Time Grid Research Articles

Mean Energy Flux, Associated Derived Height‐Integrated Conductances, and Field‐Aligned Current Magnitudes Evolve Differently During a Substorm

AbstractWe examine the average evolution of precipitation‐induced height‐integrated conductances, along with field‐aligned currents (FACs), in the nightside sector of the polar cap over the course of a substorm. Conductances are estimated from the average energy flux and mean energies derived from auroral emission data. Data are binned using a superposed epoch analysis on a normalized time grid based on the time between onset and recovery phase (δt) of each contributing substorm. We also examine conductances using a fixed time binning of width 0.25 hr. We split the data set by magnetic latitude of onset. We find that the highest conductances are observed for substorms with onsets that occur between 63 and 65° magnetic latitude, peaking at around 11 mho (Hall) and 4.8 mho (Pedersen). Substorms with onsets at higher magnetic latitudes show lower conductances and less variability. Changes in conductance over the course of a substorm appear primarily driven by changes (about 40% at onset) in the average energy flux, rather than the average energy of the precipitation. Average energies increase after onset slower than energy flux, later these energies decrease slowly for the lowest latitude onsets. No clear expansion of the main region 1 and region 2 FACs is observed. However, we do see an ordering of the current magnitudes with magnetic latitude of onset, particularly for region 1 downwards FAC in the morning sector. Peak current magnitudes occur slightly after or before the start of the recovery phase for the normalized and fixed‐time grids.

Research of the Solutions Proximity of Linearized and Nonlinear Problems of the Biogeochemical Process Dynamics in Coastal Systems

The article considers a non-stationary three-dimensional spatial mathematical model of biological kinetics and geochemical processes with nonlinear coefficients and source functions. Often, the step of analytical study in models of this kind is skipped. The purpose of this work is to fill this gap, which will allow for the application of numerical modeling methods to a model of biogeochemical cycles and a computational experiment that adequately reflects reality. For this model, an initial-boundary value problem is posed and its linearization is carried out; for all the desired functions, their final spatial distributions for the previous time step are used. As a result, a chain of initial-boundary value problems is obtained, connected by initial–final data at each step of the time grid. To obtain inequalities that guarantee the convergence of solutions of a chain of linearized problems to the solution of the original nonlinear problems, the energy method, Gauss’s theorem, Green’s formula, and Poincaré’s inequality are used. The scientific novelty of this work lies in the proof of the convergence of solutions of a chain of linearized problems to the solution of the original nonlinear problems in the norm of the Hilbert space L2 as the time step τ tends to zero at the rate O(τ).

A deep spatiotemporal approach in maritime accident prediction: A case study of the territorial sea of South Korea

Predicting the risk of maritime accidents is crucial for improving traffic surveillance and marine safety. The availability of data sources and development of machine learning and deep learning methodologies can improve operational risk prediction. Similar to larger vessels, numerous ocean accidents are caused by small- and medium-sized vessels owing to poor conditions and defective equipment. This study aims at investigating the application of deep learning in both short- and long-term predictions of different types of accident risks associated with small vessels by considering multiple influencing factors. Herein, several big data sources that contain data collected from the territorial sea of South Korea, including ocean accidents, ocean depth, weather data, and small vessel trajectories, are utilized. Four machine learning and six deep learning algorithms are implemented and compared in nine scenarios with three different grid sizes using daily, weekly, and monthly models. The results reveal that although the performance of the proposed deep spatiotemporal ocean accident prediction (DSTOAP) model varies according to grid sizes and time intervals, its accuracy (more than 78%) makes it reliable for predicting accidents. Furthermore, although all types of accidents are captured with high accuracy, more than 84% of collision accidents can be predicted accurately. For practical applications, the results of this study can guide ocean accident management and safety planners in choosing appropriate methods for different time schedules and grid sizes, according to the range of coverage of the patrol ship.

