Rolling Manner Research Articles

Multi-Timescale Reactive Power Optimization and Regulation Method for Distribution Networks Under a Multi-Source Interaction Environment

In the context of constructing new power systems, distribution networks are increasingly incorporating distributed resources such as distributed photovoltaic (PV) systems, decentralized wind turbines (WTs), and new types of energy storage system (ESS), which may lead to prominent issues such as voltage overruns and reverse heavy overloads in the distribution network. While distributed resources are valuable for voltage regulation, their regulation characteristics vary with their operation means, and the randomness and volatility of renewable power generation will also influence the optimization and regulation of voltage in the distribution network. This paper proposes a multi-timescale reactive power optimization and regulation method for distribution networks in a multi-source interactive environment. Firstly, the voltage regulation characteristics of distributed PV systems, decentralized ESSs, and distributed WTs are analyzed. Based on this analysis, a multi-timescale voltage optimization scheme for distribution networks using the MPC method is proposed, which optimizes the voltage regulation strategies for each distributed resource in a rolling manner. Furthermore, an event-triggered real-time voltage zoning control strategy based on voltage sensitivity is proposed to address the real-time sudden voltage overlimit problems. The modified IEEE 33-node system is used to verify the performance of the proposed method. Simulation results indicate that the issue of voltage overruns at distribution network nodes has been improved, and the intraday rolling optimization yields results are more realistic compared with the day-ahead optimization method.

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments benefit from improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging due to the high noise typical of environmental samples. We have developed a generalized pipeline using in- and out-of-sample model selection to test the ability of different correction models to reduce the variance in wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but the pipeline should be used to reevaluate any new data set as the sources of variance may change across locations, lab methods, and disease states. Using our best-fit model, we investigated the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We did this in a rolling manner for corrected wastewater data and for other prevalence indicators and statistically compared the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We found that wastewater trends often lead other COVID-19 indicators in predicting new surges.

Droplet Rolling Transport on Hydrophobic Surfaces Under Rotating Electric Fields: A Molecular Dynamics Study.

Driving droplets by electric fields is usually achieved by controlling their wettability, and realizing a flexible operation requires complex electrode designs. Here, we show by molecular dynamics methods the droplet transport on hydrophobic surfaces in a rolling manner under a rotating electric field, which provides a simpler and promising way to manipulate droplets. The droplet internal velocity field shows the rolling mode. When the contact angle on the solid surface is 144.4°, the droplet can be transported steadily at a high velocity under the rotating electric field (E = 0.5 V nm-1, ω = π/20 ps-1). The droplet center-of-mass velocities and trajectories, deformation degrees, dynamic contact angles, and surface energies were analyzed regarding the electric field strength and rotational angular frequency. Droplet transport with a complex trajectory on a two-dimensional surface is achieved by setting the electric field, which reflects the programmability of the driving method. Nonuniform wettability stripes can assist in controlling droplet trajectories. The droplet transport on the three-dimensional surface is studied, and the critical conditions for the droplet passing through the surface corners and the motion law on the curved surface are obtained. Droplet coalescence has been achieved by surface designs.

Optimal Allocation of Virtual Inertia and Droop Control for Renewable Energy in Stochastic Look-Ahead Power Dispatch

To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes. Since the long-term RES prediction involves significant errors, we need online configure the frequency control parameters of RESs in a rolling manner to improve the operation economics under the premise of stabilizing system frequency. To address this concern, this paper proposes a frequency constrained stochastic look-ahead power dispatch (FCS-LAPD) model to formulate the frequency control parameters of RESs and Energy Storage Systems (ESSs) as scheduling variables, which can optimally allocate the virtual inertia and droop coefficient of RESs and ESSs. In this FCS-LAPD model, the uncertainties of RESs are characterized using Gaussian Mixture Model (GMM). The required reserves are determined by frequency control parameters, and the reserve cost coefficients are adjusted properly to allocate the reserves according to the predicted power generation. Due to the nonlinearity of the frequency nadir constraint, a convex hull approximation method is proposed to linearize it with guaranteed feasibility. The proposed FCS-LAPD is ultimately cast as an instance of quadratic programming and can be efficiently solved. Case studies on modified IEEE 24-bus system and a provincial power system in China are conducted to show the effectiveness of the proposed model.

