Multiple Grid Research Articles

베지어 곡선을 활용한 육각 그리드의 그림자 생성 방법

The hexagonal grid structure has been studied for processing and representing spatial information data in Geographic Information Systems. Visualization using a hexagonal grid has high visibility compared to other grid representation methods. However, it is difficult to effectively convey quantitative data and differences between grids depending on the geospatial data represented. In this paper, we propose a method to visually emphasize the hexagonal grid by generating shadow on the outside of the hexagonal grid. To do so, we offset the outer line segments of the hexagonal grid to be emphasized and generate a Bézier curve based on that information to determine the final shadow shape. We also apply variable transparency toward the edges of the shadow because the shadow gradually fades away from the hexagonal grid. We have shown that the proposed method can effectively generate shadow areas given not only a single hexagonal grid but also multiple hexagonal grids and can generate various shadow shapes based on user interface inputs. We apply the proposed method to Yongsan-gu, one of the districts of Seoul, and show the results of visually emphasizing it after generating shadow using the proposed method.

Hierarchical 3D reconstruction algorithm of core image based on detailed and structural dictionaries

The microstructure of porous media is of great significance to the study and analysis of their physical properties and mechanical properties. At present, the three-dimensional reconstruction method is widely used to obtain digital models of porous media microstructures that can be used to reconstruct their three-dimensional structures from two-dimensional training images. The super-dimensional algorithm, a learning-based method, makes full use of a priori three-dimensional structural information, and its reconstruction effect is better than that of the aforementioned traditional method. However, most current algorithms are based on 128- or 256-size training image reconstruction, which is still far from the actual application of the image size. To make algorithms more suitable to the actual application, we herein propose a hierarchical super-dimensional reconstruction method based on the idea of a multiple grid to complete the reconstruction of large-size images, such as the three-dimensional reconstruction of 512- or even 1000-size images. Using this method, we designed a template of the detailed information dictionary and developed a corresponding matching algorithm for a hierarchical reconstruction process. To further improve the efficiency of reconstruction, dictionary element porosity characteristics were used for classification. Finally, to verify the effectiveness of the proposed method, four groups of sandstone image reconstruction experiments were carried out, and the reference images with different sizes and porosity were reconstructed several times. Furthermore, to determine the accuracy of the reconstruction results, we compared the real structure with the visual and statistical indicators, and pore and throat parameters.

Evaluation of Equivalent Dynamic Active Distribution Network Models with Individual and Aggregated Consideration of Grid Forming Converters

Detailed active distribution network modeling in the context of stability studies of future power systems can be avoided by applying complexity‐reduced equivalent dynamic models. Previous work developed an approach to derive such equivalent dynamic network models of systems dominated by grid following and grid forming converters. Nevertheless, this approach lacks an investigation of the aggregation of multiple grid forming converters to one equivalent component. Also, the approach needs to be evaluated for a closed ring topology to substantiate its validity independent of the detailed network topology. This work applies and validates the developed approach on active distribution networks in the context of three scenarios. The detailed and equivalent network models are simulated for three events and the results are used to compare the models with respect to accuracy and complexity. The derived equivalent dynamic network models with an individual representation of grid forming converters reproduce the dynamic behavior of the corresponding detailed networks very well. The aggregation of grid forming converters results in an adequate reproduction of the detailed network's dynamic behavior under the constraint of aggregating only neighboring grid forming converters of one branch. This allows for the consideration of the detailed network's topology in the corresponding equivalent dynamic model.

A target-free video structural motion estimation method based on multi-path optimization

The vibration data are quite important for structural health monitoring (SHM). This paper proposed a novel method, to adaptively estimate video motions of the structure in subpixel accuracy, without attaching any targets. The proposed method includes three steps. In the first step, to remove outliers and simultaneously preserve feature points, the Gaussian range kernel is used along with the Gaussian spatial kernel, and calculated by the polynomial fitting and recursive integrals computing. In the second step, to calculate video pixel motions varied with spatial coordinates in the region of interest (ROI) for testing, the ROI is divided into multiple grid cells. Motions in each grid cell are modeled as local spatially-variant homography matrices, and their spatial consistency are enhanced by a shape-preserving constraint. The third step is to enhance both spatial and temporal correlations of the calculated homography matrices, achieved by the data term and the smoothness term in both space and time domains. The superiority of the proposed method over traditional methods was validated in several case studies for analyzing structural motions. Among the comparisons, the proposed method can produce image denoising, camera motions, structural motions, and structural modal information in subpixel accuracy, and with the best accuracy.

