mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
Decentralized Voltage Control of Power Systems Using Multi-agent Systems

A comprehensive scheme based on decentralized control, partitioning, multi-agent systems, and fuzzy logic is presented in this paper for the voltage control of power systems. In our proposed smart self-healing method, two types of control agents are defined, namely master and local, which are applied in two steps. In the first step, the power system returns to the normal state after fault occurrence. Immediately after a fault detection in a power system, the system is divided into three subsystems using spectral graph partitioning. Partitioning is conducted based on reactive power flow in transmission lines. For each subsystem, a local control agent and a performance index (PI) are defined. Whenever the PI of a subsystem exceeds its threshold limit, the local control agent uses the Sugeno fuzzy system to intelligently select and apply control actions. In the second step as performed by the master control agent, the power system is transformed to an optimal state by solving the optimization problem. Simulations on a 39-bus New England reveal the effective performance of the proposed method.
Emission Trading Based Optimal Scheduling Strategy of Energy Hub with Energy Storage and Integrated Electric Vehicles

This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads. For multi-energy system (MES), the energy hub (EH) model including energy storage system and integrated electric vehicle (EV) is established. Based on the model, the influence of pollutant trading market on total operation cost is analyzed, and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES. Finally, this paper compares the economic benefit of the fixed mode and the response mode, and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency. The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably. Meanwhile, the proposed strategy is able to improve the operation economy of the EH, and ensure the better response effect of the demand side. The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.
A Comprehensive Power Flow Approach for Multi-terminal VSC-HVDC System Considering Cross-regional Primary Frequency Responses

For the planning, operation and control of multiterminal voltage source converter (VSC) based high-voltage direct current (HVDC) (VSC-MTDC) systems, an accurate power flow formulation is a key starting point. Conventional power flow formulations assume the constant frequencies for all asynchronous AC systems. Therefore, a new feature about the complex coupling relations between AC frequencies, DC voltages and the exchanged power via VSC stations cannot be characterized if VSC-MTDC systems are required to provide cross-regional frequency responses. To address this issue, this paper proposes a comprehensive frequency-dependent power flow formulation. The proposed approach takes the frequencies of asynchronous AC systems as explicit variables, and investigates the novel bus models of the interlinking buses of VSC stations. The proposed approach accommodates different operation modes and frequency droop strategies of VSC stations, and considers the power losses of VSC stations. The effectiveness and generality of the developed approach are validated by a 6-terminal VSC-HVDC test system. The test system presents the characteristics of the coexistence of numerous VSC operation modes, the absence of slack buses in both AC and DC subsystems, and diversified grid configurations such as point-to-point integration of renewable energy sources and one AC system integrated with multiple VSC stations.
Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

Accurate topology estimation is crucial for effectively operating modern distribution networks. Line outages in a distribution network change the network topology by disconnecting some parts of the network from the main grid. In this paper, an outage detection (or topology estimation) algorithm for radial distribution networks is presented. The algorithm utilizes noisy power flow measurements collected from a subset of lines in the network, and statistical information characterizing errors in forecasting load demands. Additionally, a sensor placement scheme is presented. The sensor placement provides critical sensing for the outage detection algorithm so that any number of possible outages in the network can be detected. The performance of the proposed outage detection algorithm using the proposed sensor placement is demonstrated through several numerical results on the IEEE 123-node test feeder.
Optimal Combined Heat and Power Economic Dispatch Using Stochastic Fractal Search Algorithm

Combined heat and power (CHP) generation is a valuable scheme for concurrent generation of electrical and thermal energies. The interdependency of power and heat productions in CHP units introduces complications and non-convexities in their modeling and optimization. This paper uses the stochastic fractal search (SFS) optimization technique to treat the highly non-linear CHP economic dispatch (CHPED) problem, where the objective is to minimize the total operation cost of both power and heat from generation units while fulfilling several operation interdependent limits and constraints. The CHPED problem has bounded feasible operation regions and many local minima. The SFS, which is a recent metaheuristic global optimization solver, outranks many current reputable solvers. Handling constraints of the CHPED is achieved by employing external penalty parameters, which penalize infeasible solution during the iterative process. To confirm the strength of this algorithm, it has been tested on two different test systems that are regularly used. The obtained outcomes are compared with former outcomes achieved by many different methods reported in literature of CHPED. The results of this work affirm that the SFS algorithm can achieve improved near-global solution and compare favorably with other commonly used global optimization techniques in terms of the quality of solution, handling of constraints and computation time.
A Model Predictive Control for Microgrids Considering Battery Aging

