Статті в журналах: "Residual based Grid Adaptation"

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Cui, Pengcheng, Bin Li, Jing Tang, Jiangtao Chen, and Youqi Deng. "A modified adjoint-based grid adaptation and error correction method for unstructured grid." Modern Physics Letters B 32, no. 12n13 (May 10, 2018): 1840020. http://dx.doi.org/10.1142/s0217984918400201.

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Grid adaptation is an important strategy to improve the accuracy of output functions (e.g. drag, lift, etc.) in computational fluid dynamics (CFD) analysis and design applications. This paper presents a modified robust grid adaptation and error correction method for reducing simulation errors in integral outputs. The procedure is based on discrete adjoint optimization theory in which the estimated global error of output functions can be directly related to the local residual error. According to this relationship, local residual error contribution can be used as an indicator in a grid adaptation strategy designed to generate refined grids for accurately estimating the output functions. This grid adaptation and error correction method is applied to subsonic and supersonic simulations around three-dimensional configurations. Numerical results demonstrate that the sensitive grids to output functions are detected and refined after grid adaptation, and the accuracy of output functions is obviously improved after error correction. The proposed grid adaptation and error correction method is shown to compare very favorably in terms of output accuracy and computational efficiency relative to the traditional featured-based grid adaptation.

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Yousuf, Mohamed, and N. Balakrishnan. "Residual-Based Grid Adaptation for Meshless LSFD-U Solver." AIAA Journal 57, no. 4 (April 2019): 1649–58. http://dx.doi.org/10.2514/1.j057633.

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Li, Li. "An Initial Investigation of Adjoint-Based Unstructured Grid Adaptation for Vortical Flow Simulations." Modelling and Simulation in Engineering 2011 (2011): 1–9. http://dx.doi.org/10.1155/2011/217326.

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A computational fluid dynamics (CFDs) method utilizing unstructured grid technology has been employed to compute vortical flow around a65°delta wing with sharp leading edge, which is specially known as the geometry of the second international vortex flow experiment (VFE-2). In VFE-2,65°delta wings with different leading edges had been broadly investigated by experiments, which resulted in a special database for CFDs codes validation. The emphasis of this paper is to investigate the effectiveness of an adjoint-base grid adaptation method for unstructured grid in capturing concentrated vortices generated at sharp edges or flow separation lines of lifting surfaces flying at high angles of attack. Earlier experiences in vortical flow simulations had indicated that the vortex behavior is highly dependent on the local grid resolution both on body surface and space. The adjoint-based adaptation method used here is hoped to save grid points with a reasonable grid resolution for vortical flow simulations. The basic idea is to construct a new adaptive sensor in a grid adaptation process with the intent to tell where the elements should be smaller or larger by introducing an adjoint formulation to relate the estimated functional error to local residual errors of both the primal and adjoint solutions.

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Wang, Shan, Ding Wang, and Junren Sun. "Artificial Neural Network-Based Ionospheric Delay Correction Method for Satellite-Based Augmentation Systems." Remote Sensing 14, no. 3 (January 31, 2022): 676. http://dx.doi.org/10.3390/rs14030676.

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Ionospheric delay is a critical error source in Global Navigation Satellite Systems (GNSSs) and a principal aspect of Satellite Based Augmentation System (SBAS) corrections. Grid Ionospheric Vertical Delays (GIVDs) are derived from the delays on Ionosphere Pierce Points (IPPs), which are observed by SBAS reference stations. SBAS master stations calculate ionospheric delay corrections by several methods, such as planar fit or Kriging. However, when there are not enough IPPs around an Ionosphere Grid Point (IGP) or the IPPs are unevenly distributed, the fitting accuracy of planar fit or Kriging is unsatisfactory. Moreover, the integrity bounds of Grid Ionospheric Vertical Errors (GIVEs) are overly conservative. Since Artificial Neural Networks (ANNs) are widely used in ionospheric research due to their self-adaptation, parallelism, non-linearity, robustness, and learnability, the ANN method for GIVD and GIVE derivation is proposed in this article. Networks are separately trained for IGPs, and five years of historical data are applied on network training. Principal Component Analysis (PCA) is applied for dimensionality reduction of geomagnetic and solar indices, which is employed as a network input feature. Furthermore, the GIVE algorithm of the ANN method is derived based on the distribution of the residual random variable. Finally, experiments are conducted on 12 IGPs over the East China region. Under normal ionospheric conditions, compared with the planar fit and Kriging methods, the residual reduction of the ANN method is approximately 15%. The ANN method fits the ionospheric delay residual error better. The percentage of GIVE availability under 2.7 m increases at least 25 points in comparison to Kriging. Under disturbed conditions, due to a lack of training samples, the ANN method is incompetent compared with planar fit or Kriging.

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Grigorov, Serhii M., Anton K. Khudyk, and Iryna S. Sukhina. "APPLIANCE OF CAD / CAM MODELING IN PROSTHETICS OF BONE DEFECTS OF FACIAL BONES." Wiadomości Lekarskie 74, no. 6 (2021): 1469–74. http://dx.doi.org/10.36740/wlek202106133.

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The aim: Improving the ability to restore the shape and function of the bones of the maxillofacial area through the use of their own techniques. Materials and methods: Materials and methods: This research was carry out on the basis of the Department of Surgical Dentistry and Maxillofacial Surgery of Kharkov National Medical University in the Department of Head and Neck Surgery, Kharkov “Regional Clinical Hospital” (2018-2020 years). Examination and treatment of 26 patients, aged from 19 to 55 years, who needed reconstructive surgery to restore the anatomical shape, integrity and function of the maxilla or mandibular jaw. Results: All patients were treated according to the developed method (utility model patent № 145754 “Method of treating traumatic fractures of the maxillofacial area in adults using bone osteosynthesis 3D modeled titanium mini plates”), which consists in 3D modeling, based on previously performed computed tomography, and exploitation individually modeled titanium mini grids and titanium endoprostheses. The experience of this technique shown the advantage of the proposed method not only in the correction of traumatic defects, but also defects of the jaw bones that occur due to the removal of bulky neoplasms. Conclusions: Individual 3D simulated mini grids / plates and endoprostheses, according to the study provide maximum adaptation and restoration of anatomical shape, relief of jaws, their integrity and contour, provide prevention of pathological fractures by stabilizing residual bone tissue (titanium mini grids) serves as a carcass , provide prevention of prolapse (germination) of soft tissues in the area of postoperative bone defect (mini grid does not allow soft tissues to germinate in the area of postoperative bone defect on the outside), which improves functional and cosmetic results. Thus, our proposed method of osteosynthesis using 3D simulated titanium miniplates can be recommended for use in clinical practice.