Longitudinal individual predictions from irregular repeated measurements data

Intensive longitudinal data can be used to explore important associations and patterns between various types of inputs and outcomes. Nonlinear relations and irregular measurement occasions can pose problems to develop an accurate model for these kinds of data. This paper focuses on the development, fitting and evaluation of a prediction model with irregular intensive longitudinal data. A three-step process for developing a prediction tool for (daily) monitoring and prediction is outlined and illustrated for intensive weight measurements in piglets. Step 1 addresses a nonlinear relation in the data by developing and applying a normalizing transformation. Step 2 addresses the intermittent nature of the time points by aligning the measurement times to a common time grid with a broken-stick model. Step 3 addresses the prediction problem by selecting and evaluating inputs and covariates in the model to obtain accurate predictions. The final model predicts future outcomes accurately, while allowing for nonlinearities between input and output and for different measurement histories of individuals. The methodology described can be used to develop a tool to deal with intensive irregular longitudinal data that uses the available information in an optimal way. The resulting tool demonstrated to perform well for piglet weight prediction and can be adapted to many different applications.

A novel fault monitoring method based on impedance estimation of power line communication equipment

Due to the close relationship between power grid fault and impedance, there is a significant defect in the use of power line communication (PLC) equipment to monitor and locate power grid faults, which is the lack of real-time impedance information. Therefore, this study proposes a new fault monitoring method based on impedance estimation of power line communication equipment. The channel frequency response (CFR) obtained from the PLC receiver is used to estimate the high-frequency input impedance to monitor and locate the ground fault of the power grid in real time. Firstly, based on the multi-conductor transmission line theory, bottom-up channel modeling method and impedance estimation technology, the basic principles of fault monitoring and location methods are clarified. Then, modeling and analysis of cables in different situations are carried out, and the characteristics of the factors affecting the impedance in CFR are extracted by variational modal decomposition (VMD), and the estimation results of impedance are obtained by inversion analysis. Based on this, the impedance change and machine learning algorithm are used to track and identify the abnormal state of the power line to achieve high-sensitivity positioning of the cable fault. The simulation results show that the method has a good identification effect on high and low impedance cable fault, and has a better effect on low resistance fault monitoring. The fault location error is less than 2.13%, and has a good positioning accuracy.

A Minimum-Control-Trajectory-Deviation Time Grid Reconstruction Strategy for Co-Design Approach

Abstract Optimizing dynamic engineering systems (DESs) is quite challenging due to the increasing pursuit of automation and intelligence in modern industry. However, most of the existing studies generally only focus on plant variables or control variables of DESs, which may fail to explore optimal solutions. In this paper, a novel minimum-control-trajectory-deviation (MCTD) time grid reconstruction strategy is presented for the co-design approach. Three co-design approaches, namely simultaneous, nested, and direct transcription quadratic programming (DTQP) are compared using the MCTD time grid reconstruction strategy. Considering a number of design variables are time-varying in practical dynamic systems, three co-design methods use a special class of numerical analysis methods known as direct transcription (DT) that implies a “discretize-then-optimize” process. Motivated by the inefficiency of the traditional uniform discrete strategy, an MCTD time grid reconstruction strategy is proposed. Combining the presented time grid reconstruction strategy, simultaneous, nested, and DTQP methods are implemented for three test problems. The MCTD time grid reconstruction strategy is verified through a mathematical example, the Van der Pol oscillator, and a machine tool case. All cases have proved the superiority of presented strategy in running cost and solution accuracy.

Prediction and mitigation of nonlocal cascading failures using graph neural networks.

