Improving detection accuracy is a challenging task when detecting defects in PCBs. To conquer the issue of degradation of recognition performance of existing intelligent detection algorithms in different environments, we present an improved algorithm, IEMA-YOLOv8, based on YOLOv8. First, we design a novel Efficient Multi-Scale Attention (EMA) combined with the Inverted Residual Mobile Block (IRMB) to form a new attention mechanism called IEMA. The IEMA module is subsequently incorporated into the C2f module to boost the model’s overall performance. Secondly, the Spatial Pyramid Pooling Enhanced with ELAN (SPPELAN) module is employed to improve the original Spatial Pyramid Pooling Fast (SPPF) module, thereby bolstering the model’s capacity to recognize defective regions. Finally, the More Focused Intersection over Union (Focaler-IoU) loss function replaces the original Complete Intersection over Union (CIoU) loss function, aiming to compensate for the limitations of the current bounding box regression methodology, thus further enhancing the efficacy of detection in the task. The experimental results show that our proposed IEMA-YOLOv8 algorithm has precision, recall, mAP50, and mAP50:95 values of 88.8%, 96.8%, 94.6%, and 51%, respectively, provided that the model complexity is kept basically the same and the Frames Per Second (FPS) value reaches 116.2. These values are 1.1%, 3.6%, 4.9%, and 6% higher than the originalYOLOv8n algorithm. Compared with theYOLO family of one-stage detection models, our proposed algorithm has significant advantages in defect detection.

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM.

With the large-scale integration of wind power into the grid in recent years, the power quality pollution in power systems has been deteriorating increasingly. The existing assessment methods are hardly applicable to wind power grid-connected systems. Therefore, to enhance the power supply quality of the grid, this paper proposes a power quality assessment method based on game theory combination empowerment. According to the characteristics of wind power generation and power quality issues, this paper establishes a set of evaluation index systems applicable to wind power grid-connected systems. Then, the G1 method and the improved CRITIC method are used to obtain the subjective and objective weights of each evaluation index. The idea of game theory is introduced to linearly combine the two, thereby obtaining the final weights of the power quality indexes. Subsequently, the TOPSIS method is employed to solve the problem, and the grey system theory is introduced to avoid the situation where it is impossible to distinguish the superiority and inferiority of samples when the closeness is equal, making the assessment results more reasonable. The results show that the method is reasonable and effective and can be applied well in the field of power quality assessment for wind power grid-connected systems, which has certain reference and research significance.

With the development of the semiconductor manufacturing process towards miniaturization and high integration, the detection of microscopic defects on wafer surfaces faces the challenge of balancing precision and efficiency. Therefore, this study proposes a lightweight inspection model based on the YOLOv8 framework, aiming to achieve an optimal balance between inspection accuracy, model complexity, and inference speed. First, we design a novel lightweight module called IRB-GhostConv-C2f (IGC) to replace the C2f module in the backbone, thereby significantly minimizing redundant feature computations. Second, a CNN-based cross-scale feature fusion neck network, the CCFF-ISC neck, is proposed to reduce the redundant computation of low-level features and enhance the expression of multi-scale semantic information. Meanwhile, the novel IRB-SCSA-C2f (ISC) module replaces the C2f in the neck to further improve the efficiency of feature fusion. In addition, a novel dynamic head network, DyHeadv3, is integrated into the head structure, aiming to improve the small-scale target detection performance by dynamically adjusting the feature interaction mechanism. Finally, so as to comprehensively assess the proposed algorithm’s performance, an industrial dataset of wafer defects, WSDD, is constructed, which covers “broken edges”, “scratches”, “oil pollution”, and “minor defects”. The experimental results demonstrate that the CISC-YOLO model attains an mAP50 of 93.7%, and the parameter amount is reduced to 1.92 M, outperforming other mainstream leading algorithms in the field. The proposed approach provides a high-precision and low-latency real-time defect detection solution for semiconductor industry scenarios.

To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line–household relationship identification (LHRI), a method for identifying line–household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption characteristic vector of user categories, thereby revealing the line–household relationship for all users. On the test dataset, the LHRI algorithm proposed in this article achieved 100% accuracy, within an allowable error range of 0.2%, and improved the accuracy by 20% compared to the traditional identification method. Finally, the LVDN simulation model established by OpenDSS 9.4.0.3 was used to verify the effectiveness of the proposed method, confirming its potential and advantages in practical applications.

This paper presents a magnetically controlled series compensation-fault current limiter (MCSC-FCL) which is composed of a magnetically controlled reactor (MCR) and a compensation capacitor. The device is improved from the traditional thyristor-controlled series compensation (TCSC), which has the characteristics of small output harmonics and simple control. The traditional saturated iron-core reactor is improved to increase the adjustable range of output reactance. During the fault, the MCR is connected to the grid alone to limit fault current; when the grid is normal, MCR and capacitor are used in series compensation mode. It effectively improves the utilization rate of the traditional current limiter MCR. In order to verify the effectiveness of the proposed MCSC-FCL, this paper analyzes the control characteristics of reactor models with different air gap structures by JMAG finite element analysis software, and verifies the voltage regulation characteristics and fault current limitation capabilities on MATLAB/Simulink platform.