priori box via the k -means++ algorithm to obtain a priori box size that is more suitable for the detection of insulators and their burst faults. Finally, we use a dataset obtained by performing data enhancement on the China power line insulator dataset to train and test the data-enhanced Yolov3-CK model. The mean precision of Yolov3-CK reaches 91.67% with 47.9 frames processed per second. Yolov3-CK provides better detection accuracy and a higher processing rate than Faster RCNN, SSD, and Yolov3. Therefore, the Yolov3-CK model is more suitable for the detection of insulators and their burst faults.