Cooperative control of time-delay wind power grid-connected unit under actuator saturation based on Hamiltonian method

In large-scale wind power generation, wide area control is often employed to overcome power oscillation, but this can result in the appearance of time-delayed voltage, which is transmitted to the wind power grid-connected system as an input signal. Additionally, ensuring the security of multi-grid parallel connection requires limiting the amplitude of the input signal. To address these challenges, this paper investigates the cooperative control problem for time-delayed wind power grid-connected unit under actuator saturation constraints. A novel control strategy that combines passive control with synchronous control is proposed. The system is transformed into a Hamiltonian structure, and a passive controller is designed using the principle of interconnection and damping assignment passivity-based (IDA-PB) to ensure stability. Based on this, an output feedback synchronous controller is proposed to account for saturation when multiple grids are connected in parallel. Stability criteria are provided to ensure the asymptotic stability of each closed-loop system and synchronous stabilization of all systems. Finally, simulation results verify the effectiveness of the proposed control strategy.

A priori and a posteriori analysis of flamelet modeling for large-eddy simulations of a non-adiabatic backward-facing step

A lean premixed ethylene–air flame in a backstep configuration is simulated on multiple grids using both direct numerical simulations (DNS) with reduced order kinetic mechanism and large eddy simulations (LES) with flamelet-based thermochemistry. The configuration includes preheated reactants and a recirculation zone that provides radicals and high temperature gases to stabilize the flame. Heat losses are present due to the proximity of cooled walls. The reacting flow obtained from DNS at different resolutions is first analyzed to investigate the property of heat transfer within the recirculation region. LES based on adiabatic flamelets with a correction of the heat capacity is then tested, and its ability to account for heat losses is compared to results obtained using a three-dimensional non-adiabatic flamelet approach. Mean fields and subgrid properties are compared to those obtained from DNS to assess the capability of the LES models. The results show that the non-adiabatic flamelet approach can predict recirculation region and temperature fields with good accuracy. The model with heat capacity correction is able to effectively correct the heat capacity behavior as observed by a priori comparisons. However, in the a posteriori context, it is observed to overestimate the temperature field, although the correct size of the recirculation region is predicted. The combined a priori and a posteriori analyses on the same configuration and at different mesh resolutions allow for a precise separation of modeling effects due to heat transfer at the wall and combustion closure, thus providing indications on the LES performance in the context of flamelets.

Power Flow Balancing With Decentralized Graph Neural Networks

We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.

Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031.

Two-stage robust optimal operation of AC/DC distribution networks with power electronic transformers

Power electronic transformers (PET) are a new type of power electronic equipment with a multi-port flexible dispatch function, which can play the role of a power hub in a system composed of multiple AC-DC hybrid distribution grids for interactive sharing of power in multiple regions. In this study, a two-stage robust optimization operation model of a hybrid AC-DC distribution network with PET is proposed based on PET power transmission and transformation characteristics. The stochastic uncertainty of the distributed renewable energy output in the AC-DC grid is handled by a two-stage robust optimization method to determine the minimum total system operation cost under the worst case of distributed renewable energy output. Finally, a constrained column generation algorithm is used to solve the two-stage robust optimization model in the min-max-min form and verifies the validity of the model in this study.