The increasing number of distributed energy resources (DERs), advancing communication and computation technologies, and reliability concerns of the customers have caused an intense interest in the concept of microgrid. Although DERs are the biggest motivation of the microgrids due to their intermittent generation characteristics, they constitute a risk for system reliability. Battery storage systems (BSSs) stand as one of the most effective solutions for this reliability problem. However, the inappropriate use of BSS creates other operational problems in power systems. In order to deal with these concerns explicitly in microgrids, an optimized microgrid central controller (MGCC) is the key factor, which controls the realtime operation of a microgrid. This work proposes a model predictive control (MPC) based MGCC that will provide optimal control of the microgrid, considering economic and operational constraints. The proposed system will minimize the energy cost of the microgrid by utilizing mixed-integer linear programming (MILP) assuming the presence of DERs and BSS as well as the bi-directional grid connection. Moreover, the aging effect of BSS will be considered in the proposed optimization problem which will provide an up-to-date system model. The proposed method is evaluated using real load and photovoltaic (PV) generation data.
A Review on Electric Vehicle Charging Infrastructure Development in the UK

This paper focuses on the development of electric vehicle (EV) charging infrastructure in the UK, which is a vital part of the delivering ultra-low-emission vehicle (ULEV) and will transition into low emission energy systems in the near future. Following a brief introduction to global landscape of EV and its infrastructure, this paper presents the EV development in the UK. It then unveils the government policy in recent years, charging equipment protocols or standards, and existing EV charging facilities. Circuit topologies of charging infrastructure are reviewed. Next, three important factors to be considered in a typical site, i. e., design, location and cost, are discussed in detail. Furthermore, the management and operation of charging infrastructure including different types of business models are summarized. Last but not least, challenges and future trends are discussed.
Investigation of accuracy of pavement mechanistic empirical prediction performance by incorporating Level 1 inputs

The recently developed AASHTOWare pavement mechanistic empirical (M-E) design requires advanced characterization of pavement materials. In particular, M-E Level 1 analysis requires materials input parameters to be obtained from the laboratory test results. In cooperation with the New Mexico Department of Transportation (NMDOT), six pavement sites were selected to compare M-E Level 1 predicted performances from laboratory tested data with the field performances. Pavement materials such as subgrade, base, asphalt concrete, binder, aggregate, etc., were collected from these pavements sites during construction. Laboratory dynamic modulus, fatigue endurance limit (FEL), dynamic shear rheometer, and resilient modulus tests were conducted on the collected materials. Using these parameters, pavement sections, as constructed, were modeled and analyzed in M-E at Levels 1, 2 and 3. The predicted distress quantities for international roughness index (IRI) and rutting were compared with the field collected data as well as among the three design levels. The analysis results demonstrated that M-E overpredicts IRI and rutting in the pavement compared to the field performance. It was also observed that Level 1 showed the lowest rutting, whereas the maximum rutting was found at Level 2. The dynamic modulus of AC layer was underpredicted at Levels 2 and 3 resulting in higher rutting and IRI.
Effect of Initial Vortex Intensity Correction on Tropical Cyclone Intensity Prediction: A Study Based on GRAPES_TYM

Predicting the intensity of tropical cyclones (TCs) is challenging in operational weather prediction systems, partly due to the difficulty in defining the initial vortex. In an attempt to solve this problem, this study investigated the effect of initial vortex intensity correction on the prediction of the intensity of TCs by the operational numerical prediction system GRAPES_TYM (Global and Regional Assimilation and Prediction System_Typhoon Model) of the National Meteorological Center of the China Meteorological Administration. The statistical results based on experiments using data for major TCs in 2018 show that initial vortex intensity correction can reduce the errors in mean intensity for up to 120-h integration, with a noticeable decrease in the negative bias of intensity and a slight increase in the mean track error. The correction leads to an increase in the correlation coefficient of V_(max) (maximum wind speed at 10-m height) for the severe typhoon and super typhoon stages. Analyses of the errors in intensity at different stages of intensity (including tropical storms, severe tropical storms, typhoons, severe typhoons, and super typhoons) show that vortex intensity correction has a remarkable positive influence on the prediction of super typhoons from 0 to 120 h. Analyses of the errors in intensity for TCs with different initial intensities indicate that initial vortex correction can significantly improve the prediction of intensity from 24 to 96 h for weak TCs (including tropical storms and severe tropical storms at the initial time) and up to 24 h for strong TCs (including severe typhoons and super typhoons at the initial time). The effect of the initial vortex intensity correction is more important for developing TCs than for weakening TCs.
Assessing the Ecological Quality of Nanjing during Its Urbanization Process by Using Satellite, Meteorological, and Socioeconomic Data

