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Along with the operating velocity and traction power increasing continuously, the issue related to the safety and reliability of the high-voltage traction power system for high-speed trains have gradually been exposed during the practical service process. When the train passes a neutral section settled roughly per 15 km between two neighboring substations, the on-board vacuum circuit breaker (VCB) must to be operated for changing the input powers with different phases. At the moment of switching on or off the VCB, the arc between the contacts in the arc-extinguishing chamber may be triggered, which tends to result in the operational overvoltage. Due to numerous inductances and capacitances existing in the traction power supply system, the overvoltage may lead to high-frequency electromagnetic oscillation spreading along the transmission routes. The on-board high-voltage equipments within the power supply system may suffer from the impulse brought from the VCB operational overvoltage frequently, which is possible to cause the insulation aging or breakdown issue. To investigate the characteristic of the operational overvoltage of VCBs, a ‘train-rail-catenary’ power supply model is built based on the measured impedance parameters, which has been verified by the experimental tests. The generating mechanism with the influence factors of the operational overvoltage when switching on or off the VCB has been explored, meanwhile the distribution of the train body overvoltage on each carriage has also been presented.

Abstract Organic solvent upgrading Indonesian lignite was performed in a 1 L autoclave under moderate temperature. The chemical structure and functional groups transformation of lignite upgraded by two organic solvents (ethanol and n-hexane) were analyzed to explore the upgrading mechanism of solvent thermal treatment by using Fourier transform infrared (FTIR) and 13 C nuclear magnetic resonance (NMR). In addition, the characteristics of pyrolysis of treated samples were investigated using thermo gravimetric (TG) to clarify the variance of pyrolysis reactivity. Results showed that the carbon content and calorific value of upgraded lignite were significantly improved, and H/C and O/C ratios of treated samples were significantly reduced with the temperature increasing. The relative percentage of carbonyl and carboxyl carbon, oxygenated aliphatic carbon and methoxyl carbon of lignite upgraded at 300 °C decreased by 20–30%. However, the carbon-substituted and protonated aromatic carbon at 120–135 ppm and protonated aromatic carbon at 90–120 ppm were significantly increased after lignite was upgraded by the two solvents at above 200 °C. These transformations indicated that oxygen-containing functional group was substituted by hydrogen or carbon-substituent as temperature increased, and were intensified at above 200 °C. In addition, oxygen-loss in the treated samples was attributed to the loss of carbonyl group at 175 ppm, dihydric phenol at 147 ppm, and methoxyl group at 55 ppm. The activation energy of upgraded lignite at 300 °C were higher than those of raw lignite and upgraded lignite at 100 and 200 °C, indicating the low reactivity of pyrolysis of the treated lignite with the temperature increasing.

In a milling process, proper selection of cutting parameters can significantly reduce the electrical energy consumption. Many researchers have conducted cutting parameter optimization of the milling process for electrical energy saving during the past several years. However, in the milling process, a large amount of auxiliary materials such as cutting tools and cutting fluids are consumed. The production process of these materials is energy-intensive and a lot of energy are consumed. Optimizing cutting parameters considering both the electrical energy consumption and embodied energy consumption of auxiliary materials can further reduce the environmental impact of the milling process. In this paper, an approach of cutting parameter optimization is proposed to maximize energy efficiency and machining efficiency for milling operation. Firstly, an energy consumption model of milling operation considering both the electrical energy consumption and embodied energy consumption of cutting tools and cutting fluids is proposed. Then a multi-objective optimization model is established to achieve maximizing energy efficiency and machining efficiency. Finally, to verify the proposed multi-objective model, case studies are carried out and the results indicate that (i) the optimum cutting parameters of milling process vary with the energy boundaries whether considering the embodied energy of the auxiliary materials or not; (ii) the optimum cutting parameter schemes for maximum machining efficiency do not ensure maximum energy efficiency; (iii) multi-objective optimization is an effective method to address the conflicts of the two objectives.

Artificial Intelligence (AI) is poised to revolutionize the architectural design and energy management of green buildings, offering significant advancements in sustainability and efficiency. This paper explores the transformative impact of AI on improving energy efficiency and reducing carbon emissions in commercial buildings. By leveraging AI algorithms, architects can optimize building performance through advanced environmental analysis, automation of repetitive tasks, and real-time data-driven decision-making. AI facilitates precise energy consumption forecasting and integration of renewable energy sources, enhancing the overall sustainability of buildings. Our study demonstrates that AI can reduce energy consumption and CO2 emissions by approximately 8% and 19%, respectively, in typical mid-size office buildings by 2050 compared to conventional methods. Further, the combination of AI with energy efficiency policies and low-emission energy production is projected to yield reductions of up to 40% in energy consumption and 90% in CO2 emissions. This paper provides a systematic approach for quantifying AI's benefits across various building types and climate zones, offering valuable insights for decision-makers in the construction industry.

The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle.

Electromechanical modes estimated from ambient wide area measurement systems measurements are often used as a basis for system dynamic security assessment. It is therefore important to know the accuracy of the mode estimates. There are two interesting phenomena widely reported in ambient mode estimation: (i) the accuracy of the mode parameter estimates improves under the lighter damping conditions and (ii) the frequency estimates are more accurate than the damping ratio estimates. This study aims to systematically investigate the estimation accuracy of ambient mode analysis. An analytical differentiation-based perturbation method is presented to examine the variance propagation along the mode estimation procedure. Monte Carlo simulations in the two-area system are carried out to analyse the influences of varying the mode parameters on the estimation accuracy of the inter-area mode, in which the estimated variances are served as quantitative measures of the accuracy analysis. In addition, theoretical explanations for the observations in ambient mode estimation are also provided as a supplementary. The investigations and discussion of the estimation accuracy are of value for yielding insight into the variability of the mode estimates under ambient conditions.