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Abstract This study investigates the non-linear relationship between urbanization paths and CO2 emissions in selected South, South-East, and East Asian countries over the period 1971–2014. Based on the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) framework, we applied the advanced and robust methods of dynamic seemingly unrelated regression (DSUR), dynamic OLS (DOLS), and fully modified OLS (FMOLS) to estimate the long-term effects. The empirical findings revealed the inverted U-shaped effects of urbanization and urban agglomeration and the U-shaped impact of the largest city ratio on CO2 emissions. Urbanization and urban agglomerations improve environmental quality in the long-run and support ecological modernization theory. However, excessive concentration in the largest cities have severely affected the environmental quality and violates the notion of compact-city efficiencies. Moreover, energy intensity and economic growth positively affect CO2 emissions, while trade openness negatively influences CO2 emissions. Our robustness analysis at the country-level applies the augmented mean group (AMG) panel ARDL technique, which further supports the non-linear effect of urbanization paths on CO2 emissions except for a few countries. The results of the panel Granger non-causality approach unveil bidirectional causality of energy efficiency, economic growth, urbanization, and largest city ratio with CO2 emissions. In contrast, unidirectional causality runs from urban agglomeration to CO2 emissions. Our findings have important policy implications as we suggest green urban infrastructures, eco-friendly dwellings, smart cities, country-specific trade policies, and renewable energy options to improve the environmental quality.

The battery power state (SOP) is the basic indicator for the Battery management system (BMS) of the battery energy storage system (BESS) to formulate control strategies. Although there have been many studies on state estimation of lithium-ion batteries (LIBs), aging and temperature variation are seldom considered in peak power prediction during the whole life of the battery. To fill this gap, this paper aims to propose an adaptive peak power prediction method for power lithium-ion batteries considering temperature and aging is proposed. First, the Thevenin equivalent circuit model is used to jointly estimate the state of charge (SOC) and SOP of the lithium-ion power battery, and the variable forgetting factor recursive least squares (VFF-RLS) algorithm and extended Kalman filter (EKF) are utilized to identify the battery parameters online. Then, multiple constraint parameters including current, voltage, and SOC were derived, considering the dependence of the polarization resistance of the battery on the battery current. Finally, the verification experiment was carried out with LiFePO4 battery. The experimental results under FUDS operating conditions show that the maximum SOC estimation error is 1.94%. And the power prediction errors at 20%, 50%, and 70% SOC were 5.0%, 8.1% and 4.5%, respectively. Our further work will focus on the joint estimation of battery state to further improve the accuracy.

AbstractVentilation door are commonly found in tunnels and other underground engineering ventilation structures, disaster periods using its explosion isolation, explosion relief, wind regulation characteristics for disaster prevention and mitigation is of great significance. This paper numerically simulates the propagation characteristics of the gas explosion shock wave in the nearby tunnel when the ventilation door are opened at different degrees, and analyzes the influence mechanism of the opening degree on the change law of the shock wave overpressure distribution in the nearby tunnel. The results show that the shock wave forms a strong turbulence area (high pressure area) on both sides in front of the ventilation door, and the area range and the overpressure value decrease with the increase of the opening degree; the ventilation door reduce the intensity of the shock wave, so that the overpressure behind the ventilation door decreases, and the smaller the opening degree, the lower the overpressure behind the ventilation door. The secondary explosion formed shock wave and the ventilation door reflected shock wave meet to form a stronger shock wave, which leads to different opening degrees of ventilation door, its before, after the roadway and after the bifurcation of the main roadway in the measured points of the overpressure change curve is different, the main difference is that the peak overpressure for the first wave or the second wave peak. The peak overpressure in the tunnel before and after the ventilation door decreases and increases respectively with the increase of the opening length, and the overall decay of the peak overpressure at 5 m and 10 m before the ventilation door is 49.56% and 4.04% respectively and only has an effect on the peak overpressure in main tunnel within 20 m from the bifurcation.

