Reduced RLC Impedance Circuit Model of Electric Vehicle DC Charging Station

[Introduction] The potential positive feedback of virtual inertia and damping control (VIDC) may exacerbate the interaction within the control loop, causing low-frequency oscillation of bus voltage and disrupting the stability of electric vehicle DC charging station (EVCS). Although the existing multi-timescale impedance model explains the stability mechanism of VIDC and the low-frequency oscillation mechanism of VIDC-controlled-EVCS, it is essentially a high-order transfer function, making it difficult to obtain analytical expressions for damping control strategies. [Method] Therefore, a detailed impedance analysis of the virtual impedance of the loop was conducted to intuitively reveal the fundamental reasons for the higher-order properties of each loop impedance. [Result] A closed-loop gain fitting method for the control loop was proposed through Bode diagram approximation, and a low-order impedance circuit model was established. [Conclusion] The effectiveness of the proposed multi-timescale impedance model is verified through Matlab/Simulation

Multifunctional Voltage Source Inverter for Renewable Energy Integration and Power Quality Conditioning

In order to utilize the energy from the renewable energy sources, power conversion system is necessary, in which the voltage source inverter (VSI) is usually the last stage for injecting power to the grid. It is an economical solution to add the function of power quality conditioning to the grid-connected VSI in the low-voltage distribution system. Two multifunctional VSIs are studied in this paper, that is, inductive-coupling VSI and capacitive-coupling VSI, which are named after the fundamental frequency impedance of their coupling branch. The operation voltages of the two VSIs are compared when they are used for renewable energy integration and power quality conditioning simultaneously. The operation voltage of the capacitive-coupling VSI can be set much lower than that of the inductive-coupling VSI when reactive power is for compensating inductive loads. Since a large portion of the loads in the distribution system are inductive, the capacitive-coupling VSI is further studied. The design and control method of the multifunctional capacitive-coupling VSI are proposed in this paper. Simulation and experimental results are provided to show its validity

Tertiary Regulation of Cascaded Run-of-the-River Hydropower in the Islanded Renewable Power System Considering Multi-Timescale Dynamics

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run-of-the-river hydropower and volatile energies such as pv and wind are built around the world. In islanded operation mode, the primary and secondary frequency control, i.e., hydro governors and automatic generation control (AGC), ensure the frequency stability. However, due to limited water storage capacity of run-of-the-river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC with fixed participation factors cannot fully exploit the adjustability of cascaded hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. The timescales of the power system and river dynamics are very different. To unify the multi-timescale dynamics to establish a model predictive controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge over a time interval is approximated by a data-based second-order polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding-horizon manner, and load shedding is minimised. Simulation of a real-life system shows a significant improvement in the proposed method in terms of reducing load shedding.Comment: Submitted to IET Renewable Power Generation; 11 page

Rising stars in energy research: 2022

Recognising the future leaders of Energy Research is fundamental to safeguarding tomorrow's driving force in innovation. This collection will showcase the high-quality work of internationally recognized researchers in the early stages of their careers. We aim to highlight research by leading scientists of the future across the entire breadth of Energy Research, and present advances in theory, experiment and methodology with applications to compelling problems

Attenuation of Vaccinia Tian Tan Strain by Removal of Viral TC7L-TK2L and TA35R Genes

Vaccinia Tian Tan (VTT) was attenuated by deletion of the TC7L-TK2L and TA35R genes to generate MVTT3. The mutant was generated by replacing the open reading frames by a gene encoding enhanced green fluorescent protein (EGFP) flanked by loxP sites. Viruses expressing EGFP were then screened for and purified by serial plaque formation. In a second step the marker EGFP gene was removed by transfecting cells with a plasmid encoding cre recombinase and selecting for viruses that had lost the EGFP phenotype. The MVTT3 mutant was shown to be avirulent and immunogenic. These results support the conclusion that TC7L-TK2L and TA35R deletion mutants can be used as safe viral vectors or as platform for vaccines

DSDCLNet: Dual-Stream Encoder and Dual-Level Contrastive Learning Network for supervised multivariate time series classification

In recent years, deep learning approaches have shown remarkable advancements in multivariate time series classification (MTSC) tasks. However, the existing approaches primarily focus on capturing the long-term correlations of time series or identifying local key sequence fragments, inevitably neglecting the synergistic properties between global and local components. Additionally, most representation learning methods for MTSC rely on self-supervised learning, which limits their ability to fully exploit label information. Hence, this paper proposes a novel approach termed Dual-Stream Encoder and Dual-Level Contrastive Learning Network (DSDCLNet), which integrates a dual-stream encoder (DSE) and dual-level contrastive learning (DCL). First, to extract multiscale local-global features from multivariate time series data, we employ a DSE architecture comprising an attention-gated recurrent unit (AGRU) and a dual-layer multiscale convolutional neural network (DMSCNN). Specifically, DMSCNN consists of a series of multi-scale convolutional layers and a max pooling layer. Second, to maximize the utilization of label information, a new loss function is designed, which combines classification loss, instance-level contrastive loss, and temporal-level contrastive loss. Finally, experiments are conducted on the UEA datasets and the results demonstrate that DSDCLNet achieves the highest average accuracy of 0.77, outperforming traditional approaches, deep learning approaches, and self-supervised approaches on 30, 23, and 27 datasets, respectively