Human trafficking interdiction with decision dependent success

This paper presents a bi-level network interdiction model to increase the effectiveness of attempting to disrupt a human trafficking network under a resource constrained environment. To model the behavior of the trafficker, we present a new interpretation of the traditional maximum flow network problem in which the arc capacity parameter serves as a proxy for the trafficker’s desirability to travel along segments of the network. The objective for the anti-human trafficking stakeholder is to invest resources in detection and intervention efforts throughout the network in a manner that minimizes the trafficker’s expected maximum desirability of operating on the network. Interdictions are binary, and their effects are stochastic (i.e., there is a positive probability that a disruption attempt is unsuccessful). We present a multi-stage version of the model, which incorporates the effect of interdictions becoming more or less successful over time. Using a genetic algorithm that uses a pseudo-utility ratio for the repair operation, we solve multiple problem instances for a case study of the road network in the Eastern Development Region of Nepal and multiple grid networks. We then discuss observations regarding the impact of probabilistic interdiction success and the implications it has for optimal policies to disrupt a human trafficking network with limited resources.

AMI Data-Driven Strategy for Hierarchical Estimation of Distribution Line Impedances

Monitoring, operation, and protection of distribution power grids fundamentally rely on the accurate estimation of line impedances. However, line impedance estimation is challenging due to the difficulties in modeling the dependence on line temperature and aging. This paper proposes a new data-driven strategy to estimate low-voltage (LV) and medium-voltage (MV) line impedances using an advanced metering infrastructure (AMI). In the proposed strategy, two-level optimization problems are formulated using generalized equations for voltage drops along LV and MV lines and then extended based on AMI data collected over time. Hierarchical estimation is achieved using the local and global references to the LV and MV root buses, respectively, enabling parallel estimation for individual LV grids and thus reduced computation time. Reinforcement learning is also integrated to compensate for possible measurement errors in the AMI data, ensuring robust estimation of LV and MV line impedances. The proposed strategy is tested on a three-phase unbalanced MV grid with multiple single-phase LV grids under various conditions characterized by measurement samples and errors. The results of case studies and sensitivity analyses confirm that the proposed strategy improves the accuracy and robustness of line impedance estimation at both LV and MV levels.

Photon flow: A three-dimensional expression of the light field using volume photon mapping

This paper proposes a new method to depict the light field based on the volume photon mapping algorithm. In the context of the light field simulation, a participating medium serves to deposit the photons, but does not disturb their propagation. The photons are therefore neither scattered nor absorbed in order to preserve their energy and trajectory within the environment, thus providing an unbiased luminance distribution. A visualisation of the photon distribution enables an intuitive interpretation of the light propagation that helps designers to understand the basic light field in the space. In addition to visualisation, the magnitude of the simulated physical light field can be numerically evaluated from the volume photon map distribution using, for example, cubic and scalar illuminance. This can further inform the designer on the light density distribution in the space, since the latter directly correlates with the density of the photons, and therefore the scalar illuminance. The accuracy of the proposed method was ascertained by comparing it with the original RADIANCE. Furthermore, its advantage in visualisation was demonstrated using a complex case study involving strong indirect lighting, reinforced by a comparison of the simulation and measurement in the actual space. In addition, photon mapping was found to evaluate illuminance in multiple grid points much faster than RADIANCE Classic, notably due to the complex ambient lighting from specular reflections. The implementation of the specialised volume photon mapping software is now part of the RADIANCE software and is available as a lighting research tool for the community.

Spatial–Temporal Data Imputation Model of Traffic Passenger Flow Based on Grid Division

Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing.

Fully automated multi-grid cryoEM screening using Smart Leginon.