A Dual Long Short-Term Memory Model in Forecasting the Number of COVID-19 Infections

Since the outbreak of the Coronavirus Disease 2019 (COVID-19), the spread of the epidemic has been a major international public health issue. Hence, various forecasting models have been used to predict the infectious spread of the disease. In general, forecasting problems often involve prediction accuracy decreasing as the horizon increases. Thus, to extend the forecasting horizon without decreasing performance or prediction, this study developed a Dual Long Short-Term Memory (LSTM) with Genetic Algorithms (DULSTMGA) model. The model employed predicted values generated by LSTM models in short-forecasting horizons as inputs for the long-term prediction of LSTM in a rolling manner. Genetic algorithms were applied to determine the parameters of LSTM models, allowing long-term forecasting accuracy to increase as long as short-term forecasting was accurate. In addition, the compartment model was utilized to simulate the state of COVID-19 and generate numbers of infectious cases. Infectious cases in three countries were employed to examine the feasibility and performance of the proposed DULSTMGA model. Numerical results indicated that the DULSTMGA model could obtain satisfactory forecasting accuracy and was superior to many previous studies in terms of the mean absolute percentage error. Therefore, the designed DULSTMGA model is a feasible and promising alternative for forecasting the number of infectious COVID-19 cases.

The impact of the size of the training set on the predictive abilities of neural models on the example of the Day-Ahead Market System of TGE S.A.

The main object of the research was to examine the acceptable time horizon that could be predicted by previously learned models of the Day-Ahead Market (DAM) TGE S.A. system. The article contains the results of research on the predicting ability of different ANN models of the DAM TGE S.A. The research was conducted based on data covering the operation of the Polish stock exchange in the period from 2002 to 2019 (the first half of the year). The research was carried out based on the learned ANN models of the DAM system. Data were taken for examination covering the time from 2002 to 2019 (1st half of the year) and was divided into a different period, i.e., a month, a quarter, and a half-year. , year, etc. The MSE, MAE, MAPE, and R2 were adopted as the criteria for assessing the ability of individual models to predict electricity prices. The research was carried out by successively expanding forecasting periods in a rolling manner. For example, for a half-year, prediction time intervals were increased from one week to month, two months, quarter, half-year, etc. results for a model representing a given period. A lot of interesting research results were obtained.

Ablation evolution of a new light weight silicon based thermal protection material in high temperature gas flow

High silicon oxygen/phenolic aldehyde (VSF/PR) ablative heat-proof material is widely used as resin-based heat-proof system for long-term aircraft. Understanding the generation and flow process of the surface droplets during high temperature is key to improving the thermal stability, and prior to which, a direct visualization of the surface evolution is required. Here we use optical imaging technique to in-situ and real time record the surface evolution of a flat plate subjected to thermal ablation at 1700 °C in wind tunnel. Based on the experimental results processed using image processing method and by combining the fluid theory in high temperature wind tunnel, we explained the distribution of the surface temperature and surface liquid droplets, the velocity field, and the movement of the liquid droplets on the plate surface. Results show that the distribution of the liquid droplets was correlated to the temperature distribution. A verification experiment for the merging process of two liquid droplets shows that droplet with high viscosity would generate a great friction force and cause the droplet to move forward in a rolling manner. The chasing and merging process of the liquid droplets with high viscosity is rather an entrainment process than a spontaneous merging process normally induced by surface tension. These findings help to understand the flow behavior of the liquid droplets during thermal ablation.

Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach

Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance.

