Ensuring the reliability and safety of electrical power systems requires the efficient detection of defects in high-voltage transmission line insulators, which play a critical role in electrical isolation and mechanical support. Environmental factors often lead to insulator defects, highlighting the need for accurate detection methods. This paper proposes an enhanced defect detection approach based on a lightweight neural network derived from the YOLOv11n architecture. Key innovations include a redesigned C3k2 module that incorporates multidimensional dynamic convolutions (ODConv) for improved feature extraction, the introduction of Slimneck to reduce model complexity and computational cost, and the application of the WIoU loss function to optimize anchor box handling and to accelerate convergence. Experimental results demonstrate that the proposed method outperforms existing models like YOLOv8 and YOLOv10 in precision, recall, and mean average precision (mAP), while maintaining low computational complexity. This approach provides a promising solution for real-time, high-accuracy insulator defect detection, enhancing the safety and reliability of power transmission systems.

In recent years, flexible thermoelectric generators(f-TEG), which can generate electricity by environmental temperature difference and have low cost, have been widely concerned in self-powered energy devices for underground pipe network monitoring. This paper studied the Cu₂S films by screen-printing. The effects of different proportions of p-type Cu₂S/poly 3,4-ethylene dioxythiophene-polystyrene sulfonate (PEDOT:PSS) mixture on the thermoelectric properties of films were studied. The interfacial effect of the two materials, forming a superconducting layer on the surface of Cu₂S, leads to the enhancement of film conductivity with the increase of PEDOT:PSS. In addition, the Seebeck coefficient decreases with the increase of PEDOT:PSS due to the excessive bandgap difference between the two materials. When the content ratio of Cu₂S and PEDOT:PSS was 1:1.2, the prepared film had the optimal thermoelectric performance, with a maximum power factor (PF) of 20.60 μW·m⁻¹·K⁻¹. The conductivity reached 75% of the initial value after 1500 bending tests. In addition, a fully printed Te-free f-TEG with a fan-shaped structure by Cu₂S and Ag₂Se was constructed. When the temperature difference (ΔT) was 35 K, the output voltage of the f-TEG was 33.50 mV, and the maximum power was 163.20 nW. Thus, it is envisaged that large thermoelectric output can be obtained by building a multi-layer stacking f-TEG for continuous self-powered monitoring.

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models.

To suppress the bus voltage fluctuations caused by disturbances such as photovoltaic-side power fluctuations and load-side power variations in hybrid energy storage systems, a fixed-time active disturbance rejection controller (FxADRC) integrated with a cascaded extended state observer (CESO) is proposed in this paper. The paper first constructs a dynamic model of the hybrid energy storage system, considering the impact of disturbances. Second, this paper designs a cascaded extended state observer to estimate the total disturbances affecting the system, achieving finite-time convergence performance. Then, a fixed-time sliding mode active disturbance rejection control strategy combined with the CESO is proposed, which can achieve bus voltage tracking with fixed-time convergence rate irrespective of the effect of the initial state. In addition, the stability of the proposed controller is validated by the Lyapunov method. Finally, Comparative studies are conducted by simulation to verify the effectiveness and outstanding performance of the proposed control scheme.

Rapid changes in meteorological conditions can lead to frequent switching of wind turbine operating states, causing wind power sequences to exhibit pronounced non-stationarity and multimodal characteristics. As a result, conventional single prediction models often struggle to simultaneously maintain forecasting accuracy and stability under different operating conditions. To address this issue, this paper proposes a wind power forecasting method based on the Convolutional Normalized Transformer Encoder and Multi-Task Learning (CNTE-MTL). First, operating samples of wind turbines are divided into different operating conditions according to typical meteorological variables, such as wind speed, wind direction, and ambient temperature, to characterize differences in meteorology-driven operating patterns. Then, wind power forecasting under different meteorological conditions is formulated as multiple related subtasks, and a multi-task learning framework consisting of a shared feature extraction network and condition-specific prediction heads is constructed. In this framework, the shared feature extraction network employs one-dimensional convolution to extract local temporal fluctuation information and combines it with a Transformer encoder to capture long-term dependency features. The condition-specific prediction heads further characterize the differentiated power evolution patterns under different meteorological conditions, thereby enabling the sharing of common cross-condition information and differentiated modeling. Short-term forecasting, long-term forecasting, supplementary comparative experiments, and ablation experiments are conducted based on SCADA data from an actual wind farm. The results show that the proposed CNTE-MTL model achieves an RMSE of 0.0165 and an $R^2$ of 0.9689 in the one-month short-term forecasting experiment, and an RMSE of 0.0072 and an $R^2$ of 0.9980 in the three-month long-term forecasting experiment, outperforming comparative models such as CNTE, Informer, Transformer, TCN, and LSTM. The ablation experiments further verify the effectiveness of meteorology-driven operating condition division, the shared feature extraction network, and the condition-specific prediction heads in improving forecasting performance.

UAV-based insulator defect detection faces persistent challenges of multi-scale defect variation, background clutter, small-target missed detections and redundant detection-head computation. This paper proposes BEAD-Net, a real-time insulator defect detection network built upon YOLOv11n with four targeted improvements. First, an Hourglass Symmetric Residual Attention (HSRA) module replaces the standard bottleneck components within C3k2, expanding the multi-scale receptive field and suppressing background interference via a symmetric hourglass dilation schedule and channel attention recalibration. Second, a Bidirectional Diffusion Feature Pyramid Network (BDFPN) built upon the Group-wise Selective Feature Integrator (GSFI) employs group-wise adaptive gating and two-level bidirectional propagation to mitigate semantic dilution during cross-scale fusion. Third, a Task-Decoupled Asymmetric Detection Head (TDAH) concentrates spatial modeling in the regression branch while simplifying classification to lightweight $1\times 1$ operations, reducing parameter redundancy and alleviating inter-task gradient conflict. Finally, Focaler-PIoU2 integrates linear interval mapping with a normalized corner distance penalty to improve boundary regression for slender insulator structures. On the IDID dataset, BEAD-Net achieves mAP@0.5 of 83.98% and mAP@0.5:0.95 of 63.82% at 275.04 FPS with 2.49 M parameters, outperforming the baseline YOLOv11n by 2.83 and 1.55 percentage points and surpassing all compared state-of-the-art methods. Additional validation on the CPLID benchmark shows that BEAD-Net remains the top performer, confirming that the proposed improvements are not specific to a single dataset.