Assessing the ecological status of different districts within a city undergoing urbanization is challenging given their complex surface types and fast pace of development. In this study, we utilized satellite data obtained from Landsat 5/TM (Thematic Mapper) and Landsat 8/OLI (Operational Land Imager) images in conjunction with meteorological and socioeconomic data to construct a remote sensing ecological index (RSEI) for monitoring the ecological quality of Nanjing, Jiangsu Province. A higher RSEI value corresponded to better ecological quality. Five ratings were associated with RSEI values of city districts: very poor, poor, average, good, and excellent. In Nanjing, the percentage of areas evidencing good RSEI ratings decreased from 55.9% in 2000 to 48.0% in 2018, whereas there was a slight increase in areas with very poor RSEI ratings during this period. Of the 11 city districts, 16.8%, 21.8%, and 61.4% respectively evidenced the increasing, decreasing, and stable ecological quality relative to their quality in 2000. Of the 11 administrative districts in Nanjing, the main urban districts evidenced increased RSEI values in 2018 compared with those in 2000, with the improved areas exceeding the ones that had deteriorated in these districts. However, the ecological quality of new urban and ed because of the urban expansion, with areas that had deteriorated exceeding the improved ones. Of the three protected ecological zones, the quality of Zijin Mountain National Forest Park was considerably better than that of Laoshan and Jiangxinzhou. Overall, the urbanization rate and RSEI evidenced a high negative correlation coefficient value (–0.76). The urbanization process of Nanjing induced a declining trend for the ecological quality, indicating the need of strong protection measures for the maintenance or improvement of its ecological environment.
A Review of Research on Tropical Air–Sea Interaction, ENSO Dynamics, and ENSO Prediction in China

Remarkable progress has been made in observations, theories, and simulations of the ocean–atmosphere system, laying a solid foundation for the improvement of short-term climate prediction, among which Chinese scientists have made important contributions. This paper reviews Chinese research on tropical air–sea interaction, ENSO dynamics, and ENSO prediction in the past 70 years. Review of the tropical air–sea interaction mainly focuses on four aspects: characteristics of the tropical Pacific climate system and ENSO; main modes of tropical Indian Ocean SSTs and their interactions with the tropical Pacific; main modes of tropical Atlantic SSTs and inter-basin interactions; and influences of the mid–high-latitude air–sea system on ENSO. Review of the ENSO dynamics involves seven aspects: fundamental theories of ENSO; diagnosis and simulation of ENSO; the two types of ENSO; mechanisms of ENSO initiation; the interactions between ENSO and other phenomena; external forcings and teleconnections; and climate change and the ENSO response. The ENSO prediction part briefly summarizes the dynamical–statistical methods used in ENSO prediction, as well as the operational ENSO prediction systems and their applications. Lastly, we discuss some of the issues in these areas that are in need of further study.
Development of Climate and Earth System Models in China: Past Achievements and New CMIP6 Results

The Earth–Climate System Model (ECSM) is an important platform for multi-disciplinary and multi-sphere integration research, and its development is at the frontier of international geosciences, especially in the field of global change. The research and development (R&D) of ECSM in China began in the 1980s and have achieved great progress. In China, ECSMs are now mainly developed at the Chinese Academy of Sciences, ministries, and universities. Following a brief review of the development history of Chinese ECSMs, this paper summarized the technical characteristics of nine Chinese ECSMs participating in the Coupled Model Intercomparison Project Phase 6 and preliminarily assessed the basic performances of four Chinese models in simulating the global climate and the climate in East Asia. The projected changes of global precipitation and surface air temperature and the associated relationship with the equilibrium climate sensitivity under four shared socioeconomic path scenarios were also discussed. Finally, combined with the international situation, from the perspective of further improvement, eight directions were proposed for the future development of Chinese ECSMs.
Strength properties and evolution laws of cracked sandstone samples in re-loading tests