Cascading failures in electrical power grids, comprising nodes and links, propagate nonlocally. After a local disturbance, successive resultant can be distant from the source. Since avalanche failures can propagate unexpectedly, care must be taken when formulating a mitigation strategy. Herein, we propose a strategy for mitigating such cascading failures. First, to characterize the impact of each node on the avalanche dynamics, we propose a novel measure, that of Avalanche Centrality (AC). Then, based on the ACs, nodes potentially needing reinforcement are identified and selected for mitigation. Compared with heuristic measures, AC has proven to be efficient at reducing avalanche size; however, due to nonlocal propagation, calculating ACs can be computationally burdensome. To resolve this problem, we use a graph neural network (GNN). We begin by training a GNN using a large number of small networks; then, once trained, the GNN can predict ACs efficiently in large networks and real-world topological power grids in manageable computational time. Thus, under our strategy, mitigation in large networks is achieved by reinforcing nodes with large ACs. The framework developed in this study can be implemented in other complex processes that require longer computational time to simulate large networks.

Polar particle flux distribution and its spatial extent

Context: The main challenge in atmospheric ionisation modelling is that sparse measurements are used to derive a global precipitation pattern. Typically this requires intense interpolation or scaling of long-term average maps. In some regions however, the particle flux might be similar and a combination of these regions would not limit the results even though it would dramatically improve the spatial and temporal data coverage. Aims: The paper intends to statistically analyse the particle flux distribution close to the geomagnetic poles labelled as Polar Particle Flux Distribution (PPFD) and identify similar distributions in neighbouring bins. Those bins are grouped and the size of the PPFD area is estimated. The benefit is that single measurements within the PPFD area should be able to represent the particle flux for the whole area at a given time. Methods: We use spatially binned energetic particle flux distributions measured by POES and Metop spacecraft during 2001–2018 to identify a Kp-dependent area with a similar flux distribution as the one found close to the geomagnetic poles (|magn.lat| > 86°). First, the particle flux is mapped on a magnetic local time (MLT) vs. magnetic latitude grid. In the second step, the gridded data is split up according to Kp levels (forming the final bins). Third, the particle flux in every bin has been recalculated in order to replace zero-count rates with rates based on longer measurement periods which results in a more realistic low flux end of the particle distribution. Then the binned flux distributions are compared to the PPFD. A “Δ-test” indicates the similarity. A threshold for the Δ-test is defined using the standard deviation of Δ-test values inside the (|magn.lat| > 86°) area. Bins that meet the threshold are attributed as PPFD area. Results: PPFDs and the corresponding PPFD areas have been determined for all investigated particle channels, covering an energy range of 154 eV–300 keV for electrons and 154 eV–2.5 MeV for protons. Concerning low energy channels a gradual flux increase with rising Kp has been identified. High energy channels show a combination of background population and solar particle event (SPE) population that adds up with increasing Kp. The size of the PPFD area depends on particle species, energy and geomagnetic disturbance, as well as MLT. The main findings are: a) There are small but characteristic hemispheric differences. b) Only above a certain energy threshold do the PPFD areas increase with particle energy. c) A clear enlargement with rising Kp is identified – with exceptions for very low Kp. d) The centre of the PPFD area is shifted towards midnight and moves with Kp. Asymmetries of the boundaries could be explained by auroral intensity. e) For low-energy particles the main restriction of the PPFD area seems to be the auroral precipitation.

Modification of heat storage system involving Trombe wall in existence of paraffin enhanced with nanoparticles

Unsteady flow within a solar system with the Trombe wall technique has been scrutinized in current paper. To enhance the productivity of the classic Trombe wall, the paraffin layer has been combined with other layers (insulation, absorber, and concrete layers). The type of paraffin is RT18, and the conditions of the surrounding in the simulation have been selected according to a sunny day in autumn. Adding alumina nanoparticles into RT18 makes the conduction mode of paraffin enhance. The regime for the air zone is turbulent, and circulation via buoyancy force makes the domain warmer, therefore, the k-ε RNG technique was utilized for this region. The heat loss from glass due to radiation and convection has been involved in modeling. A verification test has been done not only in view of modeling of classic Trombe wall but also for modeling of melting of paraffin in ventilation application. The time step and grid size were optimized to reduce the cost of computing. A comparison of results for 3D simulation and 2D modeling indicates that there is no sensible change, therefore, considering the 2D model is logical. Among various scrutinized positions of the PCM layer, the first case (PCM near the absorber) has a greater liquid fraction. Moreover, if the PCM layer has been utilized in the middle, the minimum heat loss from the walls can be obtained.

