Jie Yu

To address the inefficiency and inaccuracy of traditional offline sampling calibration and online calibration technologies for large-scale verification of electric vehicle off-board chargers (EVObCs), this paper proposes a remote verification method for EVObC measurement errors based on the charging interactions between electric vehicles (EVs) and EVObCs. Firstly, by leveraging the principles of infectious diseases transmission mode, a progressive metrological verification strategy for massive EVObCs is introduced. Additionally, a method for calculating benchmark accumulated charging energy, based on the temporal convolutional neural network-attention mechanism (TCN-Attention), is proposed to enhance the accuracy of EVObC measurement error identification. Furthermore, based on the energy decay model, a method for evaluating the intensity degradation of EVObC metrological performance is presented to objectively reflect the degradation of EVObC metrological accuracy and the dynamic variability of metering states in the proposed model. Finally, an experiment based on real data from 192 off-board chargers distributed over an area of 40 square kilometers was conducted. The results demonstrate that the proposed method can accurately identify standard non-compliant metering states of EVObCs, demonstrating its speed, accuracy, and strong generalization capabilities for large-scale verification and error identification across different types of EVObCs.

Geographically distributed data centers (DCs) have emerged as significant energy consumers, which has led to the integration of renewable energy sources (RES) into DC power provisioning systems. However, the intermittent nature of RES and the randomness of user requests can cause significant fluctuations in DC operating power. It can be detrimental to the operation of IT equipment and lead to instability in the power grid. In this paper, aiming for tightly coupled interconnection scenarios with multi-data centers in varying regions, a multi-data center tie-line power smoothing method based on demand response is proposed. By modulating the power load of server clusters with workload scheduling, we establish a control model combined with intra-DC temporal task migration and inter-DC spatial task migration to deal with high-frequency power fluctuations. The uninterruptible power supply (UPS) battery control model is established to tackle low-frequency fluctuations. Furthermore, we design the two-stage heuristic power regulation algorithm to achieve the best practice of smoothing effect by real-time tracking of power targets after two-layer filtering. Finally, this paper performs a detailed performance simulation evaluation based on tracking data from a real DC and wind and photovoltaic (PV) new energy generation data, using four interconnected DC parks of different sizes across different regions as examples. The simulation results demonstrate that the proposed method effectively smoothing the multi-data center's tie-line power. Additionally, inter-DC temporal task migration serves as a viable solution to overcome the limitations of task migration response within a single DC, reducing the frequency of UPS battery bank charges and discharges, which in turn prolongs their service life. This approach facilitates the utilization of RES while maintaining power quality, and it also aids in reducing the escalating operation and maintenance expenses of DCs.

Carbon fiber reinforced polymers (CFRPs) have been widely applied in the aerospace industry, and the health conditions of CFRPs largely affect aerospace safety. Due to the limitations of traditional detection methods, electrical impedance tomography (EIT) has been gradually applied in the damage detection of CFRP composite materials. Aiming at the problems of poor imaging quality and low identification rate in the traditional EIT reconstruction algorithm, an EIT algorithm based on the one-dimensional multi-scale residual convolution neural network (1D-MSK-ResNet) is proposed in this paper. A “voltage vector-conductivity media distribution” dataset is first established, and the training results of the testing dataset are used to verify and evaluate the algorithm. Simulation and experimental results indicated that the 1D-MSK-ResNet EIT algorithm could enhance the ability of damage identification and significantly improve the imaging quality.