Distributed stochastic model predictive control for systems with stochastic multiplicative uncertainty and chance constraints

The rapid development of technology and economy has led to the development of chemical processes, large-scale manufacturing equipment, and transportation networks, with their increasing complexity. These large systems are usually composed of many interacting and coupling subsystems. Moreover, the propagation and perturbation of uncertainty make the control design of such systems to be a thorny problem. In this study, for a complex system composed of multiple subsystems suffering from multiplicative uncertainty, not only the individual constraints of each subsystem but also the coupling constraints among them are considered. All the constraints with the probabilistic form are used to characterize the stochastic natures of uncertainty. This paper first establishes a centralized model predictive control scheme by integrating overall system dynamics and chance constraints as a whole. To deal with the chance constraint, based on the concept of multi-step probabilistic invariant set, a condition formulated by a series of linear matrix inequality is designed to guarantee the chance constraint. Stochastic stability can also be guaranteed by the virtue of nonnegative supermartingale property. In this way, instead of solving a non-convex and intractable chance-constrained optimization problem at each moment, a semidefinite programming problem is established so as to be realized online in a rolling manner. Furthermore, to reduce the computational burdens and amount of communication under the centralized framework, a distributed stochastic model predictive control based on a sequential update scheme is designed, where only one subsystem is required to update its plan by executing optimization problem at each time instant. The closed-loop stability in stochastic sense and recursive feasibility are ensured. A numerical example is employed to illustrate the efficacy and validity of the presented algorithm in this study.

Afrofuturism in Black Panther Superhero Costume as Culture Sustainability Media (Charles Sanders Pierce Semiotic Analysist)

Black Panther is a successful film with various awards and a large revenue. One factor that received attention from the global community was the hidden message contained in the film. The costuming in Black Panther was one of the most important message media. This study determines the various meanings contained in the costume using Charles Sanders Pierce’s semiotics theory, which connects representamen, object and interpretant. Pierce’s semiotics theory is classified as a communication semiotics theory, which communicates a thought with new thoughts. Semiosis in communication semiotics occurs continuously or in a rolling manner. Semiosis will be stopped if the researcher has found meaning. Communication semiotics is considered relevant to this research, because the Black Panther costume can be analyzed from various aspects, so the meaning found will be varied, unlimited and profound. This study uses a qualitative descriptive approach to the stages of determining the problem, determining the type of data, determining the method of data collection, determining analysis techniques, and making conclusions. The data obtained were analyzed using Pierce’s semiotic trichotomy table. Data analysis was also strengthened with relevant bibliography, so that contextual meaning was found. The results of semiosis in this study indicate that the Black Phanter costume represents the existence of the black race through physical form, color and symbols. The costume is a product of the afrofuturism movement, a movement that aims to campaign for African culture. The Black Panther costume represents that Africa has a rich and competitive culture. Through the Black Panther character, black people are imaged as a virtuous, civilized, advanced, intellectual, artistic and warrior-spirited nation. The image is an attempt to sensitize the public about human rights equality and tolerance between groups.
 Keywords: Black Panther, Kostum, Semiotika, Afrofuturisme.

Mass density effect on vibration of zigzag and chiral SWCNTs: A theoretical study

Carbon nanotubes fascinate new materials with astonishing mechanical, optical, and electrical properties. In fact, carbon nanotubes are kinds of rolled graphene sheets, and the rolling manner shows the basic properties of the tube, and that is actually the main reason for the extraordinary feature of the carbon nanotubes. This paper aims to presents formulation of Love’s shell theory for single-wall carbon nanotubes (SWCNTs) using Galerkin’s method. For this system, the governing equation is developed with proposed method. It is assumed that C–C and C–F support conditions are applied on the edge of carbon nanotube. Zigzag and chiral structures are considered for the vibrational analysis to investigate the effect of different mode and in-plane rigidity with end conditions. Moreover, the influence of mass density per unit lateral area on the same structure is also developed. It is also concluded that the fundamental frequencies of chiral tubes are lower than that of zigzag tubes frequencies. By increasing values of in-plane rigidity, resulting frequencies also increase and frequencies decrease on increasing mass density per unit lateral area. Though the trends of frequency values of both mass density per unit lateral area and in-plane rigidity are converse to each other. It is shown that variation in mass density has a significant effect on the vibration of SWCNTs. To generate the fundamental natural frequencies and for better accuracy and effectiveness, the computer software MATLAB is used. Tube dynamical equations for the present problem are taken in the integral form energy expressions for a carbon nanotube. The axial modal deformations are approximated by making use of the beam functions. The proposed model is continuum-model for getting the dynamic character of SWCNTs and to validate the results by earlier methods.