Iterative Methods with Nonconforming Time Grids for Nonlinear Flow Problems in Porous Media

Iterative Methods with Nonconforming Time Grids for Nonlinear Flow Problems in Porous Media

Zero fluctuation: Electric-fluctuation-elimination heat pump system with water storage tank based on time-of-use tax

Due to the difference of heating load between day and night, the power load of heat pump heating system varies greatly day and night. Furthermore, the rapid growth of heat pump heating systems for meeting the requirement of low carbon heating will result in more and more serious electricity fluctuation, which will threaten the stability of the power grid. In order to reduce this power fluctuation, a single-heat-pump heating system with water tank thermal storage (SHPHSTS) is constructed. Based on the thermodynamic calculation of the heat pump refrigeration cycle, using step search optimization method, the operation parameters of the constructed heating system are optimized with the goal of constant power consumption from power grid within a day. Then, the corresponding electricity consumption and power fluctuation, after adopting the proposed operation optimization strategy in heating season are analyzed. At last, function of power fluctuation (FL) and degree of fluctuation (DF and DF’) are proposed to quantitatively evaluate the effect of the power fluctuation reduction in heating season, and the correlation between the electricity consumption of the heating system and power grid load is analyzed. The results show that due to the thermal storage of HWST and the optimizing operation strategy, constant power consumption of heating system could be realized within a day. On the typical heating day, the power consumption in traditional peak time reduces by 60.8%, and the electric bill is 22% lower compared with that of the traditional heat pump heating system. Furthermore, the power consumption fluctuation during the whole heating season reduces substantially, the power fluctuation function reduces from 460% to 150% by adopting the proposed operation optimization strategy. The correlation analysis indicates that the busy degree of power grid in peak time could be relieved by thermal storage and the optimizing operation strategy. Consequently, the peak of power consumption of heat pump heating system could be decoupled from the peak of power grid.

Intelligent retrieval method for power grid operation data based on improved SimHash and multi-attribute decision making

IN the trend of energy revolution, power data becomes one of the key elements of the power grid. And an advance power system with "electric power + computing power" as the core has become an inevitable choice. However, the traditional search approach based on directory query is commonly used for power grid operation data in domestic and international. The approach fails to effectively meet the user's need for fast, accurate and personalized retrieval of useful information from the vast amount of power grid data. It seriously affects the real-time availability of data and the efficiency of business-critical analytical decisions. For this reason, an intelligent retrieval approach for power grid operation data based on improved SimHash and multi-attribute decision making is proposed in this paper. This method elaborates the properties of SimHash and multi-attribute decision making algorithms. And an intelligent parallel retrieval algorithm MR-ST based on MapReduce model is designed. Finally, real time grid operation data from multiple sources are analyzed on the cloud platform for example. The experimental results show the effectiveness and precision of the method. Compared with traditional methods, the search accuracy rate, search completion rate and search time are significantly improved. Experiments show that the method can be applied to intelligent retrieval of power grid operation data.

Digital twin-oriented multi-physics field simulation analysis of high-voltage SF6 circuit breakers

Circuit breakers are important components for removing short circuits in power systems and ensuring safe operation. The operation of circuit breakers may not be predicted and thus cause accidents, so there is an urgent need to innovate means to assess the operation status of power equipment and monitor the operation status of power grids in full real time, to improve the monitoring capability of major key equipment, and to study key technologies for the digital twin of circuit breakers to support the safe and stable operation of transmission networks and the digital transformation of energy systems. This paper establishes the multi-physics field coupling calculation model of the circuit breaker opening process, completes the simulation calculation of the circuit breaker opening process, gets the distribution and change trend of parameters such as electric field, pressure, temperature, density, and airflow speed inside the interrupting chamber, and analyzes the flow characteristics inside the interrupting chamber in different opening processes. We select the characteristic parameters and provide data support for the establishment of fault diagnosis based on the digital twin of the circuit breaker.

