In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm.

Composite insulators are important components of power transmission lines and their quantity is huge, and their overheating defects can cause serious power accidents, so it is crucial to detect the overheating defects of composite insulators on a regular basis. Current detection methods suffer problems such as low detection accuracy and efficiency. To solve these problems, we propose a method for detecting overheating defects of composite insulators based on infrared images and computer vision. The system is divided into two parts: composite insulator detection and key point extraction. During the experiment, we found that there are problems of sample imbalance and inaccurate positioning of prediction box, so we proposed Equalized Fully Convolutional One-Stage object detection (FCOS), which adds the sample equalization strategy and multi-dimensional dynamic attention mechanism on the basis of FCOS, and improves the Centerness and proposes the center positioning confidence. The results show that the performance of Equalized FCOS is significantly improved compared with FCOS, with mAP, AP50, AP75, APm, and APl improved by 7.6%, 5.1%, 12.1%, 4.6%, and 9.7% respectively. Finally, considering the practical application requirements, we extracted the key points of the mandrels and performed temperature analysis. We have realized the automated inspection of composite insulators.

Ultra-high Voltage Direct Current system (UHVDC) can realize long distance transmission, and its loss measurement has great significance for loss reduction and fault diagnosis. Currently, loss measurements can be performed by model-driven and data-driven approaches. However, poor operating conditions can lead to system sensing errors, which can significantly reduce the accuracy and speed of measurement. To deal with this problem, we propose a digital twin architecture and constructs a data-driven learning model. The model, DTformer, is embedded in the digital twin architecture. And it has three remarkable processes: encoder-decoder based random feature extraction, loss measurement transfer learning and knowledge distillation. These processes extract parameter information and topology features from the data of UHVDC. The model has good accuracy, response time and resistance to the missing data. The comparison experiment shows that DTformer outperforms popular feature extraction models with an average decrease of 38.80% in RMSE and 42.82% in MAE, and the best R2 value is closest to 1. The analyses of lightweight and missingness show that the DTformer also has significant response time improvement and missing resistance. Finally, we also implement the visualization of digital twin to enable operators to perform monitoring, computation, analysis and management of UHVDC.

To ensure effective heat recovery of thermoelectric generators, a cooling system is necessary to maintain the working temperature difference of the thermoelectric couples, which decreases continuously due to thermal diffusion. In order to evaluate and improve the thermoelectric performance of a concentric annular thermoelectric generator under various cooling methods, a comprehensive numerical model of the thermo-fluid-electric multi-physics field for an annular thermoelectric generator with a concentric annular heat exchanger was developed using the finite-element method. The effects of four cooling methods and different exhaust parameters on the thermoelectric performance were investigated. The results show that, in comparison to the cocurrent cooling pattern, the countercurrent cooling pattern effectively reduces temperature distribution non-uniformity and hence increases the maximum output power; however, it requires more thermoelectric semiconductor materials. Furthermore, when using the cocurrent air-cooling method, high exhaust temperatures may result in lower output power; high exhaust mass flow rates result in high exhaust resistance and reduce system net power. The maximum net power output P_net = 432.42 W was obtained using the countercurrent water-cooling, corresponding to an optimal thermoelectric semiconductor volume of 9.06 × 10⁻⁴ m³; when compared to cocurrent water-cooling, the maximum net power increased by 8.9%, but the optimal thermoelectric semiconductor volume increased by 21.4%.