The working condition of opening damper directly affects the opening mechanical characteristics and component lifetime of high-voltage circuit breaker (HVCB). Vibration diagnostic technique is a noninvasive and efficient way for on-line evaluation of the opening damper. Vibration time-frequency image which constructed by using the method of time-frequency analysis (TFA) contains rich feature information of opening damper’s working condition. In this paper, a condition evaluation method for opening damper of spring operated HVCB based on vibration time-frequency image and convolutional neural network (CNN) is proposed and analyzed. The one-dimensional (1D) vibration signal of HVCB is transformed into three-dimensional (3D) time-frequency image by utilizing Hilbert-Huang transform (HHT). The 3D time-frequency image directly served as a training sample for the CNN, which avoids the tedious and invalid work of manual feature extraction or selection and can achieves accurate condition evaluation of HVCB’s opening damper. It can be seen that the noninvasive vibration on-line monitoring and the evaluation algorithm for the opening damper of HVCB is feasible and effective. Test results carried out on several 12 kV vacuum HVCBs substantiate the proposed approach.

Vibration signals generated during circuit breaker (CB) closing or opening operations contain rich information of operating mechanism (OM), transmission mechanism and interrupter(s). Effective feature extraction method can mine the information as a criterion for diagnosis and maintenance. However, due to the very complicated mechanical system and extremely short operation time of CB, the vibration signal is highly nonlinear and non-stationary, which makes it very difficult to precisely extract effective features for machinery fault diagnosis. Chaotic nonlinear dynamic technique provides a new way to study the complex vibration signals. In this paper, phase space reconstruction is utilized to study the effect of faults on the chaotic attractor (the trajectories in multidimensional phase space) behavior. The power spectrum and Lyapunov exponent proof the existence of chaos in the CB's vibration signals. The invariant measures and ergodic quantities such as the largest Lyapunov exponent (LLE), correlation dimension (CD) and Kolmogorov entropy (KE) which can be estimated on the reconstructed attractor are presented as a set of new features for fault diagnosis. The estimation of these features was tested on the experimental data sets recorded from a 12-kV, 1250-A vacuum CB and a 252-kV, 4000-A SF6 CB, respectively.

This paper presents a fluid-thermal coupled analysis method to compute the temperature distribution in a 31.5 MVA/110 kV oil natural air natural (ONAN) three-phase three-limb transformer. The power losses of windings and core are measured by load-loss test and no-load test respectively. The convective heat transfer process, radiation and oil flow inside the transformer are investigated by finite volume method (FVM). In order to validate the feasibility and accuracy of the presented method, the temperature measuring system based on fiber Brag grating (FBG) sensor is constructed for the temperature rise test of the 31.5 MVA/110 kV ONAN transformer. The simulation results deduced from the proposed method agree well with experimental data. This model can be applied to optimizing design and load scheduling.

Crustal movement and land subsidence will cause tower foundation settlement, which seriously affects the safety of transmission line operation. This paper proposed a short-term failure warning method for transmission tower under land subsidence condition based on finite element method, strain monitoring and time series prediction. By simulating the stress distribution of the tower in actual foundation settlement, the weak components of the settlement tower were founded. Combined with the finite element simulation results in various conditions, the reliability of simulation results is verified, and the strain variation range of the corresponding measuring point is obtained. By installing the real-time strain monitoring system and establishing the autoregressive integrated moving average (ARIMA) time series forecast model, warning of the failure of the tower can be sounded in short time according to the strain monitoring and forecasting results. The field results show that the tower is in good condition and with no risk in short term. The method proposed by this paper is economical and easy to operate, which can be used to conduct the emergency treatment of the dangerous tower under land subsidence condition.

To improve the jamming cognitive level of radar in the complex and changeable electromagnetic environment, this paper proposes a YOLO-CJ (YOLO network for compound jamming detection) lightweight network for compound jamming signal detection. Firstly, the compound jamming image dataset is constructed by signal 0000-0000 © 2022 IEEE model and short-time Fourier transform (STFT). Secondly, based on the YOLOv7-tiny lightweight baseline model, the designed SimSPPFCSPC module, Triplet attention mechanism, and the C2f module are illustrated to improve the YOLOv7-tiny and establish the YOLO-CJ lightweight network, which aims to enhance the saliency and difference of the extracted features in the case of low SNR/JNR while effectively control the quantity of computation and parameters. Subsequently, combined with the compound jamming image dataset, the ablation experiment, algorithm comparison experiment, and robustness verification are carried out. The results demonstrate that the detection precision (mAP) and the inference time of a single image of the YOLO-CJ model can reach 98.69% and 12.73 ms, and the trade-off value of detection precision and speed can reach the highest 79.93%. Moreover, the mAP of the YOLO-CJ for compound jamming detection under SNR / JNR ≥ 0dB can reach more than 96%, which has good robustness and generalization ability under low SNR and JNR. Source code is released at https://github.com/zhuxuan-96/YOLO-CJ.