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Christodoulou, George C., Arthanassios E. Kouloumbis, and Alice O. Aza. "2 - D CIRCULATION IN THE SARONIC GULF." Coastal Engineering Proceedings 1, no. 20 (January 29, 1986): 182. http://dx.doi.org/10.9753/icce.v20.182.

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The paper refers to mathematical modeling of the 2-D hydrodynamic circulation in the Saronic Gulf, in Greece. The finite element method is followed to evaluate the flow behaviour. Two grids are employed, a coarse one for initial model adaptation and a finer one for a more detailed study of circulation patterns. Actual tide data are obtained at the boundary of the domain and a typical compound tide is introduced; however residual tidal currents are found to be negligible. Wind-generated flow fields under several prevailing wind directions are determined and their comparison to qualitative field evidence is satisfactory. Furthermore, a possible surface tilt along the open boundary associated with large scale circulation in the Aegean Sea, is studied and shown to produce, significant motion in the Gulf. Based on circulation patterns obtained, preliminary estimates of dispersion from a proposed sea outfall for Athens sewage can be made. Further work proceeds towards quantitative verification of the model in connection with the proper description of forcing mechanisms and their superposition.

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Wang, Feiyan, Zhengyong Ren, and Lihong Zhao. "A goal-oriented adaptive finite-element approach for 3-D marine controlled-source electromagnetic problems with general electrical anisotropy." Geophysical Journal International 229, no. 1 (November 30, 2021): 439–58. http://dx.doi.org/10.1093/gji/ggab485.

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SUMMARY We present a goal-oriented adaptive finite-element algorithm for accurately modelling marine controlled-source electromagnetic responses in 3-D media with general electrical anisotropy. We formulate the primal boundary value problem in terms of the total electric field for general applications. Following the goal-oriented adaptivity concept, the dual problem is derived from a functional designed to measure the data errors of interest in light of the data quality. We approximate the solutions to the primal and dual formulations using the edge finite-element method on tetrahedral grids for a flexible treatment of complex geological settings and survey geometries. To control the mesh adaptation, we develop a reliable residual-type a posteriori error estimation that takes account of the volumetric residual and the numerical discontinuity of the normal component of the electrical current density and that of the tangential component of the magnetic field with respect to non-smooth and anisotropic coefficients. We demonstrate the proposed modelling solver on 1-D MCSEM scenarios with varying degrees of electrical anisotropy. The comparison with goal-oriented adaptivity results obtained from other three commonly used error indicators shows that our approach is robust in dealing with both moderate and strong electrical anisotropy. After that, we constructed a 3-D hydrocarbon-bearing reservoir model with slope seafloor topography and tilted transverse isotropy in the background to examine our algorithm for the case of multiple sources. Finally, we implement a sensitivity analysis procedure to evaluate the resolution of the electrical anisotropy. The quantitative results indicate limitations and preferences of conventional MCSEM data in resolving anisotropic models, providing fundamental insights for inversion based data interpretation.

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Cui, Pengcheng, Yongqian Zheng, Peimin Xu, Bin Li, Mingsheng Ma, and Guiyu Zhou. "The comparison of adjoint-based grid adaptation and feature-based grid adaptation method." Journal of Physics: Conference Series 2280, no. 1 (June 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2280/1/012003.

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Abstract Grid adaption is a popular method to enhance the resolution of flow field and the precision of numerical simulation, which automatically optimizes the grid distribution instead of manual complicated work. There exist usually two grid adaptation methods, the feature based grid adaption and adjoint based grid adaption, the former focuses on shocks, vortexes and other features of flow field, and the latter focuses on lift, drag and other aerodynamic characteristics. The comparison of adjoint based grid adaption and feature based grid adaption method is investigated in this paper. Numerical simulations show that both feature adaption and adjoint adaption could improve the resolution of flow field and the precision of numerical simulation such as lift and drag. As for the flow features, the feature adaptation could capture the obvious shock waves and vortexes in the flow field, the adjoint adaptation, however, only captures the flow features that are contributory to the accuracy of aerodynamic characteristics. As for the aerodynamic characteristics, some shock waves and vortexes have little influences to the forces, so the feature adaptation is not efficient as adjoint adaptation, which could greatly improve the accuracy of aerodynamic characteristics.

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Tatsis, Vasileios A., and Konstantinos E. Parsopoulos. "Differential Evolution with Grid-Based Parameter Adaptation." Soft Computing 21, no. 8 (November 4, 2015): 2105–27. http://dx.doi.org/10.1007/s00500-015-1911-2.

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10

Graf, U., P. Romstedt, and W. Werner. "Use of a Dynamic Grid Adaptation in the Asymmetric Weighted Residual Method." Nuclear Science and Engineering 92, no. 1 (January 1986): 66–70. http://dx.doi.org/10.13182/nse86-a17866.

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Fung, Larry S. K., and Ali H. Dogru. "Parallel Unstructured-Solver Methods for Simulation of Complex Giant Reservoirs." SPE Journal 13, no. 04 (December 1, 2008): 440–46. http://dx.doi.org/10.2118/106237-pa.

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Summary The major issues for parallel solvers in a modern reservoir simulator are robustness, scalability, efficiency, and flexibility. There is significant interest in running fast field-scale simulations for complex giant Middle Eastern reservoirs, which will require tens of millions to hundreds of millions of grid cells to give reasonable resolution. At the same time, significant geologic complexity will require the treatment of dual-permeability regions, faulting and fractures, and high variations of reservoir and fluid properties. Of course, the methods should also work well for extracted-sector simulation with local grid refinements in both the structured and unstructured discretization. The preconditioning methods considered in this work include both the single-stage and multistage frameworks. In the single-stage framework, a novel method is considered in addition to the well-known variants of incomplete lower-upper (ILU) factorizations [ILU0, ILU(k), and ILUT]. The new method is a highly parallel method, which, in this paper, will be referred to as the unstructured line-solve power-series (LSPS) method. The method will be discussed and contrasted in light of key issues for parallel linear solvers. The unstructured LSPS has certain interesting properties in the parallel construct, which make it a highly effective component. The multistage method researched in this work is of the constraint pressure residual (CPR) framework. The method uses approximate pressure solve as the first-stage preconditioning to the full-system preconditioning. A number of original adaptations based on this concept were researched. Here, the use of the parallel algebraic multigrid (PAMG) method and other single-level methods mentioned previously in combinations within the multistage CPR framework were explored. Certain methods constructed in this way are found to be highly efficient, scalable, and robust. The methods developed are discussed, and several test problems are included, in this paper. The largest simulation model tested to date using these solver methods is a 172-million-cell full-field model of a supergiant carbonate complex with more than 3,000 wells and 60 years of history simulation. Introduction Parallel reservoir simulation involving millions of grid cells is now common practice and is an essential component for the management of many giant carbonate complexes in the Middle East. The recent advances are aided in part by the computational power offered by inexpensive PC clusters. Many of today's parallel machines are built with mass-produced commodity-based components. At the same time, research and development on parallel highly scalable methods in the modern reservoir simulator have made routine field-scale simulation an effective and useful part of resource planning and analysis. Field-scale analyses are often desired over sector simulation for a comprehensive understanding of overall reservoir-behavior and recovery-processes performance. Special study involving an area of interest frequently arises in a full-field project. For example, evaluation of alternative designs for expensive maximum-reservoir-contact wells with intelligent downhole controls and production equipment requires near-wellbore reservoir simulation and optimization workflow. Thus, the demand is high for simulation capabilities with mixed structured and unstructured grids for fast field-scale megacell modeling. The capability to refine and coarsen at ease regionally and perform simulation and analyses at multiple scales within a single project is a primary near-term goal. This paper addresses one critical component of the tool set required to accomplish this mission--the linear solver. The primary solver methods in the old generation of reservoir simulators typically use nested factorization or variants of ILU-factorization method for preconditioning. While extension to small-scale parallel processing was achieved in the late 1990s, these methods have limitations in terms of scalability or robustness for the very-large-scale simulations where parallel processing with hundreds or even thousands of processors is required for speed and performance. Previously, within the structured-grid framework, a solver method known as the z-line Neumann series, which is more scalable for parallel field-scale simulation of structured grid, was documented by Dogru et al. (2002). Later, a parallel structured multigrid method was introduced by Fung and Dogru (2000) for treating the local-grid-refinement problems. The additional solver method for the dual-porosity dual-permeability system was later described by Fung and Al-Shaalan (2005). In this work, new ideas in the fully unstructured setting are being researched and developed. These ideas involve both the single-stage method and the multistage method. In the single-stage method, a novel idea of building an approximate inverse preconditioner through matrix substructuring of the Jacobian matrix was investigated. This substructuring method, which we refer to as LSPS, is a powerful generalization of the z-line Neumann series method. The method is fully unstructured. It increases robustness by tracing the maximum-transmissibility direction of the 3D unstructured graph. The strategy is particularly beneficial for reservoirs with fracture corridors and superpermeability (super-K) regions that cause difficulties for other solver methods. Furthermore, parallel efficiency is maintained, which is crucial for large-scale multiprocessor applications of the method. In the multistage method, the two-stage CPR method was investigated. The CPR method was first introduced into the petroleum literature by Wallis (1983) and Wallis et al. (1985). It was recently applied by Gratien et al. (2004) and Cao et al. (2005) in a new simulator development in which they have used the PAMG method as the pressure preconditioner. The research documented here explores the quasi-implicit-pressure-explicit-saturation (quasi-IMPES) reduction methods and the use of various approaches to solve the pressure approximately as a first-stage preconditioning to the full-system matrix. Solver results for several sample problems are included for comparison of the various methods. These include the public-domain data sets for the SPE1 (Odeh 1981) and SPE10 (Christie and Blunt 2001) comparative-solution projects and several megacell-simulation models. To add some challenge for the solver methods, the SPE1 grid system has been refined uniformly to 300,000 cells. To put all the methods into proper prospective, the three variants of the ILU factorizations [ILU0, ILU(k), and ILUT] are used as baseline comparison for some problems. The ILU preconditioners are well-known and are described in Saad (2003), thus descriptions of them are not included here. Interested readers can refer to Saad (2003) or the many other reference papers concerned with them.

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Park, Michael Andrew. "Adjoint-Based, Three-Dimensional Error Prediction and Grid Adaptation." AIAA Journal 42, no. 9 (September 2004): 1854–62. http://dx.doi.org/10.2514/1.10051.

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Srinivasan, Kumar, and Stanley G. Rubin. "Solution-Based Grid Adaptation through Segmented Multigrid Domain Decomposition." Journal of Computational Physics 136, no. 2 (September 1997): 467–93. http://dx.doi.org/10.1006/jcph.1997.5774.

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Balasubramanian, R., and J. C. Newman. "Comparison of adjoint-based and feature-based grid adaptation for functional outputs." International Journal for Numerical Methods in Fluids 53, no. 10 (2007): 1541–69. http://dx.doi.org/10.1002/fld.1361.

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Jiadong Ren, Jingyan Cui, and Caiyan Pei. "Clustering over Data Streams based on Adaptation and Irregular Grid." Journal of Convergence Information Technology 6, no. 9 (September 30, 2011): 302–9. http://dx.doi.org/10.4156/jcit.vol6.issue9.35.

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Brix, Kolja, Silvia Sorana Melian, Siegfried Müller, and Gero Schieffer. "Parallelisation of Multiscale-Based Grid Adaptation using Space-Filling Curves." ESAIM: Proceedings 29 (2009): 108–29. http://dx.doi.org/10.1051/proc/2009058.

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Tatsis, Vasileios A., and Konstantinos E. Parsopoulos. "Experimental Assessment of Differential Evolution with Grid-Based Parameter Adaptation." International Journal on Artificial Intelligence Tools 27, no. 04 (June 2018): 1860004. http://dx.doi.org/10.1142/s0218213018600047.

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Evolutionary algorithms have been long established as an essential field of research in Computational Intelligence. Differential Evolution is placed among the most successful algorithms of this type. However, it has proved to be highly sensitive on its parameters. For this purpose, offline and online parameter-control methods have been proposed. Recently, a grid-based parameter adaptation procedure was introduced and successfully applied on Differential Evolution. Despite the generality of the method, the resulting algorithm was capable to compete with already tuned adaptive algorithms on high-dimensional test problems without any additional preprocessing. The present work extends the experimental study of this approach on the state-of-the-art CEC-2013 test suite. Two variants of the algorithm are considered and different initial conditions are tested to shed light on its performance aspects. Comparisons with other algorithms as well as between the proposed approaches are reported. The results verify the potential of the grid-based parameter adaptation method as a general-purpose alternative for parameter setting.

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Assassa, Fady, and Wolfgang Marquardt. "Optimality-based grid adaptation for input-affine optimal control problems." Computers & Chemical Engineering 92 (September 2016): 189–203. http://dx.doi.org/10.1016/j.compchemeng.2016.04.041.

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Sárközi, Réka, Péter Iványi, and Attila Béla Széll. "Formex algebra adaptation into parametric design tools and rotational grids." Pollack Periodica 15, no. 2 (August 2020): 152–65. http://dx.doi.org/10.1556/606.2020.15.2.14.

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Abstract:This paper describes the adaptation of the formex configuration processing to the computer program Grasshopper 3D and focuses on the applied mathematical solutions. Formex algebra is a mathematical system, primarily used for planning structural systems like truss-grid domes and vaults, together with the programming language Formian. The goal of the research is to allow architects to plan truss-grid structures easily with parametric design tools based on the versatile formex algebra mathematical system. To produce regular structures, coordinate system transformations are used. Owing to the abilities of the parametric design software, it is possible to apply further modifications on the structures and gain special forms. The paper covers the basic dome types, and it introduces additional dome-based structures using special coordinate-system solutions based on a spherical coordinate system, vault structures and their modifications based on a cylindrical coordinate system and circular structures and their modifications based on polar coordinates. Moreover two rotational grid tools are introduced, which uses coordinate system transformations on a unique way to create surfaces of revolutions based on the given generating curve and create grid structures on these surfaces. It also describes the solution technique to implement the triangular grid version of every one of these tools based on diamatic domes. The adaptation of formex algebra and the parametric workflow of Grasshopper together give the possibility of quick and easy design and optimization of special truss-grid domes.

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Chen, Cun Xiang, Zun Wen He, Jing Ming Kuang, and Hong Mei Sun. "Grid-Based Cluster Head Selection Algorithm in Wireless Sensor Networks." Applied Mechanics and Materials 427-429 (September 2013): 1497–501. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1497.

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Although low-energy adaptive clustering hierarchy (LEACH) protocol adopts distributed clustering algorithm and randomized rotation of Cluster Heads (CHs) mechanism to reduce energy consumption, election of CHs without residual energy and position information of each nodes brings about irregular distribution of CH, low network coverage and short lifecycle. In order to avoid these shortcomings, a Grid-based Cluster Head Selection (GCHS) is proposed. Referring to sensing distance, network is divided into several grids equivalent to independent clusters which can meet network coverage and connectivity. Furthermore, CH is selected ground on residual energy level of each node. Simulation taking full advantage of concept mentioned above manifests that it achieves a significant improvement in network coverage and lifecycle.

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Boey, Edward, Yves Bourgault, and Thierry Giordano. "Anisotropic residual based a posteriori mesh adaptation in 2D: Element based approach." Journal of Computational and Applied Mathematics 349 (March 2019): 1–20. http://dx.doi.org/10.1016/j.cam.2018.08.053.

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Jiang, Yao, and Siva Nadarajah. "Adjoint-based error estimation for grid adaptation for large eddy simulation." Computers & Fluids 236 (March 2022): 105295. http://dx.doi.org/10.1016/j.compfluid.2021.105295.

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Zhong, Minyue, Yao Tan, Jie Li, Hongming Zhang, and Siyi Yu. "Cattle Number Estimation on Smart Pasture Based on Multi-Scale Information Fusion." Mathematics 10, no. 20 (October 18, 2022): 3856. http://dx.doi.org/10.3390/math10203856.

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In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE.

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Sun, Zekun, Hao Liu, Yueming Ding, Haopeng Luo, Ting Liu, and Qinyue Tan. "Collaborative Control Strategy of Power Quality Based on Residual Capacity of Photovoltaic Inverter." Energies 15, no. 21 (October 29, 2022): 8049. http://dx.doi.org/10.3390/en15218049.

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With the large-scale distributed PV connected to the grid, the random and intermittent nature of PV output, the non-linearity of the inverter, as well as the low daytime base-load and large-scale back feeding cause outstanding power quality problems such as overvoltage, three-phase unbalance, and high harmonic content at the end of the power supply system, which seriously affects the safe and stable operation of the grid and power efficiency. Based on the residual capacity of the inverter, this paper investigates a cooperative power quality control strategy that integrates active power filter, reactive power compensation, and inverter functions to achieve comprehensive power quality control and management without adding additional hardware equipment. The validation results show that the control strategy strongly improves the power quality, grid security, stability, and efficiency. This strategy indicates significant economic benefits.

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Liu, Jian Feng, and Yong Guo Sun. "Research on Residual Current Protective Device Based on Micro-Controller of MSP430." Advanced Materials Research 662 (February 2013): 838–41. http://dx.doi.org/10.4028/www.scientific.net/amr.662.838.

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A residual current protector based on high performance micro-controller MSP430 is introduced. The micro-controller MSP430F149 is used as the main controller in the device, which can detect the real-time residual current value. And the residual current operating value can be switched automatically according to the present condition of the power system. When the protection is occurred, the residual current protective device has the inverse time lag function, ideal time delay and the automatic reclosing function. So the residual current protective device can be used at all kinds of complicated rural grid and climatic condition, and the safety and the reliability of the power supply can be ensured.

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Zhou, Chuansai, Haochen Li, Chen Yu, Jiangjiang Xia, and Pingwen Zhang. "A station-data-based model residual machine learning method for fine-grained meteorological grid prediction." Applied Mathematics and Mechanics 43, no. 2 (January 27, 2022): 155–66. http://dx.doi.org/10.1007/s10483-022-2822-9.

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AbstractFine-grained weather forecasting data, i.e., the grid data with high-resolution, have attracted increasing attention in recent years, especially for some specific applications such as the Winter Olympic Games. Although European Centre for Medium-Range Weather Forecasts (ECMWF) provides grid prediction up to 240 hours, the coarse data are unable to meet high requirements of these major events. In this paper, we propose a method, called model residual machine learning (MRML), to generate grid prediction with high-resolution based on high-precision stations forecasting. MRML applies model output machine learning (MOML) for stations forecasting. Subsequently, MRML utilizes these forecasts to improve the quality of the grid data by fitting a machine learning (ML) model to the residuals. We demonstrate that MRML achieves high capability at diverse meteorological elements, specifically, temperature, relative humidity, and wind speed. In addition, MRML could be easily extended to other post-processing methods by invoking different techniques. In our experiments, MRML outperforms the traditional downscaling methods such as piecewise linear interpolation (PLI) on the testing data.

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27

Mdrafi, Robiulhossain, Qian Du, Ali Cafer Gurbuz, Bo Tang, Li Ma, and Nicolas H. Younan. "Attention-Based Domain Adaptation Using Residual Network for Hyperspectral Image Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 6424–33. http://dx.doi.org/10.1109/jstars.2020.3035382.

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Vogt, Michael, Norbert Mu¨ller, and Rolf Isermann. "On-Line Adaptation of Grid-Based Look-up Tables Using a Fast Linear Regression Technique." Journal of Dynamic Systems, Measurement, and Control 126, no. 4 (December 1, 2004): 732–39. http://dx.doi.org/10.1115/1.1849241.

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Advanced control systems require accurate process models, while processes are often both nonlinear and time variant. After introducing the identification of nonlinear processes with grid-based look-up tables, a new learning algorithm for on-line adaptation of look-up tables is proposed. Using a linear regression approach, this new adaptation algorithm considerably reduces the convergence time in relation to conventional gradient-based adaptation algorithms. An application example and experimental results are shown for the learning feedforward control of the ignition angle of a spark ignition engine.

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29

Nakamura, Sota, Yuichi Kobayashi, and Taisei Matsuura. "Grid-Based Estimation of Transformation Between Partial Relationships Using a Genetic Algorithm." Journal of Robotics and Mechatronics 34, no. 4 (August 20, 2022): 786–94. http://dx.doi.org/10.20965/jrm.2022.p0786.

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Human motor learning is characterized by adaptation, wherein information obtained in the past is transferred to a different situation. In this study, we investigate a grid-based computation for explaining the reuse of the information of an existing controller for adaptation to a partial malfunction of a controller. To this end, a motor learning scheme is adopted based on the detection and estimation of partial relationships. The transformation between the partial relationships is estimated based on a grid-based estimation of the two coordinate systems. In this estimation, the coordinate systems are optimized using a genetic algorithm. Two arms in a reflection are considered, and it is confirmed that the transformation of the differential kinematics (Jacobian), as an example of the partial relationships, can be estimated by the proposed method.

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30

Yu, Insang, Huicheul Jung, Christabel Jane P. Rubio, Hayong Kim, and Sangman Jeong. "Assessment of a Grid-Based Wind Disaster Risk for Climate Change Adaptation." Journal of the Korean Society of Hazard Mitigation 19, no. 2 (April 30, 2019): 209–19. http://dx.doi.org/10.9798/kosham.2019.19.2.209.

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31

Gerhard, Nils, Daniel Caviedes-Voullième, Siegfried Müller, and Georges Kesserwani. "Multiwavelet-based grid adaptation with discontinuous Galerkin schemes for shallow water equations." Journal of Computational Physics 301 (November 2015): 265–88. http://dx.doi.org/10.1016/j.jcp.2015.08.030.

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32

Balaguru, S., Vela Murali, and P. Chellapandi. "Measurement of the Residual Stresses and Investigation of Their Effects on a Hardfaced Grid Plate due to Thermal Cycling in a Pool Type Sodium-Cooled Fast Reactor." Science and Technology of Nuclear Installations 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/8353256.

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In sodium-cooled fast reactors (SFR), grid plate is a critical component which is made of 316 L(N) SS. It is supported on core support structure. The grid plate supports the core subassemblies and maintains their verticality. Most of the components of SFR are made of 316 L(N)/304 L(N) SS and they are in contact with the liquid-metal sodium which acts as a coolant. The peak operating temperature in SFR is 550°C. However, the self-welding starts at 500°C. To avoid self-welding and galling, hardfacing of the grid plate has become necessary. Nickel based cobalt-free colmonoy 5 has been identified as the hardfacing material due to its lower dose rate by Plasma Transferred Arc Welding (PTAW). This paper is concerned with the measurement and investigations of the effects of the residual stress generated due to thermal cycling on a scale-down physical model of the grid plate. Finite element analysis of the hardfaced grid plate model is performed for obtaining residual stresses using elastoplastic analysis and hence the results are validated. The effects of the residual stresses due to thermal cycling on the hardfaced grid plate model are studied.

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33

Aljohani, Nader, Tierui Zou, Arturo S. Bretas, and Newton G. Bretas. "Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers." Applied Sciences 11, no. 10 (May 13, 2021): 4419. http://dx.doi.org/10.3390/app11104419.

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In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the IEEE 14 and IEEE 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.

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34

Thompson, Allen. "Adaptation, Transformation, and Development." Environmental Ethics 42, no. 1 (2020): 5–20. http://dx.doi.org/10.5840/enviroethics20204213.

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It is widely accepted that we must adapt to climate change. But we sit on the edge of radical, unprecedented, and rapid anthropogenic environmental changes that are driven by many factors in addition to greenhouse gas emissions. In this way, we occupy a unique and precarious position in the history of our species. Many basic conditions of life on Earth are changing at an alarming rate and thus we should begin to transform and broaden our thinking about adaptation. The conceptual history of climate adaptation intersects with conceptions of human development and sustainability, which provides a framework for adaptation in how we think about human flourishing and, subsequently, what it is to be human in the Anthropocene. If sustainability is about maintaining human welfare across generations but we acknowledge that climate change may undercut our ability to deliver as much and as good total or natural capital to subsequent generations, we have a residual duty to otherwise positively affect the welfare of future generations. A subjective, preference-based conception of human welfare is compared to an objective, capabilities-based approach and, while some adaptive preferences are unavoidable, embracing an objective theory of human flourishing provides a superior approach for meeting the residual duty we have to future generations by beginning the process of adapting our conception of human natural goodness, or what it is to be a good human being.

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35

Zheng, Chen, Shuangyin Dai, Bo Zhang, Qionglin Li, Shuming Liu, Yuzheng Tang, Yi Wang, Yifan Wu, and Yi Zhang. "A Residual Voltage Data-Driven Prediction Method for Voltage Sag Based on Data Fusion." Symmetry 14, no. 6 (June 20, 2022): 1272. http://dx.doi.org/10.3390/sym14061272.

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Voltage sag is the most serious power quality problem in the three-phase symmetrical power system. The influence of multiple factors on the voltage sag level and low computational efficiency also pose challenges to the prediction of residual voltage amplitude of voltage sag. This paper proposes a voltage sag amplitude prediction method based on data fusion. First, the multi-dimensional factors that influence voltage sag residual voltage are analyzed. Second, these factors are used as input, and a model for predicting voltage sag residual voltage based on data fusion is constructed. Last, the model is trained and debugged to enable it to predict the voltage sag residual voltage. The accuracy and feasibility of the method are verified by using the actual power grid data from East China.

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36

Li, Changzhi, Dandan Liu, Mao Wang, Hanlin Wang, and Shuai Xu. "Detection of Outliers in Time Series Power Data Based on Prediction Errors." Energies 16, no. 2 (January 4, 2023): 582. http://dx.doi.org/10.3390/en16020582.

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The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.

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37

Sivasankarareddy, V., G. Sundari, Ch Rami Reddy, Flah Aymen, and Edson C. Bortoni. "Grid-Based Routing Model for Energy Efficient and Secure Data Transmission in WSN for Smart Building Applications." Applied Sciences 11, no. 22 (November 9, 2021): 10517. http://dx.doi.org/10.3390/app112210517.

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Presently, due to the establishment of a sensor network, residual buildings in urban areas are being converted into smart buildings. Many sensors are deployed in various buildings to perform different functions, such as water quality monitoring and temperature monitoring. However, the major concern faced in smart building Wireless Sensor Networks (WSNs) is energy depletion and security threats. Many researchers have attempted to solve these issues by various authors in different applications of WSNs. However, limited research has been conducted on smart buildings. Thus, the present research is focused on designing an energy-efficient and secure routing protocol for smart building WSNs. The process in the proposed framework is carried out in two stages. The first stage is the design of the optimal routing protocol based on the grid-clustering approach. In the grid-based model, a grid organizer was selected based on the sailfish optimization algorithm. Subsequently, a fuzzy expert system is used to select the relay node to reach the shortest path for data transmission. The second stage involves designing a trust model for secure data transmission using the two-fish algorithm. A simulation study of the proposed framework was conducted to evaluate its performance. Some metrics, such as the packet delivery ratio, end-end delay, and average residual energy, were calculated for the proposed model. The average residual energy for the proposed framework was 96%, which demonstrates the effectiveness of the proposed routing design.

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38

Li, Jinchao, Shaowen Zhu, Qianqian Wu, and Pengfei Zhang. "A Hybrid Forecasting Model Based on EMD-GASVM-RBFNN for Power Grid Investment Demand." Mathematical Problems in Engineering 2018 (September 26, 2018): 1–17. http://dx.doi.org/10.1155/2018/7416037.

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Power grid as an important infrastructure which ensures the healthy development of economy and society and accurate and reasonable prediction of the power grid investment demand has always been the focus problem of the power planning department and the power grid enterprises. In view of the complex nonlinear and nonstationary characteristics of the power grid investment demand sequence, a novel hybrid EMD-GASVM-RBFNN forecasting model based on empirical mode decomposition (EMD) method, support vector machines optimized by genetic algorithm (GA-SVM) model, and radial basis function neural network (RBFNN) model is proposed. Firstly, the EMD method is used to decompose the original power grid investment data sequence into a series of IMF components and a residual component which have stronger regularity compared with the original data. Then, according to the different characteristics of each subsequence, the GA-SVM and RBFNN model will be used to forecast different subsequences, respectively. Next, the prediction results of different subsequences are aggregated to obtain the final prediction results of the power grid investment. Finally, this paper dynamically simulates China’s power grid investment from 2018 to 2020 based on the EMD-GASVM-RBFNN hybrid forecasting model and Monte Carlo method.

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39

Chen, Yucheng, Haipeng Xie, Guangming Lu, Lulu Zhang, Jianfeng Yan, and Shan Li. "Transient voltage stability assessment of renewable energy grid based on residual SDE-Net." Energy Reports 8 (November 2022): 991–1001. http://dx.doi.org/10.1016/j.egyr.2022.10.179.

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40

Momeni, M., and A. H. Mazinan. "Improvement of power quality in grid-connected inverter through adaptation-based control strategy." Energy, Ecology and Environment 4, no. 1 (February 2019): 37–48. http://dx.doi.org/10.1007/s40974-019-00111-y.

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41

Schäfer, Pascal, Artur M. Schweidtmann, Philipp H. A. Lenz, Hannah M. C. Markgraf, and Alexander Mitsos. "Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices." Computers & Chemical Engineering 132 (January 2020): 106598. http://dx.doi.org/10.1016/j.compchemeng.2019.106598.

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42

Kun, Zhao, Xing Ying, Jie Xu, Yin Zhang, Yan Lei, and Xing Dong. "Rapid Double-Layer Identification Method of Multi-Relevant Bad Data in Large-Scale Power Grids." Applied Mechanics and Materials 672-674 (October 2014): 1294–300. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1294.

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Bad data detection and identification is an important part of state estimation. When the relevant bad data appears, however, there is residual pollution and residual submerged condition in currently available methods of bad data detection and identification. In view of the above problem, this article presents a double-layer bad data detection and identification technique. At first, it is based on regularization residual detection method (Rn detection method) to identify the suspect measurement sets. And then, it presents a fast search technique of interrelated suspect measurements to search interrelated measurements in all the suspect measurements of the entire power grid and produce interrelated suspect measurement sets. Furthermore, use double-layer identification method to fast identify the bad data in interrelated suspect measurement sets, in other words, identify all the bad data in entire power grid. At last, taking IEEE39 node power grid for example, this detection method of bad data is analyzed, the accuracy and effectiveness of this method is to be verified.

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43

Maharjan, Sangita, and Ram P. Regmi. "Grid Based Temperature and Relative Humidity Distribution Map of the Kathmandu Valley." Journal of Institute of Science and Technology 19, no. 1 (November 8, 2015): 7–13. http://dx.doi.org/10.3126/jist.v19i1.13819.

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Kathmandu valley has seen visible transformations in its intrinsic climate in recent decades. The atmosphere of the Kathmandu valley has become increasingly uncomfortable, unhealthy and more susceptible to, so far, unanticipated viral and other diseases. The lack of spatial distribution and temporal variation of potential climatic parameters as well as future scenarios have given rise to serious uncertainties with respect to climate change and development of adaptation strategies for sustainable climate of the valley and beyond. An accurate assessment and gridded documentation of prevailing climate at desired resolution is instrumental. Present study has been carried out to develop a high-resolution gridded map of near surface temperature and humidity for Kathmandu valley and its immediate surrounding areas as a starting point to address the foreseen problem of climate change and adaptation. Grid based seasonal and annual distribution map of near surface temperature and humidity have been developed as predicted by numerical simulation at horizontal resolution of 1km2 with the application of WRF Modeling System initializing with NCEP meteorological and USGS land use and terrain elevation data. Model predictions agree well with the available observation data.Journal of Institute of Science and Technology, 2014, 19(1): 7-13

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44

Hashim, Shaimaa Mudhafar, and Israa Bader Al-Mashhadani. "Adaptation of powerline communications-based smart metering deployments with IoT cloud platform." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 2 (February 1, 2023): 825. http://dx.doi.org/10.11591/ijeecs.v29.i2.pp825-837.

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<span lang="EN-US">The necessity of energy management and optimization through smart devices has an essential role in sustainable energy. Smart grid features and cutting-edge technologies are progressively integrated into traditional electricity networks. One of these features is the interference between power line communications and IoT. The introduction and deployment of these grids are mainly focused on the development of the field of smart metering. A new proposed module for smart meter design within the existing infrastructure grid system using power line communication (PLC) is presented. The system will include a transmitter with a microcontroller (MCU) and numerous sensors, as well as communication channels that include PLC and an in-house powerline network, and a receiver with an MCU. The suggested system interacts with the IoT system, characterized by a free web interface showing the data directly in real-time. Based on real-world experience, this paper develops guidelines for various aspects of PLC Smart Metering network deployment via the cloud environment. The practical result of packet losses is about 0, 1, or 2 characters of received data, and the time difference between transmitter and receiver is about 5000 milliseconds for a fixed transmission line.</span>

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45

Hu, Changbin, Linshu Cheng, Shanna Luo, and Heming Yan. "Robust control-based power quality optimization strategy for inverters." Journal of Physics: Conference Series 2215, no. 1 (February 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2215/1/012002.

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Abstract A dynamic compensation control strategy based on a residual observer combined with a gradient descent optimization algorithm is proposed to address the power quality problem of distributed AC networks. The robust control structure based on the residual observer is designed according to the theory of Plug and Play control framework. The proposed structure is effective in improving the dynamic performance of the system without changing the structure and parameters. A gradient descent optimization algorithm is used to optimize the parameters of the rectified control structure online. Based on the need for fast response capability and stable performance of distributed grid systems, the gradient descent algorithm is optimized using the model matching principle, which provides a theoretical basis for the selection of initial points in the optimization algorithm. A low-order derivation form of the robust control structure is given through a step-down process to further enhance the speed of optimization under this strategy. The analysis shows that the strategy effectively enhances the robustness of the distributed grid system in comparison with the conventional double closed-loop control. Modelling simulation results verify the effectiveness of the proposed control strategy.

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46

Zhu, Chaobing, Dahai zhang, and Qingguo Fei. "Impact characteristics and strength evaluation of a slender body obliquely entering water at high speed." Journal of Physics: Conference Series 2338, no. 1 (September 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2338/1/012024.

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Abstract Based on Coupled Eulerian-Lagrangian method, the impact characteristics and strength evaluation of high speed multi-angle oblique water entry of slender body were studied. In order to solve the complex fluid-structure coupling problem and large deformation problem, the Lagrangian grid is used to divide the slender body structure grid, and the Euler grid is used to divide the fluid grid, the effectiveness of the method is verified by comparing with the experimental results. Furthermore, by simulating the impact process of multi-angle water entry, the time-history curves of acceleration, stress and strain at the head position of the slender body were analyzed, and the strength evaluation was carried out to verify the structural safety combined with the residual strength formula. The results show that there is a positive correlation between the impact load and the water inlet angle, and the larger the water inlet angle, the greater the impact load; the residual strength factor of the slender body is positive at 15° and 20°, and the structure is safe, at 30°, the residual strength factor is -0.203, and the structure is unsafe.

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47

Yang, Bingru, Qi Li, Liang Chen, Changqing Shen, and Sundararajan Natarajan. "Bearing Fault Diagnosis Based on Multilayer Domain Adaptation." Shock and Vibration 2020 (September 29, 2020): 1–11. http://dx.doi.org/10.1155/2020/8873960.

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Bearing fault diagnosis plays a vitally important role in practical industrial scenarios. Deep learning-based fault diagnosis methods are usually performed on the hypothesis that the training set and test set obey the same probability distribution, which is hard to satisfy under the actual working conditions. This paper proposes a novel multilayer domain adaptation (MLDA) method, which can diagnose the compound fault and single fault of multiple sizes simultaneously. A special designed residual network for the fault diagnosis task is pretrained to extract domain-invariant features. The multikernel maximum mean discrepancy (MK-MMD) and pseudo-label learning are adopted in multiple layers to take both marginal distributions and conditional distributions into consideration. A total of 12 transfer tasks in the fault diagnosis problem are conducted to verify the performance of MLDA. Through the comparisons of different signal processing methods, different parameter settings, and different models, it is proved that the proposed MLDA model can effectively extract domain-invariant features and achieve satisfying results.

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48

Gu, Wen, Haohui Xu, Rui Mei, Chaohang Zheng, YU Jian, and Xi Wu. "Research on Harmonic Mitigation Strategy of Static VAR Generator Based on Frequency Adaptation." Journal of Physics: Conference Series 2477, no. 1 (April 1, 2023): 012107. http://dx.doi.org/10.1088/1742-6596/2477/1/012107.

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Abstract Offshore wind farms are integrated into the power grid via AC submarine cables, which may cause severe harmonic problems and reduce the power quality of the grid. To address the problem of excessive harmonics in offshore wind power plants (WPPs), this paper proposes a method applying the static var generator (SVG) with frequency adaptive harmonic mitigation function to the offshore WPPs. Firstly, the SVG model and the mechanism of harmonic mitigation are investigated. Secondly, the proportional and resonant control strategy is proposed according to the internal model theorem and applied to the voltage control loop of SVG. The harmonic suppression performance of SVG is studied through the Bode diagram. Thirdly, to adapt to the uncertainty of harmonic frequencies, the recursive least-squares method is introduced to realize the real-time tracking of harmonic frequencies. And the harmonic compensation bandwidth of the SVG is broadened by adjusting the parameters of the controller. Finally, the offshore WPPs in Jiangsu are built based on the Matlab/Simulink platform to verify the effectiveness of the mitigation strategy. Compared with the LC filter, the improved method can effectively mitigate (inter-) harmonics and has strong robustness and adaptability.

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49

Heidary Yazdi, Seyed Saed, Jafar Milimonfared, and Seyed Hamid Fathi. "Adaptation of VSC-HVDC Connected DFIG Based Offshore Wind Farm to Grid Codes: A Comparative Analysis." International Journal of Renewable Energy Development 8, no. 1 (February 2, 2019): 91. http://dx.doi.org/10.14710/ijred.8.1.91-101.

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Lack of synchronism between VSC-HVDC (Voltage Source Converter - High Voltage Direct Current) connected offshore wind farm and onshore grid leads to immunity of wind turbines to grid contingencies. Focusing on DFIG (Doubly Fed Induction Generator) based wind farms; this paper has presented a univalent control structure based on inertial and primary frequency response in which DC link voltage is utilized as synchronization interface. Based on the presented structure, four approaches based on the communication system, frequency, voltage and combined frequency and voltage modulation are utilized and compared to inform the onshore grid status to individual wind turbines. Considering Kondurs two area power system, results have revealed that all four approaches have similar ability (with negligible error) in offering inertial and primary frequency response to improve slow network oscillations. On the other hand, voltage and combined frequency and voltage modulation approaches have the ability to satisfy Fault Ride Through (FRT) requirements thanks to superior dynamics. However, communication and frequency modulation approaches lose that ability as communication and frequency measurement delays increase respectively. It has been concluded that combined frequency and voltage modulation, as the superior approach, has advantages like minimum FRT DC voltage profile increase and deviation from operating point after the fault, the minimum imposition of electrical and mechanical stress on DFIG and preservation of prevalent control structure thanks to appropriate dissociation between slow and fast dynamics.©2019. CBIORE-IJRED. All rights reservedArticle History: Received Dec 8th 2017; Received in revised form July 16th 2018; Accepted December 15th 2018; Available onlineHow to Cite This Article: Yazdi, S.S.H., Milimonfared, J. and Fathi, S.H. (2019). Adaptation of VSC-HVDC Connected DFIG Based Offshore Wind Farm to Grid Codes: A Comparative Analysis. Int. Journal of Renewable Energy Development, 8(1), 91-101.https://doi.org/10.14710/ijred.8.1.91-101

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50

Behrends, Holger, Dietmar Millinger, Werner Weihs-Sedivy, Anže Javornik, Gerold Roolfs, and Stefan Geißendörfer. "Analysis of Residual Current Flows in Inverter Based Energy Systems Using Machine Learning Approaches." Energies 15, no. 2 (January 14, 2022): 582. http://dx.doi.org/10.3390/en15020582.

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Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

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