Abstract Newer generation sources and loads are posing new challenges to the conventional power system protection schemes. Adaptive and intelligent protection methodology, based on advanced measurement techniques and intelligent fault diagnosis such as machine learning (ML), is found to be useful to meet these challenges. A large number of research works are reported on ML-based power system fault diagnosis. However, ML techniques are evolving at a very fast pace, and an inclusive, as well as state-of-the-art review on ML-based power system fault diagnosis, is not available in the literature. Given this need and growing trend towards ML, the study presented in this paper aims to provide a comprehensive review of ML-based power system fault diagnosis. At first, efforts have been made to enlist the issues present in conventional fault diagnosis which led to the popularity of ML techniques. Also, a baseline framework and workflow for ML-based fault diagnosis are presented. Next, various unsupervised and supervised learning techniques have been discussed separately which have been used by several researchers for fault diagnosis. The discussion throughout is supported with tabulated facts for fault detection, classification and localization works with techniques used, different simulation tools used, and their application system. The advantages and disadvantages of all the techniques of fault diagnosis have also been discussed which will help the readers in the selection of techniques for their research. A brief review of reinforcement learning and transfer learning is also given as they are gaining popularity in power system-related studies and have the potential to be used for fault diagnosis. Finally, the research trends, some key issues, and directions for future research have been highlighted.

The objective of power system restoration is to re store the load as soon as possible, while the successful start-up of generator is the precondition of load recovery and the emphasis of power system restoration. In this paper, both the generator start-up sequence and the restoration path are considered. A preference multi-objective model which considers the difference of the objectives' importance is proposed. To get the most preferred alternatives, a non-r-dominance sorting genetic algorithm II (r-NSGA-II) is used to solve the model. The reference point determination method and solution set scale controlling method are proposed to improve the algorithm, which makes the solution set scale controllable and the preference to be better expressed. Simulation results show that the proposed method has practical value when solving the generator start-up problem for network reconfiguration.

A GIS-based method is proposed to identify critical links in urban road networks. This study utilizes a geographic information system (GIS) to evaluate the distribution of road infrastructure, road density, and network accessibility at the micro, meso, and macro levels. At the micro level, GIS is used to assess the distribution of public facilities along the roads. At the meso level, a city’s road density distribution is evaluated. At the macro level, a spatial barrier model and a transportation network model are constructed to assess the network accessibility. An inverse distance weighting method is employed to interpolate the accessibility. Furthermore, a network topology is established, and the entropy method is utilized to evaluate the sections comprehensively. The sections are ranked based on the evaluation results to identify the critical links in the urban road network. The road-network data and points of interest (POI) data from the Anning District in Lanzhou are selected for a case study, and the results indicate that the top five critical links have scores of 0.641, 0.571, 0.570, 0.519, and 0.508, respectively. Considering the three indicators enhances the accuracy of critical section identification, demonstrating the effectiveness of the proposed method. Visualizing each indicator using GIS 10.7 provides a new approach to identifying critical links in urban road networks and offers essential theoretical support for urban planning.

Oil shale is a very important alternative energy, hydraulic mining is an effective method for oil shale utilization in Jilin Province. For the Nong'an oil shale in Jilin Province, the average compressive strength of horizontal bedding is 17.14MPa, and the average compressive strength of vertical bedding is 10.90MPa. For cone straight-shaped nozzle, when the jet pressure P is 10MPa and the nozzle outlet diameter d is 1mm, striking force generated by the jet is 15.65N, it can break the oil shale.

The present paper examines the vortex-induced vibration (VIV) of a 5:1 rectangular cylinder with a detached splitter in its near wake. The tested gap ratio between the cylinder and the splitter (g/D, where g is the gap between the cylinder rear and the splitter and D is the depth of the rectangular cylinder) ranges from 0.5 to 2.0, with an increment of 0.5. To serve as a reference case, the rectangular cylinder without the splitter is also tested under the same conditions. The test Reynolds number ranges from 32 320 to 56 507. This study delves into the vibration response, pressure distributions, and power spectral densities (PSD) of the cylinder under varying gap ratios. Based on qualitative and quantitative analyses between the cylinder and the splitter using phase average techniques, smoke-wire visualization, and numerical simulation, the different vortex shedding modes according to different gap ratios were identified. Experimental and numerical results show that the detached splitter and its gap ratio play important roles in determining the cylinder VIV properties. For g/D = 0.5, the detached splitter has a sensible mitigation on the cylinder VIV. However, as the gap ratio increases, the VIV response initially recovers to the reference case at g/D = 1.0 and subsequently enlarges at g/D = 1.5 and 2.0. The pressure distribution results showed that the detached splitter demonstrates its effects primarily through fluctuations in the pressure field rather than the mean field. In addition, at g/D = 0.5, a sensibly decayed PSD is observed, while at g/D = 1.0–2.0, an intensified PSD is detected. The underlying mechanism of the detached splitter on the VIV of the 5:1 rectangular cylinder should be attributed to the von Kármán vortex street compared to the reference case.