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Dissolved gas analysis is important for the early warning and diagnosis of transformer faults. Aiming at the problems of numerous types of features for dissolved gas in oil and the insufficient analysis of fault associated features, a new fault diagnosis method for oil-immersed transformers based on feature selection of dissolved gas in oil is proposed. Firstly, the derivation of original features for dissolved gases is completed. The optimal combination of features is selected by calculating the importance of features for fault diagnosis based on random forest (RF). Then, the tree-structured parzen estimator (TPE) is used to realize the parameter optimization of the RF model, and the TPE-RF diagnostic model is obtained. Combined with the various evaluated indicators, the proposed method is proved to be able to diagnosis the transformer faults accurately. Finally, the TreeSHAP model is introduced to analyze the importance of the features corresponding to each fault, and the specialized features for each fault are selected. According to the case of transformer in operation, the applicability of the method in the power system is discussed, and the effectiveness of the method is verified.

A high voltage current transformer of the inverted type is a commonly used measurement equipment in power grids. The thermal behaviour of the current transformer has a significant impact on its operating conditions and is directly related to its service life. The study presents a three‐dimensional simulation model of a 220 kV inverted‐type oil‐immersed current transformer, and the fluid and temperature field are analysed by the finite volume method. The simulation results show that the hot‐spot temperature under the rated load of the calculation model introduced in the study is 61.5°C, and located at the upper part of the core and the secondary winding. An experiment setup was designed to measure the temperature distribution inside a LVB‐220W3‐type current transformer. The temperature data measured under the rated load indicate that the simulation results agree well with the experimental results. [ABSTRACT FROM AUTHOR]

Efficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids. [ABSTRACT FROM AUTHOR]

This paper focuses on the security condition of line worker during the implementation of helicopter live-line work by platform method on 1000 kV ultrahigh voltage (UHV) transmission lines. First, a model of helicopter live-line work is presented, and the electric field distribution of line worker when performing live-line work on 1000 kV UHV transmission lines is analyzed. Then, the capacitances between helicopter live-line work platform, transmission lines, and ground are obtained by energy method, and an equivalent circuit model is presented to analyze the current characteristic of bonding procedure. At the end of this paper, an experiment of helicopter live-line work is performed, the electric field distributions of line worker and the discharge current are measured and compared with simulation results, which will be used to judge the security condition of line worker and the feasibility of helicopter live-line work by platform method on 1000 kV UHV transmission lines. [ABSTRACT FROM PUBLISHER]

Performance analysis of a coil launcher is very important for experimental research and electromagnetic optimization design. To check the effect of the improvement and analyze electromagnetic transient in a coil launcher, a field coupling circuit simulation method is introduced in this paper. Initially, the current of drive coils is calculated by the circuit model based on improved current filament method, and then the exciting current is loaded in 2-D axisymmetric field model. The field model is built based on nonoverlapping mortar finite-element method (NO-MFEM). NO-MFEM divides the whole domain into two subdomains: one contains the movable part (armature), and the other contains the source current (coils). The two subdomains are discretized independently and the two sets of meshes are nonconforming on the interface. When the movable part changes its location, it is necessary only to change the node coordinates in movable subdomains and information of mortar nodes and elements. In this paper, performance analysis of a three-stage coaxial induction coil launcher is carried out based on the proposed field-circuit method. The correspondence of the simulation results proves the validity of the field-circuit method. [ABSTRACT FROM PUBLISHER]