Single-particle cryo-electron microscopy (cryoEM) is a swiftly growing method for understanding protein structure. With increasing demand for high-throughput, high-resolution cryoEM services comes greater demand for rapid and automated cryoEM grid and sample screening. During screening, optimal grids and sample conditions are identified for subsequent high-resolution data collection. Screening is a major bottleneck for new cryoEM projects because grids must be optimized for several factors, including grid type, grid hole size, sample concentration, buffer conditions, ice thickness and particle behavior. Even for mature projects, multiple grids are commonly screened to select a subset for high-resolution data collection. Here, machine learning and novel purpose-built image-processing and microscope-handling algorithms are incorporated into the automated data-collection software Leginon, to provide an open-source solution for fully automated high-throughput grid screening. This new version, broadly called Smart Leginon, emulates the actions of an operator in identifying areas on the grid to explore as potentially useful for data collection. Smart Leginon Autoscreen sequentially loads and examines grids from an automated specimen-exchange system to provide completely unattended grid screening across a set of grids. Comparisons between a multi-grid autoscreen session and conventional manual screening by 5 expert microscope operators are presented. On average, Autoscreen reduces operator time from ∼6 h to <10 min and provides a percentage of suitable images for evaluation comparable to the best operator. The ability of Smart Leginon to target holes that are particularly difficult to identify is analyzed. Finally, the utility of Smart Leginon is illustrated with three real-world multi-grid user screening/collection sessions, demonstrating the efficiency and flexibility of the software package. The fully automated functionality of Smart Leginon significantly reduces the burden on operator screening time, improves the throughput of screening and recovers idle microscope time, thereby improving availability of cryoEM services.

Efficiency Optimization via Mission Profile-Based Power Routing by Design of Hybrid Grid Connecting Converter Architecture

Hybrid grids with low voltage direct current (LVdc) distribution are an attractive opportunity for the integration of electric vehicle charging stations and photovoltaic power plants. With dc connectivity reduced power losses are possible, however, multiple conversion stages can impair an efficient operation. A solution can be the use of an “interconnected” architecture that allows the integration of multiple insulated dc and ac grids. The reconfiguration of this modular topology allows optimization for different design objectives. This work investigates the system efficiency-optimized reconfiguration. Two approaches for deriving the reconfiguration of the hybrid grid connecting converter architecture based on mission profiles are proposed. A validation of the analysis via laboratory results is given.

Crop Yield Prediction Using Improved Random Forest

Agriculture has an important role in India’s economic development. Crop productivity is affected by the rising population and the country’s ever-changing climate. Crop yield estimation is a challenge in the farming sector. Numerous studies have been conducted in the agricultural sector to better estimate crop yield through machine learning techniques. It is an efficient method for anticipating crop yields and determining which crops to cultivate. Random Forest has been widely utilized for this purpose. A set of parameters in the Random Forest classifier must be stay tuned. The machine learning algorithm will yield better results with correct hyper parameter adjustment. This work presents a hybrid approach to agricultural yield estimation using a Random Forest classifier and the Random Search method with a 0.99 R2 score, 0.045 MSE, and 0.022 MAE, the suggested method outperformed other existing approaches such as Decision Tree (DT), Multiple Linear Regression (MLR), Random Forest (RF), and Grid Search (GS) optimized RF. Validation methods such as R2, Mean Squared Error, and Mean Absolute Error to cross-validation have been used to confirm the authenticity of the outcomes. The purpose of this study is to apply the crop yield prediction approach into action to assist farmers in solving agricultural production concerns.

Wideband spectrum sensing using step-sampling based on the multi-path nyquist folding receiver

Wideband spectrum sensing with a high-speed analog-digital converter (ADC) presents a challenge for practical systems. The Nyquist folding receiver (NYFR) is a promising scheme for achieving cost-effective real-time spectrum sensing, which is subject to the complexity of processing the modulated outputs. In this case, a multipath NYFR architecture with a step-sampling rate for the different paths is proposed. The different numbers of digital channels for each path are designed based on the Chinese remainder theorem (CRT). Then, the detectable frequency range is divided into multiple frequency grids, and the Nyquist zone (NZ) of the input can be obtained by sensing these grids. Thus, high-precision parameter estimation is performed by utilizing the NYFR characteristics. Compared with the existing methods, the scheme proposed in this paper overcomes the challenge of NZ estimation, information damage, many computations, low accuracy, and high false alarm probability. Comparative simulation experiments verify the effectiveness of the proposed architecture in this paper.