To study the strength properties and evolution laws of cracked sandstone samples in re-loading tests, strength and damage evolution properties of intact sandstone samples were first analyzed through the triaxial compression tests carried on TAW-2000 microcomputer control electro-hydraulic servo rock triaxial test system. Damage evolution models were established based on dilatancy properties realizing the real-time and quantitative evaluation of samples damage state in loading process. On this basis, samples with different damage were obtained by pre-peak, peak point, post-peak and residual strength stage unloading tests in the loading process of intact samples. The characteristics of the stress-strain curves and strength evolution laws were studied through the re-loading tests of samples with different damage under different stress states. The experimental results showed that the slope of stress-strain curves, peak strength and residual strength of cracked samples increased linearly with confining pressure and decreased linearly with damage. The equivalent cohesion decreased with damage in the exponential decay curves. The mechanics properties of samples transformed from strain softening to strain hardening with damage.
Ground movements modeling applying adjusted influence function

Mathematical modeling of surface deformations caused by underground mining operation is commonly carried out with use of empirical, numerical or stochastic models. One of the most frequently applied model for prediction of ground deformation in many countries is Knothe model. The model developed by Knothe belongs to the stochastic methods and is based on the influence function. In China a prediction method named Probability Integration Method (PIF) was established by Liu Baochen and Liao Guohua based on the stochastic medium theory. Modified version of that model allows to predict ground movements caused by mining operation in extremely complex technical and geological conditions. That model is commonly applied for coal, metal ore and salt deposits. The article presents several modifications of the mathematical model used in China and Poland. This model is very widespread in the world, therefore the generalizations proposed in the article can be implemented for the purposes of prediction surface deformations for various types of deposits in many countries. The presented generalizations were then tested on specific examples of coal mining, copper ore mining and rock salt deposit. The obtained results indicate high efficiency of methods based on the influence function in complex geological and mining conditions.
Coal mine roof rating (CMRR), rock mass rating (RMR) and strata control: Carborough Downs Mine, Bowen Basin, Australia

The rock mass rating (RMR) has been used across the geotechnical industry for half a century. In contrast, the coal mine roof rating (CMRR) was specifically introduced to underground coal mines two decades ago to link geological characterization with geotechnical risk mitigation. The premise of CMRR is that strength properties of mine roof rock are influenced by defects typical of coal measures stratigraphy. The CMRR has been used in longwall pillar design, roof support methods, and evaluation of extended cuts, but is rarely evaluated. Here, the RMR and CMRR are applied to a longwall coal mine. Roof rock mass classifications were undertaken at 67 locations across the mine. Both classifications showed marked spatial variability in terms of roof conditions. Normal and reverse faulting occur across the mine, and while no clear relationships exist between rock mass character and faulting, a central graben zone showed heterogeneous rock mass properties, and divergence between CMRR and RMR. Overall, the CMRR data fell within the broad envelope of results reported for extended cuts at Australian and U.S. coal mines. The corollary is that the CMRR is useful, and should not be used in isolation, but rather as a component of a strata control programme.
Methods applied in Australian industry to evaluate coal mine slope stability

Strata failure is a principal hazard in open cut coal mining as it has the potential to cause multiple fatalities. Prior to the excavation of any slope, a geotechnical assessment should review the likely slope performance, including the risk of slope failure. Controls to manage this risk to an acceptable level should accompany the geotechnical analysis. A survey of 43 practising geotechnical engineers indicated that kinematic and 2D limit equilibrium analyses were the methods most commonly applied to analyse excavated slope stability. While these methods are well established and widely applied in the broad rock engineering disciplines (e.g. civil, hard rock), a recent review of over 60 slope failures suggests these methods have limited suitability for modelling the complex failure mechanisms observed in excavated coal mine slopes. Kinematic techniques do not adequately capture the rock mass component of excavated slope failure and do not provide a geospatial location of potential failure, while 2D limit equilibrium techniques do not adequately capture the 3D mechanisms of excavated slope failures. Methods which do consider the 3D mechanisms of slope failure are summarized for industry consideration and application.
Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019

To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist- and computer-derived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r = -0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r ¼ 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (c2 ¼ 8.160, P ¼ 0.004). Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans.