Insulators are essential components in power transmission lines. Due to the harsh variations in bad environments, insulators may experience faults. Detecting these insulator faults promptly and effectively is an urgent issue. To rapidly and accurately locate insulators and their faulty regions in aerial images of insulators with complex backgrounds and varying fault sizes, this paper proposes an improved YOLOv8 algorithm for the detection of multiple insulator fault types (MFI-YOLO). This algorithm achieved target feature extraction in complex background images by replacing the C2F network constructed by fusing the GhostNet and multi-scale asymmetric convolution (MSA-GhostBlock). Furthermore, in the feature fusion stage, a multi-scale feature fusion structure called ResPANet, based on residual skip connections, was constructed to replace the PANet. This enhancement aims to improve the network detection accuracy in multi-target scenarios. Finally, to evaluate the algorithm's performance, this study constructed a target detection dataset containing four types of insulators: normal, self-explosive, damaged, and flashover. Experimental results indicate that, compared to the original model, the improved model has increased mean accuracy from 89.2% to 93.9%. The designed model exhibits high detection accuracy in the insulator and its three fault categories, especially for some hard-to-detect fitting.

Aerial images have complex backgrounds, small targets, and overlapping targets, resulting in low accuracy of intelligent detection of overhead line insulators. This paper proposes an improved algorithm for insulator breakage detection based on YOLOv5: The ECA-Net (Efficient Channel Attention Network) attention mechanism is integrated into its backbone feature extraction layer, and the effective distinction between background and target is achieved by increasing the weight of important channels. A bidirectional feature pyramid network is added to the feature fusion layer, and large-scale images with more original information are combined to effectively retain small target features. Incorporating a flexible detection frame selection algorithm Soft-NMS (Soft Non-Maximum Suppression) into the prediction layer to re-screen the target frame, thereby reducing the probability of mistaken deletion of overlapping targets. The effectiveness of the improved YOLOv5 algorithm is verified in the actual aerial image dataset, and the results show that the mean Average Precision (mAP) of the improved algorithm is 95.02% and the detection speed FPS (Frames Per Second) can reach 49.4 frames/s, which meets the real-time and accuracy requirements of engineering applications.

Aerial insulator defect images have some features. For instance, the complex background and small target of defects would make it difficult to detect insulator defects quickly and accurately. To solve the problem of low accuracy of insulator defect detection, this paper concerns the shortcomings of IoU and the sensitivity of small targets to the model regression accuracy. An improved SIoU loss function was proposed based on the regular influence of regression direction on the accuracy. This loss function can accelerate the convergence of the model and make it achieve better results in regressions. For complex backgrounds, ECA (Efficient Channel Attention Module) is embedded between the backbone and the feature fusion layer of the model to reduce the influence of redundant features on the detection accuracy and make progress in the aspect. As a result, these experiments show that the improved model achieved 97.18% mAP which is 2.74% higher than before, and the detection speed could reach 71 fps. To some extent, it can detect insulator and its defects accurately and in real-time.

Unbalanced dc links usually occur when a Vienna rectifier is used as a charger for series-connected battery packs. The redundant vector preselection optimal switching sequence model predictive control (OSS-MPC) scheme does not require weighting factor tuning and is applicable to unbalanced dc links. However, its use is complicated for switching sequence selections and duty cycle calculations. Therefore, this article proposes a computationally efficient OSS-MPC scheme for three-phase Vienna rectifier operating under balanced and unbalanced dc links. The scheme achieves the same performance as that achieved by the redundant vector preselection OSS-MPC scheme, however, its execution is significantly more efficient. In the proposed method, an unbalanced factor is proposed by considering unbalanced dc links, and a look-up table is derived to describe the equivalence relation among the six feasible switching sequences. Then, duty cycles are calculated for only one switching sequence, and the OSS and its duty cycles are reconstructed based on the predefined table. Unlike the redundant vector preselection OSS-MPC scheme, the enumeration process is eliminated in the proposed method, thus, the computational burden is significantly reduced. Experimental results are provided to validate the effectiveness of the proposed method. This article is accompanied by a PDF demonstrating the look-up tables.

A d-q frame impedance model of modular multilevel converter (MMC), which is suitable for high-frequency resonance (HFR) analysis under the condition that the number of submodules in MMC reaches hundreds level, is presented in this work. A 10th-order dynamic phasor model for the MMC is linearized and combined with a control subsystem model. A modular modeling approach based on transfer matrix is applied to the overall linearized model to derive the impedance model. The validity and accuracy of the model are verified using a 501-level MMC aggregated model in SIMULINK. Results show that the impedance model has satisfactory accuracy in the frequency ranging from 1 to 3000 Hz. Then, the impedance model is used to analyze an HFR accident in a realistic MMC project. Therefore, a high time delay in controller is proved can provide negative resistance in high frequency anc cause instability.