Numerical simulation of unsteady hydrodynamic performance of CRP

Strong hydrodynamic interference between forward and aft contra-rotating propellers (CRP) can result in load fluctuations, local blade vibrations, and noise. This study adopts a Reynolds-averaged Navier–Stokes solver (RANS) and a shear stress transport (SST) k−ω turbulence model to investigate the unsteady hydrodynamic performance of CRPs during uniform flow. The influences of time step and grid scale on the numerical simulation are discussed first. The numerical results of the open-water hydrodynamic performances of the CRPs agree well with experimental data. The fluctuation patterns of the hydrodynamic loads (i.e., force and torque) and propulsion efficiency of CRPs having different advance coefficients induced by the mutual hydrodynamic interference between the forward and aft propeller are investigated. A series of probes that rotate with the propellers are set upon the surface of the blades to analyze the unsteady local pressures under the hydrodynamic interference between the forward and aft propellers.

Asynchronous Truncated Multigrid-Reduction-in-Time

In this paper, we present the new “asynchronous truncated multigrid-reduction-in-time” (AT-MGRIT) algorithm for introducing time parallelism to the solution of discretized time-dependent problems. The new algorithm is based on the multigrid-reduction-in-time (MGRIT) approach, which, in certain settings, is equivalent to another common multilevel parallel-in-time method, Parareal. In contrast to Parareal and MGRIT that both consider a global temporal grid over the entire time interval on the coarsest level, the AT-MGRIT algorithm uses truncated local time grids on the coarsest level, each grid covering certain temporal subintervals. These local grids can be solved completely in an independent way from each other, which reduces the sequential part of the algorithm and, thus, increases parallelism in the method. Here, we study the effect of using truncated local coarse grids on the convergence of the algorithm, both theoretically and numerically, and show, using challenging nonlinear problems, that the new algorithm consistently outperforms classical Parareal/MGRIT in terms of time to solution.

Design and implementation of user oriented power ubiquitous Internet of things access

The State Grid Corporation proposed at the 2019 ubiquitous power Internet of things promotion meeting that it should make full use of big data technology to improve the digital and intelligent operation level of power grid, deepen the construction of digital service, fully promote the information interaction of power customers and the docking of supply and demand, quickly respond to the needs of power customers, and better serve the revitalization and development of local economy. The ubiquitous power Internet of things just meets the needs of the power industry. Its core is to build an intelligent network system with the ability of intelligent judgment and adaptive adjustment, which can integrate multiple energy into the network and distributed management. It can monitor and collect the power consumption information of the power grid and users in real time, and use the most economical and secure power transmission and distribution mode to transmit power to the end users, so as to achieve the goal The optimal allocation and utilization of electric energy, improve the reliability of power grid operation and energy efficiency. Based on a variety of on-site intelligent terminal devices and data processing platforms with edge computing capabilities, this paper forms solutions around typical application scenarios such as household energy consumption, and establishes data access nerve terminals for power ubiquitous Internet of things applications, so as to promote the intelligent interaction of source network load and storage and the improvement of social energy efficiency. This paper uses the Internet of things technology, a community ubiquitous power Internet of things ecosystem composed of hardware (intelligent appliances, intelligent hardware, security control equipment, furniture, etc.), software system and cloud computing platform, which brings real estate developers, property companies, households / merchants into a platform to realize the functions of remote control equipment, interconnection between equipment, self-learning of equipment, etc And through the collection and analysis of user behavior data, provide personalized life services for users, make home life safe, comfortable, energy-saving, efficient and convenient. The power ubiquitous Internet of things is the development and upgrading based on the smart grid in recent years. It intelligentizes the load storage facilities of the source network through the Internet of things technology, and has the functions of energy consumption monitoring, fault self diagnosis, and active status reporting, which makes the participants of the power ubiquitous Internet of things obtain more convenient and reliable services.

Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

Dynamic programming (DP) is frequently used to obtain the optimal solution to the hybrid electric vehicle (HEV) energy management. The trade-off between the accuracy and the computational effort is the biggest problem for the DP method. The closed-form solution to the DP is proposed to solve this problem. Firstly, the affine linear model of the engine fuel rate is obtained based on engine test data. The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery. Then, the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split. The results show that this method can greatly reduce the computing burden by 93% while ensuring near-optimal fuel economy compared with the conventional DP method.

Bi-level energy management model for the smart grid considering customer behavior in the wireless sensor network platform

Today, smart grids (SG) and its relationship to wireless sensor network (WSN) provides a suitable platform for establishing the two-way communication of the energy management system and network users. This issue can accurately plan the smart grid in real time with minimum cost. This paper proposes a bi-level energy management model for smart grids in uncertainty and demand-side management (DSM) in the Internet of things (IoT) platform based on WSN. In the upper level, for accurate planning in real-time and relationship with energy management system, the customers are modelled in wireless sensor network platform and clustered by fuzzy C-Means algorithm. Then, the energy consumption of the network sensors in the IoT platform is optimized by the genetic algorithm (GA). Moreover, the customer behavior optimization was performed utilizing the price-based demand response (PBDR) model and transferring demand load from the peak to the valley load times. The objective functions in the lower layer minimized the emission pollution, operation costs and energy not supplied (ENS) index using the multi-objective Moth–Flame optimization (MOMFO) algorithm. The Monte Carlo technique is used for modeling renewable energy sources (RES)'s uncertain output. Finally, the suggested approach is confirmed through the analysis of a case study. The proposed model's capability in planning a uniform smart grid operation is demonstrated by simulation study and comparison with multi-objective particle swarm optimization (MOPSO), epsilon-constraint and benders techniques.

Parallel computation of Inverse Compton Scattering radiation spectra based on Liénard-Wiechert potentials

Inverse Compton Scattering (ICS) has gained much attention recently because of its promise for the development of table-top-size X-ray light sources. Precise and fast simulation is an indispensable tool for predicting the radiation property of a given machine design and to optimize its parameters. Instead of the conventional approach to compute radiation spectra which directly evaluates the discretized Fourier integral of the Liénard-Wiechert field given analytically (referred to as the frequency-domain method), this article focuses on an approach where the field is recorded along the observer time on a uniform time grid which is then used to compute the radiation spectra after completion of the simulation, referred to as the time-domain method. Besides the derivation and implementation details of the proposed method, we analyze possible parallelization schemes and compare the parallel performance of the proposed time-domain method with the frequency-domain method. We will characterize scenarios/conditions under which one method is expected to outperform the other.

MGRIT-Based Multi-Level Parallel-in-Time Electromagnetic Transient Simulation

In this paper, we present an approach for multi-level parallel-in-time (PinT) electromagnetic transient (EMT) simulation. We evaluate the approach in the context of power electronics system-level simulation. While PinT approaches to power electronics simulations based on two-level algorithms have been thoroughly explored in the past, multi-level PinT approaches have not yet been investigated. We use the multigrid-reduction-in-time (MGRIT) method to parallelize a dedicated EMT simulation tool which is capable of switching between different converter models as it operates. The presented approach yields a time-parallel speed-up of up to 10 times compared to the sequential-in-time implementation. We also show that special care has to be taken to synchronize the time grids with the electronic components’ switching periods, indicating that further research into the usage of different models from adequate model hierarchies is necessary.