A classifying ubiquitous clinic recommendation approach for forming patient groups and recommending suitable clinics

Existing ubiquitous clinic recommendation systems assume that patients’ preferences are alike and thus apply the same recommendation mechanism to all mobile patients. However, this assumption may be unreasonable and must be relaxed. Accordingly, this study proposes a classifying ubiquitous recommendation approach. The classifying ubiquitous recommendation approach divides patients into multiple groups by mining their unknown preferences before recommending them suitable clinics. To tune the recommendation mechanism of each patient group, an integer nonlinear programming problem is solved in a rolling manner. When a new patient accesses the ubiquitous clinic recommendation system, the recommendation mechanisms of all groups are applied to recommend the suitable clinic to the new patient. The results of a regional experiment indicated that the classifying ubiquitous recommendation approach improved the successful recommendation rate by up to 52%. Therefore, patients’ unknown preferences are different and affect their behaviors in choosing clinics, which should be considered in grouping patients and in tailoring the clinic recommendation mechanism.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Fluctuation characteristics and rolling control for an underactuated spherical underwater exploration robot

Compared with other underwater exploration robots, Spherical underwater robot has an outstanding advantage for the underwater exploration, whose spherical shell has the excellent resiliency to protect the internal electronic components. In addition, this steering resistance is very small to move flexibly. In this paper, a type of spherical underwater robot with the pendulums and a propeller was studied on moving at the water bottom in a rolling manner. The structure and force were analyzed to understand that the hydrodynamic force’s affection on the robot’s rolling at the water bottom. A mathematical model was established with the mass parameters and speeding parameters. The virtual simulation environment was established in Adams software. Furthermore, the coupling fluctuation characteristics of the speed, swing angle and the torque were studied by the simulation and the experiment in a pool. The study proved that this robot not only can use the propeller to move in water, but also can roll at the water bottom by driving the spherical shell. Especially, the result also can be obtained that the robot can roll at water bottom stably by increasing the pendulum mass and lowering the motor speed.

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

Large Vesicle Moves in Rolling Manner by Myosin 5C along Actin Tracks

Class 5 myosins are actin-based molecular motors implicated in intracellular cargo transport. In vertebrates, myosin 5 (Myo5) consists of three isoforms, named myosin 5a, 5b, and 5c. The ability of myosin V molecules to move continuously along actin filaments (i.e. processivity) is required for efficient cargo transport in cells. This physical property has been well addressed in Myo5a and 5b, which demonstrate processive movement as a single molecule. In contrast, Myo5c shows no processivity as a single molecule, although it is found in cargo and is believed to participate in cargo transports. This raises the possibility that multiple Myo5c molecules should transport cargos. Recent studies have shown that Myo5c binds to Zymogen granule (ZGs) in pancreatic cells and transports ZGs to cell periphery. Purified ZGs was investigated the movement manner in vitro. The velocity and run-length of ZGs along actin track was measured (99 nm/sec and 1255 nm, respectively). We identified that the movement manner of ZGs move in rolling manner instead of gliding along actin tracks. Mathematical model of multiple Myo5c motors ensemble applied to understand the movement behavior vs. number of Myo5c on the vesicle.

Phenomena of Foamed Concrete under Rolling of Aircraft Wheels

Engineered Material Arresting System (EMAS) is an effective technique to reduce hazards associated with aircraft overrunning runway. In order to ascertain phenomena of the foamed concrete used for EMAS under rolling of aircraft wheel, a specially designed experimental setup was built which employed Boeing 737 aircraft wheels bearing actual vertical loads to roll through the foamed concrete. A number of experiments were conducted upon this setup. It is discovered that the wheel rolls the concrete in a pure rolling manner and crushes the concrete downwards, instead of crushing it forward, as long as the concrete is not higher than the wheel axle. The concrete is compressed into powder in-situ by the wheel and then is brought to bottom of the wheel. The powder under the wheel is loose and thus is not able to sustain wheel braking. It is also found that after being rolled by the wheel the concrete exhibits either of two states, i.e. either 'crushed through' whole thickness of the concrete or 'crushed halfway', depending on combination of strength of the concrete, thickness of the concrete, vertical load the wheel carries, tire dimension and tire pressure. A new EMAS design concept is developed that if an EMAS design results in the 'crushed through' state for the main gears while the 'crushed halfway' state for the nose gear, the arresting bed would be optimal to accommodate the large difference in strength between the nose gear and the main gear of an aircraft.

Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland

A stochastic mixed integer linear optimization scheduling model minimizing system operation costs and treating load and wind power production as stochastic inputs is presented. The schedules are updated in a rolling manner as more up-to-date information becomes available. This is a fundamental change relative to day-ahead unit commitment approaches. The need for reserves dependent on forecast horizon and share of wind power has been estimated with a statistical model combining load and wind power forecast errors with scenarios of forced outages. The model is used to study operational impacts of future high wind penetrations for the island of Ireland. Results show that at least 6000 MW of wind (34% of energy demand) can be integrated into the island of Ireland without significant curtailment and reliability problems.

Comparison of bonding procedures for 3D-structured polyimide films on silicon substrates applied to ink-jet cartridges

Established bubble-jet printheads consist of an assembly of three layers, which are a CMOS substrate, a channel layer and a nozzle layer. The aim of the presented work was to simplify the setup of bubble-jet printheads by means of integrating channel and nozzle structures in one single three-dimensional laser-structured polyimide nozzle plate. Different bonding techniques for an assembly onto 1/3 inch standard CMOS printhead substrates are validated. The main challenges are a variety of bonding materials, an alignment accuracy of <5 μm and the prevention of blocking the 20 μm deep fluidic channels in the polyimide which have minimal lateral dimensions of 10 μm and a minimal pitch of 15 μm. In total, three bonding techniques, with and without additional adhesion layers, were developed and evaluated. One method applies a 4 μm thick layer of Epotec 353ND (Polytec), a standard two-component epoxy, in a specifically adapted rolling manner onto the film that is subsequently aligned to the silicon chip using a flip-chip-bonder. Screen-printing and dispensing processes of adhesives were investigated but failed due to insufficient structural resolution. The second method uses photolithographic processes to produce structured adhesion preforms in SU-8 resist. With a layer thickness of 3 μm and an adapted curing schedule, promising results concerning resolution and contour accuracy were obtained. Thirdly, bonding without additional adhesion layers was achieved in a micro-sealing process that takes advantage of the highly defined thermoplastic softening of polyimide KJ (DuPont). The different processes were compared regarding to yield, printing behavior of the assembled printheads and applicability to high volume productions. The hereby developed adhesion technology was applied on the assembly of large one inch printheads for special applications.

Real-time multi-functional optical coherence tomography

We demonstrate real-time acquisition, processing, and display of tissue structure, birefringence, and blood flow in a multi-functional optical coherence tomography (MF-OCT) system. This is accomplished by efficient data processing of the phase-resolved inteference patterns without dedicated hardware or extensive modification to the high-speed fiber-based OCT system. The system acquires images of 2048 depth scans per second, covering an area of 5 mm in width x 1.2 mm in depth with real-time display updating images in a rolling manner 32 times each second. We present a video of the system display as images from the proximal nail fold of a human volunteer are taken.