Optimal bidding for large-scale hydropower stations serving multiple power grids in multi-regional monthly electricity markets

Driven by the development of the multi-level and unified electricity market in China, large-scale hydropower stations serving multiple power grids (LHSMPGs) have recently been required to enter into multiple regional electricity transactions. However, due to the multiple uncertainties of runoff and electricity prices, complex operation constraints, and difficulties in quantifying opportunity costs, formulating reliable bidding strategies for multi-regional monthly auction markets to maximize profits presents a challenge for LHSMPGs in China. Accordingly, in this study, we propose a tractable bidding optimization method for LHSMPGs in multi-regional monthly electricity markets. First, a scenario-based method is developed to describe the uncertainties in runoff and multi-regional electricity prices, as well as their cross-correlations. Then, a stochastic programming model is developed to coordinately optimize the piecewise bidding curves in multi-regional auction markets. The model considers the opportunity cost of bid quantities and complex monthly electricity allocation requirements during the whole time horizon of a year. Moreover, a monthly-rolling solution framework is designed to solve the stochastic programming model to ensure that the latest actual data can be dynamically considered to improve the accuracy of declaration. The application of the proposed method is verified through two cases based on the Xiluodu Hydropower Station, which ranks fourth in the world in terms of installed capacity. The results demonstrate that optimizing bidding curves in multi-regional monthly auction markets coordinately can enhance gross revenues and electricity sales. Moreover, a sensitivity analysis reveals that an excessively large or small allocation proportion of electricity between annual and monthly markets would weaken the guiding role of monthly auction markets in optimizing the allocation of hydropower resources and exacerbate the uneven distribution of hydropower generation between flood and dry seasons.

A Non-Rigid Hierarchical Discrete Grid Structure and Its Application to UAVs Conflict Detection and Path Planning

<p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Following the development of the unmanned aerial vehicle (UAV) industry, the number of UAVs has increased significantly, and higher expectations regarding the effect of spatial path planning have been expressed. Traditional coordinate-based conflict detection and path planning methods of UAVs are highly complex and have low computational efficiency. While the conflict detection method based on the grid system can improve efficiency, the rigid grid structure of the existing local grid system or discrete global grid system is insufficient. Since the rigid structure only encodes grid cells, multiple or different size grids are required to identify UAVs in motion, further complicating path planning. The main contributions of this article are as follows. First, we proposed a nonrigid hierarchical discrete grid structure and coding method for spatial three-dimensional grids and grid data model. This method can accomplish the aggregation of any adjacent eight grids to form multiple nonrigid structures, making the original fixed relationship between parent and child grids more flexible. Second, the nonrigid grid structure optimizes the identification ability of grid vertices, grid edges, and grid faces. It has eight times the identification ability of the rigid grid structure. We exploited this structure for UAV identification with the same size grid at any position of the grid structure. Third, based on the nonrigid grid structure, an improved UAV conflict detection method and path planning method are designed. Experimental results indicate that, on an average, the efficiency of the improved conflict detection is an order of magnitude higher than that of the traditional computational detection method. The improved path planning method reduces the path length by an average of 11% compared to the single-grid planning method, and is 3.85 times more efficient than the multi-grid buffer planning method.

Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets

More energy flexibility is required to alleviate the stress of power systems caused by intermittent and weather-dependent solar and wind power. The utilization of demand-side flexibility (e.g., flexibility management strategies and distributed storage technologies) can be considered a win-win approach for demand-side users and power grids. However, the economic benefits of distributed energy storage systems in buildings are usually underestimated without considering the full-scale flexibility utilization, which may impede storage investments. Meanwhile, the disposal problem of retired EV batteries is becoming more serious. Repurposing these batteries for stationary applications in buildings seems cost-effective and eco-friendly. Therefore, this study first proposes novel optimal dispatch strategies for different storage systems in buildings to maximize their benefits from providing multiple grid flexibility services simultaneously, and then conducts a comparative life-cycle economic analysis on thermal energy storage, new and second-life batteries. The optimal configuration of hybrid storage systems is also analyzed to facilitate the decision-making of building owners/operators. Test results show that thermal energy storage and electrical energy storage can increase the economic benefits by 13% and 2.6 times, respectively. Battery storage may no longer be an expensive option for building-scale investment due to downward trends in capacity costs and environmental impacts.