Traditional disease and pest detection technology employ cloud-based deep learning, which facing the pressures, such as high data storage and communication costs, unbalanced and insufficient data from orchards, diversity of pests and diseases, and complex detection environments. In this article, we propose a multiple pest detection technique based on federated learning (FL) and improved faster region convolutional neural network (R-CNN). As the new distributed computing model, FL can derive a shared model integrating the advantages of data from all parties without uploading local data and also reduces the communication cost. A restriction $M$ is added to the FL algorithm to ensure the convergence of the model and improve the training speed. According to the original faster R-CNN network, ResNet-101 is used instead of VGG-16 to construct the base convolutional layer to maintain the original structure of small-sized targets and improve the detection speed. Then, the multisize fusion of feature maps from different convolutional layers is performed to improve the detection accuracy of multisize multiple pests and diseases. Finally, a soft-nonmaximum suppression (NMS) algorithm is proposed to solve the apple obscured problem after the region proposal network (RPN). The experimental results show that the improved faster R-CNN can achieve an average accuracy of 90.27% on multiple pest detection, and the detection time is only 0.05 s per image. After using FL, the mean average precision (mAP) of the model reached 89.34% and the model training speed was improved by 59%.

The existing transmission line detection technologies based on cloud computing are faced with the problems such as low response speed, high communication cost, and the difficulty to obtain scattered data. This paper proposes a transmission line defect detection method based on adaptive Federated Learning (FL), which can adaptively change the data volume of the next training round according to the training energy consumption of each round. As a result, the proposed method can achieve the optimal number of communication between local training cost and global communication efficiency, which greatly improves the training speed and reduces the communication cost. Furthermore, a lightweight Mask Region-Convolutional Neural Networks (R-CNN) that adopts the MobileNet architecture to replace the original network is designed for the edge, which reduces the required number of parameters and improves the detection speed. The experimental results show that the model training efficiency of the proposed adaptive FL is 70% higher than the centralized cloud computing, and the computing cost is saved by about 40%. Compared with the original Mask R-CNN, the proposed lightweight Mask R-CNN model can achieve 1.1% higher precision, 10% shorter training time, and 40% faster detection speed.

Aiming at the difficulty in detecting defects of key equipment of transmission lines in small samples and complex environments, and the problems of low accuracy and unreliability in one-time detection using traditional deep learning-based methods, an image detection scheme combining optimized deep convolutional neural networks and Kalman filtering is proposed. The convolutional neural network architecture is based on Faster Region-based Convolutional Neural Networks (R-CNNs). First, the model backbone network is constructed by MobileNet, which effectively reduces the computational cost. Secondly, a soft nonmaximum suppression algorithm is integrated to solve the occlusion problem of target parts, and the context-aware ROI pooling layer replaces the original pooling layer, maintaining the original structure of small-sized components. Finally, the detection results are corrected twice by Kalman filtering to further improve the detection accuracy and reliability. The experimental results show that this method can realize the accurate detection of components in complex transmission line equipment, the mean Average Precision (mAP) reaches 91.10%, which is 11.05% higher than the original model, and the detection time of each picture is only 0.05 s. Compared with other detection algorithms under the same conditions, the comprehensive performance of the proposed method can be improved by 20%.

The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics.

This paper proposes a novel metal oxide arrester (MOA) fault detection technology based on a small sample infrared image. The research is carried out from the detection process and data enhancement. A lightweight MOA identification and location algorithm is designed at the edge, which can not only reduce the amount of data uploaded, but also reduce the search space of cloud algorithm. In order to improve the accuracy and generalization ability of the defect detection model under the condition of small samples, a multi-model fusion detection algorithm is proposed. Different features of the image are extracted by multiple convolutional neural networks, and then multiple classifiers are trained. Finally, the weighted voting strategy is used for fault diagnosis. In addition, the extended model of fault samples is constructed by transfer learning and deep convolutional generative adversarial networks (DCGAN) to solve the problem of unbalanced training data sets. The experimental results show that the proposed method can realize the accurate location of arrester under the condition of small samples, and after the data expansion, the recognition rate of arrester anomalies can be improved from 83% to 85%, showing high effectiveness and reliability.

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems.