Multiple Stations Research Articles

Distributed economic operation strategy of charging stations based on consistency algorithm

Considering that the centralized optimization control strategy is difficult to adapt to the access of large-scale electric vehicles, a distributed collaborative control strategy based on consensus algorithm is proposed in this paper to control the operation of multiple charging stations. In order to reduce the computational cost, a charging station aggregation model is established to reduce the optimization variables of large-scale electric vehicle access. According to the dispatching instructions issued by the distribution network, this method takes the economic operation of charging stations as the goal, satisfies the global and local constraints, and realizes the power allocation among charging stations. The case analysis proves the accuracy, effectiveness and robustness of the method, and the adaptability to large-scale electric vehicle access.

Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks

Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic.

Global shockwaves of the Hunga Tonga-Hunga Ha’apai volcano eruption measured at ground stations

The eruption of the Tonga volcano created globally propagating spherical shockwaves in the atmosphere. Analyses are done to data from two southern U.S. stations of the author sampling at 3-21s intervals and 189 weather stations at 1-5min intervals. The shockwaves arrived from two routes in the atmosphere: the shortest spherical arc and the longer spherical arc through the antipole. In most stations, signals up to the 6th path of shockwaves were recorded as the waves traveled around the globe multiple times. The speed of shockwaves is estimated to be 309.5± 2.9m/s, consistent with the speed of sound at the top of the troposphere where a waveguide exists. Discussion is made on the post-shockwave ringing of 4-8min as higher amplitude oscillations above the level of pre-shockwaves background noise. A theoretical wave dispersion is derived which verifies that the spherical shockwave's phase speed is the same as the speed of sound.

Unified Power Quality Management for Traction Substation Groups Connected to Weak Power Grids

In remote areas, weak power grids, fewer grid access points, and power quality (PQ) problems dominated by excessive voltage unbalance (VU) and lower power factor (PF) have been prominent obstacles in single-phase 25 kV AC traction power supply systems. To achieve a long-distance power supply with a high PQ, a traction substation group connected to a weak remote power grid is introduced, where the electrical structure is composed of multiple slave traction substations using single-phase traction transformers (TTs), a master traction substation (MTS) using a combination of TTs and a power flow controller (PFC) adopting a modular multilevel converter (MMC). Then, a unified mathematical model of the proposed system suitable for different TT wiring types revealing the effect of asymmetrical grids on the PFC is established. The negative sequence current (NSC) caused by multiple traction stations at the point of common coupling (PCC) and the PF can be improved effectively based on power conservation theory. In addition, to reduce the compensation capacity, an optimization compensation strategy is proposed. Finally, the effectiveness of the proposed algorithm is verified by simulation and a case study. The installed capacity of the PFC is 18.5% lower than that of traditional compensation.

S3701 Necrotic Ulcer From Diffuse Large B-Cell Lymphoma

Introduction: Diffuse B cell lymphoma is a non-Hodgkin’s lymphoma that mainly occurs within the lymph nodes. However, about 40% of the time it occurs in extranodal sites. The most common sites are within the gastrointestinal tract, especially within the stomach and ileocecal regions. We present a case of a patient with diffuse large B Cell lymphoma within the cardia of the stomach. Case Description/Methods: A 69-year-old male with a past medical history of diffuse large B cell lymphoma was admitted for active melena. Further workup revealed a hemoglobin of 5.6 g/dL with a baseline around 10 g/dL, as well as a BUN of 67 mg/dL. Aside from mild tachycardia the patient was otherwise hemodynamically stable. He had received K Centra and one unit of blood in the emergency room. A CT angiogram of the abdomen showed recurrence of diffuse large B cell lymphoma with multiple lymph node stations in the pancreas, left kidney, and associated vasculature. Ultimately, the patient underwent an endoscopy. A necrotic ulcer measuring 5 cm in the cardia of the stomach with pigmented material on the surface indicating recent bleeding. Multiple biopsies were taken of each of these ulcers to rule out underlying malignancy. The pathology report revealed that the chronic ulcer in the cardia of the stomach was diffuse large B cell lymphoma and no evidence of Helicobacter pylori. The patient was treated with pantoprazole and Carafate. Their hemoglobin remained stable after 3 units of packed red blood cells and the patient was discharged. However, it was advised for them to follow up outpatient with hematology and oncology however he has since elected for hospice (Figure 1). Discussion: Diffuse large B cell lymphoma is the most common type of non-Hodgkin's lymphoma. The stomach is the most common extranodal site involved in non-Hodgkin's lymphoma representing up to 65% of all GI lymphomas. In these patients, symptoms may include dyspepsia, nausea, and vomiting. Primary gastric lymphoma is the most common extranodal lymphoma but only accounts for 2-8% of all stomach cancer. Thus, our case of diffuse large B lymphoma in the cardia of the stomach is a rare entity. Most patients are treated with conservative management like chemotherapy or immunotherapy. One retrospective study showed the use of R-CHOP to have a high remission rate. Surgical intervention is usually reserved for lesions that cause perforation, obstruction, or intractable bleeding. Our case would have required surgical intervention had the bleeding continued.Figure 1.: 5 cm necrotic ulcer found on retroflexion in gastric cardia.

A UAV swarm communication network architecture based on consortium blockchain

In the context of the development of UAVs towards clustering, intelligence, and autonomy, it is necessary to ensure that multiple UAVs and ground control stations can communicate and share information securely in real-time so that UAV swarms can make real-time and effective decisions and collaborations. These factors pose challenges to the security, robustness, and operational efficiency of UAV swarm communication networks. This paper combines blockchain technology with a UAV swarm communication network, proposes a new UAV swarm communication network architecture based on consortium blockchain, and describes the network structure and transaction process in detail. The performance analysis shows that the security mechanism of the architecture can meet the security requirements of the UAV swarm communication network. And this solution is superior to existing solutions based on blockchain technology such as Ethereum in performance and is a technical solution with both security and usability.

Research on a Real-Time Monitoring Method for the Three-Dimensional Straightness of a Scraper Conveyor Based on Binocular Vision

Measuring the straightness of the scraper conveyor, which is an indispensable piece of equipment in a fully mechanized coal face, can prevent accidents such as derailment of the shearer and is also important for the precise positioning of the shearer and the accurate control of the hydraulic support. The existing scraper conveyor straightness measurement methods have the disadvantages of inconsistent measurement cost, accuracy, and reliability as well as low dimension of straightness description. To this end, this paper proposes a method for monitoring the three-dimensional straightness of a scraper conveyor based on binocular vision. Trapezoidal window matching technology was used to realize the image acquisition of multiple stations and multiple sensors. The bit pose acquisition model of binocular vision 3D reconstruction was used to obtain the 3D coordinates of the feature points on the sign board in each local coordinate system and the bit pose information of each sign board. The pose relay videometric method was used to convert the straightness in each local coordinate system to the global coordinate system to realize the 3D reconstruction of the scraper conveyor. Finally, the straightness measurement test of the preset scraper conveyor was carried out, and the error analysis was carried out by comparing with the manual measurement results, which showed that the measurement errors were all within ±30 mm. At the same time, the standard deviation of errors in both directions was small, which indicates that the straightness measurement method for the scraper conveyor in this paper has high reliability. In addition, the visual measurement process is independent of each other, so there is no error accumulation in the visual straightness measurement method. The analysis shows that the method of measuring the straightness of a scraper conveyor based on binocular vision can realize continuous and real-time monitoring of the scraper conveyor.

A holistic approach for modeling and predicting bike demand

Bike-sharing systems have enjoyed tremendous success in many major cities around the world today as a new means of urban public transportation offering a green and facile solution for daily commuters and tourists. One common problem featured in these systems is that the distribution of bikes among stations can be quite uneven, due to various factors including topography, location of dockings, hours of service, safety and security, weather, rush hours or even during the occurrence of major events around the city. Such imbalances often result in shortage of bikes or bike parking racks. An unbalanced bike system indicates an unreliable form of transportation and disappointed users. Current studies in the literature are limited as they are not designed to handle fluctuating, high or unpredictable demand during large city events that typically affect multiple stations and require real-time rebalancing, during the event, to ensure seamless operation. In this work we solve the bike rebalancing problem while considering fluctuating demand that leads to an imbalance between supply and demand. We present “SmartBIKER”, a holistic and cost-effective framework for bike sharing systems addressing both normal operation and operation during major city events. SmartBIKER models bike demand during both normal operation and major events, identifies bike stations with low or high demand using two different forecasting models and determines a relocation strategy that maximizes the utility of the stations while minimizing the relocation cost. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art relocation and prediction schemes.

Optimization of multiple battery swapping stations with mobile support for ancillary services

The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging–discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging–discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging–discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.

Non-uniform effect of COVID-19 lockdown on the air quality in different local climate zones of the urban region of Kochi, India

Deterioration of ambient air quality associated with urbanisation is a serious concern in many parts of the world. In India, air pollution, primarily due to particulate matter, has increased exponentially in the last few decades due to rapid urbanization, industrialization and population growth. This study investigates the non-uniform influence of COVID-19 lockdown on the ambient air quality of three distinct local climate zones (LCZs) within the urban region of Kochi (Kerala, India). The analysis of the air pollutant data of the ambient air quality monitoring stations during the pre-lockdown (PRLD), lockdown (LD) and post-lockdown (PTLD) periods of 2021 implies the significance of lockdown measures in the improvement of urban air quality. The air quality index (AQI) exhibits characteristic variability in different LCZs and contrasting behaviour between the LD period of 2020 and 2021, primarily due to the differences in the lockdown restrictions and strategies as well as the influence of local climatic factors. This study highlights the need for multiple monitoring stations in the urban regions with respect to different LCZs to identify the urban air quality hot spots.

Day-Ahead Hourly Solar Irradiance Forecasting Based on Multi-Attributed Spatio-Temporal Graph Convolutional Network.

Solar irradiance forecasting is fundamental and essential for commercializing solar energy generation by overcoming output variability. Accurate forecasting depends on historical solar irradiance data, correlations between various meteorological variables (e.g., wind speed, humidity, and cloudiness), and influences between the weather contexts of spatially adjacent regions. However, existing studies have been limited to spatiotemporal analysis of a few variables, which have clear correlations with solar irradiance (e.g., sunshine duration), and do not attempt to establish atmospheric contextual information from a variety of meteorological variables. Therefore, this study proposes a novel solar irradiance forecasting model that represents atmospheric parameters observed from multiple stations as an attributed dynamic network and analyzes temporal changes in the network by extending existing spatio-temporal graph convolutional network (ST-GCN) models. By comparing the proposed model with existing models, we also investigated the contributions of (i) the spatial adjacency of the stations, (ii) temporal changes in the meteorological variables, and (iii) the variety of variables to the forecasting performance. We evaluated the performance of the proposed and existing models by predicting the hourly solar irradiance at observation stations in the Korean Peninsula. The experimental results showed that the three features are synergistic and have correlations that are difficult to establish using single-aspect analysis.

STAR: a carrier sense agnostic MAC scheme for a crowded NLoS-FSOC optical LAN

Nondirect line-of-sight free-space optical communications (NLoS-FSOC) operate based on an optical wireless broadcast channel shared by multiple stations to communicate, thus forming an optical local area network (OLAN). Such a channel in the NLoS-FSOC is generated by a diffuse reflector (DR) that reflects light equally in all directions except toward itself. While the broadcast channel of an OLAN is accessible to many stations as in radio-frequency wireless LANs, the signal strength of the reflected beam in an OLAN depends not only on the receiver-DR distance, but also on the angle of incidence of the transmitted beam. An incident beam with a wide angle generates a weak reflected signal that may be undetectable by some stations in the communication range. The loss of the reflected power may render channel sensing ineffective. To address this challenge, we propose the use of the access point to indicate the time to attempt accessing the channel. We also propose a medium access control (MAC) scheme for OLANs using explicit start rather than the conventional channel sensing. The proposed MAC scheme, called STAR, not only enables stations to contend for the available channel, but also decreases collisions, and in turn, stations experience high throughput and low delay. Furthermore, we generalize STAR for the transmission of voice, video, and data traffic. We compare STAR to leading MAC schemes, adapted for their use on an OLAN. We show that STAR achieves 83% throughput for differential traffic and is a 15% higher throughput than the throughput of IEEE 802.11.

Simulating inter-individual contact in the inter-station passenger transfer system connecting multiple metro stations based on space–time path data

Determining passengers’ inter-individual contact in the metro station area (MSA) is an important issue to simulate and mitigate the spread of the Coronavirus disease 2019 (COVID-19) pandemic. Taking the inter-station passenger transfer system (IPTS) as an example, this study aimed to verify the passenger flows’ influence on the inter-individual contact around the MSA. Based on actual observed data, the passengers’ space–time paths (STP) in the network were obtained through an agent-based simulation. In this study, the direct contact model and the mediate contact model were used to describe the inter-individual contact in view of the passengers’ STP. The contact count and the exposure duration were defined as indicators to measure the contact degree of individual and the system. The results show that the time-varying trip distribution of the metro passengers significantly affected the inter-individual contact degree and the spatial distribution of contact risk region in the MSA. The intersection of passenger flow in different directions and the concentrated movement of passenger flow in the same direction increased the inter-individual contact and prolonged exposure in the morning. Through simulation experiments, the study verified the effects of controlling the flow direction and equalizing passenger flow generation measures aiming to reduce inter-individual contact and cumulative exposure duration.

Magnetohydrodynamic simulation of coronal mass ejections using interplanetary scintillation data observed from radio sites ISEE and LOFAR

Interplanetary scintillation (IPS) is a useful tool for detecting coronal mass ejections (CMEs) throughout interplanetary space. Global magnetohydrodynamic (MHD) simulations of the heliosphere, which are usually used to predict the arrival and geo-effectiveness of CMEs, can be improved using IPS data. In this study, we demonstrate an MHD simulation that includes IPS data from multiple stations to improve CME modelling. The CMEs, which occurred on 09–10 September 2017, were observed over the period 10–12 September 2017 using the Low-Frequency Array (LOFAR) and IPS array of the Institute for Space-Earth Environmental Research (ISEE), Nagoya University, as they tracked through the inner heliosphere. We simulated CME propagation using a global MHD simulation, SUSANOO-CME, in which CMEs were modeled as spheromaks, and the IPS data were synthesised from the simulation results. The MHD simulation suggests that the CMEs merged in interplanetary space, forming complicated IPS g-level distributions in the sky map. We found that the MHD simulation that best fits both LOFAR and ISEE data provided a better reconstruction of the CMEs and a better forecast of their arrival at Earth than from measurements when these simulations were fit from the ISEE site alone. More IPS data observed from multiple stations at different local times in this study can help reconstruct the global structure of the CME, thus improving and evaluating the CME modelling.

MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88–0.93, mean absolute error of 0.17–0.19, and normal root mean square error of 0.05–0.09. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

Hilbert–Huang Transform and Energy Rate Functions for Earthquake Source Characterization—A Study from the Japan Trench

ABSTRACT The Hilbert–Huang Transform (HHT) has been sparsely applied to problems in seismology, although previous studies have pointed to its broad scope. In this maiden attempt, we use the HHT to represent earthquake energy release duration and frequency content and compare the results with two conventional inversion methods. By selecting examples from interplate, intraplate, and intraslab settings, we demonstrate that the HHT has the power to discriminate energy release of earthquakes with different tectonic affiliations. We observe that the dominant frequencies for energy release are higher for intraslab earthquakes than for interplate and intraplate events. We use the empirical mode decomposition-based HHT and introduce a new parameter, which we name the energy rate function (ERF), to quantify the energy release. By employing empirical Green’s functions to remove the path and site effects and using a linear combination of a select set of intrinsic mode functions, we generate the station-specific relative measure of energy that we refer to as relative ERFs (RERFs). Averaged over RERFs from multiple stations, the ERF represents a measure of the total relative energy release, comparable to the moment rate functions (MRFs) and SCARDEC source time functions (STFs). Results for six of the seven earthquakes we analyzed show high cross correlation with the STFs (0.84 ± 0.03) and MRFs (0.79 ± 0.06), but there are mismatches between ERFs and MRFs or STFs when the energy release is complex and involves multisegment or bilateral ruptures. The proposed method is computationally efficient, requiring only 3.46 ± 2.62 s on average, compared to ~20 min (~1200 s) for the teleseismic inversion method we employ. With its ability to represent the seismic source in terms of energy release, the ERF method has the potential to evolve not as an alternative to waveform inversion but as a rapid time–frequency analysis tool, useful for earthquake hazard assessment.

A multivariate Chain-Bernoulli-based prediction model for cyanobacteria algal blooms at multiple stations in South Korea

Predicting the occurrence of algal blooms is of great importance in managing water quality. Moreover, the demand for predictive models, which are essential tools for understanding the drivers of algal blooms, is increasing with global warming. However, modeling cyanobacteria dynamics is a challenging task. We developed a multivariate Chain-Bernoulli-based prediction model to effectively forecast the monthly sequences of algal blooms considering hydro-environmental predictors (water temperature, total phosphorus, total nitrogen, and water velocity) at a network of stations. The proposed model effectively predicts the risk of harmful algal blooms, according to performance measures based on categorical metrics of a contingency table. More specifically, the model performance assessed by the LOO cross-validation and the skill score for the POD and CSI during the calibration period was over 0.8; FAR and MR were less than 0.15. We also explore the relationship between hydro-environmental predictors and algal blooms (based on cyanobacteria cell count) to understand the dynamics of algal blooms and the relative contribution of each potential predictor. A support vector machine is applied to delineate a plane separating the presence and absence of algal bloom occurrences determined by stochastic simulations using different combinations of predictors. The multivariate Chain-Bernoulli-based prediction model proposed here offers effective, scenario-based, and strategic options and remedies (e.g., controlling the governing environmental predictors) to relieve or reduce increases in cyanobacteria concentration and enable the development of water quality management and planning in river systems.

Daily peak-shaving model of cascade hydropower serving multi-grids considering an HVDC channel shared constraint

High-voltage direct current (HVDC) is widely applied in large-scale hydropower transmission in China because of its long distance, large capacity and low power loss. However, when multiple hydropower stations send power by sharing one HVDC transmission channel, it is challenging to ensure the security of the sending-end power grid. To address this problem, a short-term peak-shaving model serving multiple power grids considering an HVDC channel shared (HCS) constraint is proposed. First, a daily peak-shaving model for cascade hydropower stations serving multiple power grids coupling with conventional HVDC constraints is established. Second, for the power transmission of different hydropower stations sharing one HVDC channel, the HCS constraint restructured by set-calculation is integrated into the model. Finally, the Big-M method is adopted to linearize the restructured HCS constraint to build an MILP model due to its flexibility in handling massive inequality constraints. The result from the case study shows that the proposed HCS constraint can be well coupled into the model to ensure power transmission security. The peak-valley difference in dry and flood season reduced by 37% and 21%, respectively, which indicates that the model can make full use of the flexibility of hydropower to achieve a satisfactory peak-shaving result.

Multi-Station Multi-Axis Hybrid Layered Manufacturing (MSMA-HLM)

Additive manufacturing is advanced manufacturing technology in which material is added layer by layer. The hybrid approach with conventional and additive processes is utilized for making the component faster and cost-effective. Hybrid Layered Manufacturing (HLM) is the process in which a 3D component is made by adding and subtracting material layer by layer. HLM is basically for a metallic component in which material is added by depositing metal in wire form through an arc, LASER, EBM, and a small amount of material is removed by machining and thus making the system hybrid. There are various levels of hybridization in terms of approach, material, layering, joining process, kinematics and operations. A complete setup of HLM is developed with multiple stations and with a simultaneous five-axis system named as Multi-Station Multi-Axis Hybrid layered Manufacturing (MSMA-HLM). MSMA-HLM has its application in aerospace, automobile, marine, medical, etc. The various case studies with the HLM process are also demonstrated in this paper with cost and time compared with conventional manufacturing. The need for multiple stations during the HLM process is explained in this paper. The preheating, hammering, and inspection units are the add-ons in the manufacturing process for making defect-free components.

Comprehensive Control of Reflux Safety Parameters Based on Flexible Grounding Scheme in DC Railway Electrification System

Currently, the excessive rail potential (RP) and stray current (SC) are disasters to the safety of equipment and staff in dc railway electrification system (dc RES). However, abnormal increase of RP and SC still exists and the coupling relationship between RP and SC with the complex grounding scheme has not been clarified. In this article, a flexible grounding scheme (FGS) is proposed to realize the comprehensive control of RP and SC with dynamic operation of multitrain. By establishing the simulation model of dc RES with FGS, the coupling mechanism of the abnormal increase between RP and SC is clarified. In order to prove the effectiveness of FGS, simulation results of dc RES with ungrounded scheme, over voltage protection device (OVPD) grounded scheme and FGS are compared. Field tests of reflux safety parameter (RSP) are carried out in Hefei Metro Line 2 with different grounding resistance. Simulations and field tests show that coupling effect between RP and SC at multiple stations is obvious, and FGS can realize the comprehensive control of RP and SC effectively.