Grid Algorithm Research Articles

GPU-Accelerated Target Strength Prediction Based on Multiresolution Shooting and Bouncing Ray Method

The application of the traditional planar acoustics method is limited due to the low accuracy when computing the echo characteristics of underwater targets. Based on the concept of the shooting and bouncing ray which considers multiple reflections on the basic of the geometrics optics principle, this paper presents a more efficient GPU-accelerated multiresolution grid algorithm in the shooting and bouncing ray method (SBR) to quickly predict the target strength value of complex underwater targets. The procedure of the virtual aperture plane generation, ray tracing, scattered sound field integral and subdividing the divergent ray tubes are all implemented on the GPU. Particularly, stackless KD-tree traversal is adopted to effectively improve the ray-tracing efficiency. Experiments on the rigid sphere, cylinder and corner reflector model verify the accuracy of GPU-based multiresolution SBR. Besides, the GPU-based SBR is more than 750 times faster than the CPU version because of its tremendous computing capability. Further, the proposed accelerated GPU-based multiresolution SBR improves runtime performance at least 2.4 times that of the single resolution GPU-based SBR.

Retraction Note to: Design and implementation of fuzzy priority deadline job scheduling algorithm in heterogeneous grid computing

Retraction Note to: Design and implementation of fuzzy priority deadline job scheduling algorithm in heterogeneous grid computing

Data analysis and management for optimal application of an advanced ML-based fault location algorithm for low voltage grids

As the need for automatization of the electricity grid’s fault diagnosis schemes is rising, the application of technologies such as the artificial intelligence (AI) can provide practical solutions to the problem. AI can overcome the challenges that complex topologies like those of the low voltage (LV) smart grids pose and prove to be a powerful tool in the development of advanced fault diagnosis methods. An important parameter for the success of any AI-based method is the quality of data. Therefore, in this paper a data analysis is performed in order to evaluate the type of data produced by a small LV grid and an representative AI algorithm’s response to those. In the context of this analysis, the most important features and meters were identified. Furthermore, as a response to the large volume of available data, a data management strategy is proposed. The strategy combines original and reshaped features. For this purpose, five dimensionality reduction methods are tested and compared. Truncated-SVD is deemed the most appropriate and is subsequently utilized for the reshaping of the dataset that is introduced to the XGBoost fault location model. The integration of the dimensionality reduction technique in the algorithm results in the decrease of the computational time and the dataset’s size and in a higher generalizability of the algorithm. Thus, the application of the proposed method is not limited by the grid’s topology. The method’s robustness was verified against various influencing parameters such as the fault resistance, the size of the dataset, the loss of data and the photovoltaics’ penetration level. The overall algorithm achieved a mean squared error of 13.26 and a training and test accuracy of more than 99% when tested on the CIGRE LV benchmark grid.

Control and Optimization Algorithms for Lattice Power Grids With Multiple Grid-Forming Converters

The growth of renewable energies, together with their power converter interfaces, reshapes power systems into more-electronics power systems. Along with this paradigm shift is the requirement of grid formation by grid-tied power converters. However, existing grid architectures only allow parallel operation of grid-forming converters, which excludes high-voltage applications. This article proposes novel lattice power grids that combine the advantages of multilevel converters and power grids, thereby allowing both serial and parallel connectivity with modularity and scalability. Further, we propose control and optimization algorithms for lattice power grids by use of graph theory. In particular, we investigate H-bridge-based lattice power grids and achieve several objectives, including desired voltages and currents between any two selective nodes in lattice power grids as well as efficiency optimization by minimizing switching actions. To achieve these objectives, this article details control and optimization methodology for square lattice power grids. Finally, the proposed algorithms and lattice power grids are validated via simulation results.

A fully-conservative sliding grid algorithm for compressible flows using an Isogeometric Discontinuous Galerkin scheme

This work aims at developing a high-order, fully conservative, discretization of sliding grids with applications to compressible flows at different regimes (from subsonic to supersonic). The proposed approach combines a discontinuous Galerkin formulation for Navier–Stokes equations with rational representations originating from Isogeometric Analysis, which allows to design a watertight and fully conservative sliding grid algorithm. A verification exercise is first carried out to rigorously establish the convergence rate of the method. Then, the accuracy and robustness are demonstrated for a flow around a pitching ellipse at different regimes. A comparison with a grid deformation technique and a sensitivity study with respect to the location of the sliding interface are also investigated. Finally, the flow around a vertical-axis wind turbine configuration is simulated to show the potentiality of the proposed approach to deal with more complex geometries.

An enhanced fast fundamental frequency estimator for three-phase electric aircraft grid

This article proposes a robust and enhanced three-phase fundamental frequency estimation algorithm for the electric aircraft grid. Unlike conventional frequency-locked loops, the digital signal processing technique-based frequency estimator proposed relies on the storage of five consecutive samples of the fundamental grid voltage signal. Furthermore, the ability to successfully operate at a low sampling frequency, i.e. 8 kHz, makes it sufficiently attractive as regards reducing the memory storage in a low-cost real-time controller. The proposed frequency estimator can additionally eliminate the negative effects of the DC-offset and the fundamental negative sequence components present in the grid signal without any additional effort. Also, the tuning efforts are reduced for the frequency variation range of 350–900 Hz owing to the existence of a single tuning gain parameter. The proposed algorithm has also been found to have a fast dynamic response, which has been experimentally validated through the use of a real-time digital controller.

Linear/Ridge expansions: enhancing linear approximations by ridge functions

We consider approximations formed by the sum of a linear combination of given functions enhanced by ridge functions—a Linear/Ridge expansion. For an explicitly or implicitly given objective function, we reformulate finding a best Linear/Ridge expansion in terms of an optimization problem. We introduce a particle grid algorithm for its solution. Several numerical results underline the flexibility, robustness and efficiency of the algorithm. One particular source of motivation is model reduction of parameterized transport or wave equations. We show that the particle grid algorithm is able to find a Linear/Ridge expansion as an efficient nonlinear model reduction.

Precise ROCOF estimation algorithm for low inertia power grids

The precise estimation of Rate Of Change Of Frequencies (ROCOFs) in a generation trip event can be helpful on power system inertia estimation, fast system response, and accurate event capturing. However, the ROCOF estimations from the existing Synchronized Measurement Devices (SMDs) are usually simply calculated by the finite difference between two adjacent frequency measurement points. The ineluctable noises, disturbances, and spikes from real-world frequency measurements can bring large dynamics to the ROCOF estimation and thus can result in an inaccurate estimation of the initial ROCOF. This issue becomes more serious when the target power grid has low inertia and a large amount of distributed energy sources are deployed. To address this issue, a precise ROCOF estimation algorithm is designed based on least square method with flexible window size. In addition, a median filter is also designed and applied on the frequency measurements before using the proposed algorithm. The window size and thresholds in the proposed algorithm are determined with historical event data analysis. The proposed algorithm is deployed in a low cost, flexible, and distribution level universal grid analyzer (UGA) platform. Multiple experiments are conducted in both a laboratory and the Hawaiian Islands to verify the effectiveness of the proposed algorithm.

Development of a modification of the method for constructing energy-efficient sensor networks using static and dynamic sensors

Due to the widespread use of sensors and sensor networks in the tasks of territory coverage, the relevant criteria are maximizing coverage and minimizing energy consumption. At the same time, the compliance of the network with these criteria is an urgent problem in the modern technological world. A modification of the method for constructing energy-efficient sensor networks is proposed by introducing an additional criterion for minimizing the number of sensors and limiting the number of sensors used, which allows reducing the energy consumption of sensor networks by 19 %. In the resulting optimization problem, the optimality criteria are the functions of minimizing the area of uncovered territory, the value of energy consumption, and the number of sensors. The optimum solution is formed by pairs of values of the coverage radius and the level of intersection of the coverage areas, which provide maximum coverage while minimizing energy consumption and the number of sensors used. To solve the problem, the parameter convolution method and the genetic algorithm were used. In the case of dynamic sensors, the problem is to find such a trajectory of the sensor that provides the maximum flyby of the territory with a minimum length. A grid algorithm is proposed to find the necessary trajectory. The presented algorithm consists in dividing the territory into nodes and estimating the value of the covered territory by the sensor in this node. After the formation of estimates, the search for a Hamiltonian path was used. The case of a multiply connected territory with the possibility of turning it into a simply connected one is considered. A scheme for finding the parameters of energy-efficient coverage of the territory using static and dynamic sensors is proposed.

PI Optimized Cuckoo Search Algorithm for Single Phase Grid Tied PV Inverter with SEPIC Converter

A new approach for single phase grid interconnected PV module with SEPIC converter is introduced in this paper. As the outcome of PV is low voltage DC, a suitable converter is needed to boost the voltage. Hence SEPIC converter is used as it has high voltage gain with same polarity in the input as well as output side. A closed loop control is executed with a PI controller tuned with Cuckoo search (CS) algorithm. As the traditional tuning of PI controller results in peak overshoot problems, Cuckoo search (CS) based Optimization is utilized as it is simple with has a smaller number of tuning parameters. This output is being fed to the grid through a single phase VSI and the grid synchronization is accomplished by PI controller by analogizing the actual and reference values of power. Thus, the proposed control strategy is verified through MATLAB and it is observed that the source current THD is minimized, which satisfies the IEEE standard.

Miniaturized Distributed Generation for a Micro Smart Grid Simulator

In this paper, a miniaturization method is proposed for developing micro distributed generation for a micro smart grid simulator. The micro smart grid simulator is a fault simulator that was built to test and verify the new operation control algorithms for smart grids in the laboratory and has a size downscaled to one-thousandth of that of an actual smart grid. The micro distributed generation was designed in a multi-layered structure (dimension: 13 × 20 cm2), in which each function is implemented in several layers, to satisfy the size requirements. Next, the grid synchronization and PQ control algorithms required for the distributed generation were developed. A three-phase 19 V power system was built, and a 19 V–7.5 W three-phase micro distributed generation was realized through experimental verification. In addition, by verifying the effectiveness through grid synchronization and 7.5 W PQ control experiments, it was confirmed that the micro distributed generation based on the proposed miniaturization method can be implemented in a micro smart grid simulator.

Quantune: Post-training quantization of convolutional neural networks using extreme gradient boosting for fast deployment

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision representation is converted to a lower bit representation. To overcome problems such as sensitivity of the training dataset, high computational requirements, and large time consumption, post-training quantization methods that do not require retraining have been proposed. In addition, to compensate for the accuracy drop without retraining, previous studies on post-training quantization have proposed several complementary methods: calibration, schemes, clipping, granularity, and mixed-precision. To generate a quantized model with minimal error, it is necessary to study all possible combinations of the methods because each of them is complementary and the CNN models have different characteristics. However, an exhaustive or a heuristic search is either too time-consuming or suboptimal. To overcome this challenge, we propose an auto-tuner known as Quantune, which builds a gradient tree boosting model to accelerate the search for the configurations of quantization and reduce the quantization error. We evaluate and compare Quantune with the random, grid, and genetic algorithms. The experimental results show that Quantune reduces the search time for quantization by approximately 36.5× with an accuracy loss of 0.07–0.65% across six CNN models, including the fragile ones (MobileNet, SqueezeNet, and ShuffleNet). To support multiple targets and adopt continuously evolving quantization works, Quantune is implemented on a full-fledged compiler for deep learning as an open-sourced project.

Coherence‐based automatic power factor correction (APFC) algorithm for power grids

A power factor correction (PFC) plays a major role towards the improvement of power systems quality and stability, as well as protection and control systems performance. This paper presents a novel method for automatic power factor correction (APFC) based on the coherence approach. In this method, the original power factor and the required reactive power compensation can be determined using the cross-coherence coefficients evaluated between voltage and current signals. Beside auto-coherence coefficients are useful for detecting the fault/unbalance conditions in power systems. The proposed algorithm prevents the PFC process in the events of over-compensation and abnormal events. In addition, new proposed operating characteristics for PFC, based on the coherence coefficient limits, are developed. Extensive simulation results of the proposed method are included and its accuracy and feasibility can be verified. Compared with the conventional PFC, the coherence-based PFC is easy to control, and the power factor is nearer to unity can be obtained, where its performance accuracy is greater than 99.4%. The proposed method is characterized by being simple, fast and reliable, and it can be implemented practically. Its sensitivity and security is controllable via selecting coherence setting values.

Numerical simulation of magnetic fluid thermal coupling of cylindrical heat source based on LBM lattice algorithm

In order to improve the accuracy of numerical simulation of magnetic fluid thermal coupling of cylindrical heat source, a numerical simulation method of magnetic fluid thermal coupling of cylindrical heat source based on LBM grid algorithm was studied. Based on the theoretical model of natural convection and radiative heat dissipation of cylindrical heat source, the boundary conditions were set, and the thermal coupling performance of flow field and temperature field in the process of natural convection and radiative heat dissipation of cylindrical heat source was simulated by LBM grid algorithm. Simulation results show that the relative error between simulation results and experimental results is less than 1.5%. The results show that this method can accurately simulate the magnetofluid-thermal coupling of cylindrical heat source. With the increase of Rayleigh number, different numerical solutions appear: steady state solution when Rayleigh number is 105, periodic oscillation solution when Rayleigh number is 106, chaotic solution when Rayleigh number is 107.

FDI attack detection using extra trees algorithm and deep learning algorithm-autoencoder in smart grid

Today's smart grid (SG) combines the classical power system with the information technology, leading to a cyber-physical system (CPS). Its strong dependencies on digital communication technology bring up new vulnerabilities. Among the existing cyber-attacks, false data injection (FDI) attack is targeted at compromising power system state estimation by injecting false data into meter measurements. Such a malicious attack cannot be identified by the traditional bad data detection (BDD) techniques. According to this problem, finding a way to detect this kind of attack is necessary. Therefore, to overcome this problem, extremely randomized trees algorithm is proposed in this paper because of its high accuracy and fastness compared with other algorithms like support vector machine (SVM), random forest, and k-nearest neighbor (KNN). It is evident that as the system size increases, the computational complexity increases. Thus, a stacked autoencoder is designed along with extremely randomized trees classifier to tackle with dimensionality issue. Autoencoder is a deep neural network which can present a new representation of data in lower dimension. Performance evaluation on the standard IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and 118-bus systems verifies that the proposed model outperforms other algorithms in the literature by improving the detection accuracy and reducing false positive rate.

The projection point geodesic grid algorithm for meshing the sphere

Geodesic grids are used in geophysics, meteorology, mapping and geodesic domes. We describe a new algorithm to create a geodesic grid using slicing planes from a projection point to slice an icosahedral section of a sphere into a quasi-uniform grid of triangles with near equal area. The new algorithm is an improvement compared to a frequently used classical system that is based on bisecting the ribs of an icosahedron. The area variation of the triangles is 0.37% for the new system, compared to 20.66% for the classical bisecting system. The complexity of constructing the algorithm and finding a triangle at a specific coordinate remains low, which makes it suitable for parallel processing. In addition, the algorithm can be used to construct a 3D spherical solid mesh, using tetrahedrons and octahedrons, with minimal variations in volume and shape. This is particularly useful for geophysical simulators.

Data Analysis and Management for Optimal Application of an Advanced Ml–Based Fault Location Algorithm for Low Voltage Grids

Data Analysis and Management for Optimal Application of an Advanced Ml–Based Fault Location Algorithm for Low Voltage Grids

A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

Оценка накладных расходов при выполнении расчётов на локально измельчаемых сетках

A method for presenting grid data and algorithms for their processing when performing calculations on locally refined adaptive grids is considered. Estimates are obtained of the dependence of the number of operations and computational overheads on such parameters as the size of the base regular grid, the depth of refinement, and the number of processors. Algorithms for dynamic load balancing of processors, which provide a low level of overhead costs, are considered.

Differential Privacy-Based Location Protection in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a location-based outsourcing service whereby SC-server allocates tasks to workers with mobile devices according to the locations outsourced by requesters and workers. Since location information contains individual privacy, the locations should be protected before being submitted to untrusted SC-server. However, the encryption schemes limit data availability, and existing differential privacy (DP) methods do not protect the tasks’ location privacy. In this paper, we propose a differential privacy-based location protection (DPLP) scheme, which protects the location privacy of both workers and tasks, and achieves task allocation with high data utility. Specifically, DPLP splits the exact locations of both workers and tasks into noisy multi-level grids by using adaptive three-level grid decomposition (ATGD) algorithm and DP-based adaptive complete pyramid grid (DPACPG) algorithm, respectively, thereby considering the grid granularity and location privacy. Furthermore, DPLP adopts an optimal greedy algorithm to calculate a geocast region around the task grid, which achieves the trade-off between acceptance rate and system overhead. Detailed privacy analysis demonstrates that our DPLP scheme satisfies <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -differential privacy. The extensive analysis and experiments over two real-world datasets confirm high efficiency and data utility of our scheme.