Grid Optimization Method Research Articles

A Two-Step Grid–Coordinate Optimization Method for a Wind Farm with a Regular Layout Using a Genetic Algorithm

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process.

State of energy estimation of lithium battery based on least squares and support vector regression

To enhance the estimation precision of lithium battery state of energy (SOE) and avoid the complex modeling and parameter identification process in the estimation process, a joint algorithm using the least square method (LS) and support vector regression (SVR) is proposed in this paper. Data on the voltage, current, temperature, and charge state of the lithium battery are extracted and incorporated into the energy factor. The lithium battery SOE estimation model is designed by the least-squares support vector regression (LS-SVR) methods, while the grid optimization method is deployed to optimize the hyper-parameters. Finally, the SOE prediction of lithium batteries under a dynamic scenario is realized. Comparison results show that the energy factor chosen in our article accurately reflects the SOE of lithium batteries, and the MAE is within 1.5%.

Phonetic Feature Extraction and Recognition Model in Korean Pronunciation Practice Based on AdaBoost

This paper proposes a phonetic feature extraction and recognition model in Korean pronunciation exercises based on AdaBoot moments. Feature extraction algorithms have a great influence on speech emotion recognition algorithms, among which Mel-frequency Cepstral Coefficients (MFCC) is the most commonly used feature in speech emotion recognition. The feature extraction method and the influence of SVM kernel function and parameter selection on the recognition result are studied, and the existing speech feature extraction algorithms and their respective advantages and disadvantages are analyzed, as well as the influence of different kernel functions, kernel parameters and penalty parameters on the recognition performance. An improved grid optimization method is used to further improve the recognition time of voice information.

Synchronization in electric power networks with inherent heterogeneity up to 100% inverter-based renewable generation.

The synchronized operation of power generators is the foundation of electric power network stability and a key to the prevention of undesired power outages and blackouts. Here, we derive the conditions that guarantee synchronization in power networks with inherent generator heterogeneity when subjected to small perturbations, and perform a parametric sensitivity analysis to understand synchronization with varied types of generators. As inverter-based resources, which are the primary interfacing technology for many renewable sources of energy, have supplanted synchronous generators in ever growing numbers, the center of attention on associated integration challenges have resided primarily on the role of declining system inertia. Our results instead highlight the critical role of generator damping in achieving a stable state of synchronization. Additionally, we report the feasibility of operating interconnected electric grids with up to 100% power contribution from inverter-based renewable generation technologies. Our study has important implications as it sets the basis for the development of advanced control architectures and grid optimization methods that ensure synchronization and further pave the path towards the decarbonization of the electric power sector.

An Overall Uniformity Optimization Method of the Spherical Icosahedral Grid Based on the Optimal Transformation Theory

The improvement of overall uniformity and smoothness of spherical icosahedral grids, the basic framework of atmospheric models, is a key to reducing simulation errors. However, most of the existing grid optimization methods have optimized grid from different aspects and not improved overall uniformity and smoothness of grid at the same time, directly affecting the accuracy and stability of numerical simulation. Although a well-defined grid with more than 12 points cannot be constructed on a sphere, the area uniformity and the interval uniformity of the spherical grid can be traded off to enhance extremely the overall grid uniformity and smoothness. To solve this problem, an overall uniformity and smoothness optimization method of the spherical icosahedral grid is proposed based on the optimal transformation theory. The spherical cell decomposition method has been introduced to iteratively update the grid to minimize the spherical transportation cost, achieving an overall optimization of the spherical icosahedral grid. Experiments on the four optimized grids (the spring dynamics optimized grid, the Heikes and Randall optimized grid, the spherical centroidal Voronoi tessellations optimized grid and XU optimized grid) demonstrate that the grid area uniformity of our method has been raised by 22.60% of SPRG grid, −1.30% of HR grid, 38.30% of SCVT grid and 38.20% of XU grid, and the grid interval uniformity has been improved by 2.50% of SPRG grid, 2.80% of HR grid, 11.10% of SCVT grid and 11.00% of XU grid. Although the grid uniformity of the proposed method is similar with the HR grid, the smoothness of grid deformation has been enhanced by 79.32% of grid area and 24.07% of grid length. To some extent, the proposed method may be viewed as a novel optimization approach of the spherical icosahedral grid which can improve grid overall uniformity and smoothness of grid deformation.

Digital and automatic design of free-form single-layer grid structures

Grid shells have been widely used in various long-span public buildings, and many of them are defined over free-form surfaces with complex boundaries. This emphasizes the importance of general and digitalised grid generation and optimization methods in the initial design stage to achieve visually sound grid shells. In this paper, a framework is presented for the development of a digital tool and to generate regular and fluent grids for structural design over free-form surfaces, especially those with complex boundaries. Both triangular and quadrilateral grid generation are addressed. To generate regular and fluent grids for free-form surfaces, a simple yet practical framework is proposed based on a spring-mass model. Firstly, an initial casual quadrilateral grid is tiled on the surface based on surface discretization and mesh parameterization. Secondly, the distribution of the initial grid vertices is adjusted by a dynamic relaxation procedure, assuming the grid as a spring-mass system. Thirdly, the grid vertices corresponding to the adjusted particles in the equilibrium state are then reconnected to produce a grid with a predefined pattern (triangular or quadrilateral). Finally, the generated grid is relaxed with the spring-mass model, alongside additional geometric operations including grid size adjustment and filtering techniques, to further improve the grid regularity and fluency. As part of its contribution, this paper also broadens the application scope of the fluency index, which can be used to quantitatively evaluate the suitability of a given triangular or quadrilateral grid for architectural and structural expression. Examples are presented and show that the proposed framework is effective for the triangular and quadrilateral grid generation over various surfaces and to optimize the resulted grids along complex boundaries. The method proposed can be useful for rapid design and performance evaluation of free-form grid structures.

Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining

Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case

A sequential hybrid method to establish practical sizing systems based on anthropometric data

PurposeSize fit and economic efficiency are two crucial aspects that need to be considered in designing a sizing system. However, there could exist a trade-off between those aspects in order to establish a practical sizing system. The purpose of this paper is to develop a sequential hybrid method of grid and optimization to generate a practical sizing system using anthropometric data.Design/methodology/approachThe proposed sequential hybrid method consisted of two sequential steps, which employs grid method and optimization method. In the initial step, the grid method creates primary grids that accommodate a designated percentage (e.g. 90%) of users with best size fit. In the subsequent step, the optimization method generated additional grids to provide acceptable fit, with minimum fit penalty scores for users unaccommodated by the primary grids. Our method was applied to the development of a sizing system for men's military jackets. The proposed method performances were evaluated in terms of accommodation percentage, size fit and number of sizing categories.FindingsOur proposed method resulted in 26 primary grids during the initial step, which cover 90% of users. Next, we generated six additional grids during the subsequent step that provide minimum fit penalty scores for the rest (10%) users.Originality/valueThe main contributions of this paper are as follows: consider accommodation percentage, size fit and number of sizing categories in the design of sizing system; combine the grid and optimization methods and evaluate a sizing system for men's military jackets. The proposed method is applicable to develop optimal sizing systems for multiple-size products.

Deep kernel learning approach to engine emissions modeling

Abstract We apply deep kernel learning (DKL), which can be viewed as a combination of a Gaussian process (GP) and a deep neural network (DNN), to compression ignition engine emissions and compare its performance to a selection of other surrogate models on the same dataset. Surrogate models are a class of computationally cheaper alternatives to physics-based models. High-dimensional model representation (HDMR) is also briefly discussed and acts as a benchmark model for comparison. We apply the considered methods to a dataset, which was obtained from a compression ignition engine and includes as outputs soot and NO x emissions as functions of 14 engine operating condition variables. We combine a quasi-random global search with a conventional grid-optimization method in order to identify suitable values for several DKL hyperparameters, which include network architecture, kernel, and learning parameters. The performance of DKL, HDMR, plain GPs, and plain DNNs is compared in terms of the root mean squared error (RMSE) of the predictions as well as computational expense of training and evaluation. It is shown that DKL performs best in terms of RMSE in the predictions whilst maintaining the computational cost at a reasonable level, and DKL predictions are in good agreement with the experimental emissions data.

A triangular grid generation and optimization framework for the design of free-form gridshells

Gridshells have been widely used in various public buildings, and many of them are defined over complex free-form surfaces with complex boundaries. This emphasizes the importance of general grid generation and optimization methods in the initial design stage to achieve visually sound and easy-to-manufacture structure. In this paper, a framework is presented to generate uniform, well-shaped and fluency triangular grids for structural design over free-form surfaces, especially those with complex boundaries. The framework employs force-based algorithms and a connectivity-regularization algorithm to optimize grid quality. First, an appropriate distribution of internal points is randomly generated on the surface. Secondly, a bubble-packing method is employed to increase the uniformity of the initial point distribution, and the points are connected using Delaunay-based triangularization to produce an initial grid with rods of balanced length. Thirdly, the grid connectivity is optimized using a range of edge-operations including edge-flip, collapse and split. The optimization process features a grid relaxation objective which includes the degree of the vertices, leading to improved regularity. As a final step, the grid is relaxed to improve fluency using a net-like method. As part of its contribution, this paper, therefore, proposes a metric for fluency, which can be used to quantitatively evaluate the suitability of a given grid for architectural and structural expression. Two case-study examples are presented to demonstrate the effective execution of the grid generation and optimization framework. It is shown that by using the proposed framework, the fluency index of the grid can be improved by up to 157%.

Economical optimization of grid power factor using predictive data

We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger &#x0028 OLTC &#x0029 and transmission lines. The system power factor &#x0028 PF &#x0029 regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method. Capacitor-only control strategy is a common photovoltaic &#x0028 PV &#x0029 regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine.

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard-Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995-10664, 7991-6661, and 4326-3999 cm-1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42-33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Condition Monitoring of an Induction Motor Stator Windings Via Global Optimization Based on the Hyperbolic Cross Points

The objective of condition monitoring of induction machines is to detect the incipient stage of a fault before serious damage occurs with high associated cost. Although the condition monitoring techniques have been intensively investigated in the last decades, research is still carried out in reducing cost and improving accuracy. This paper proposes a novel method that enables efficient and accurate monitoring of the stator winding circuit fault. The proposed method is based on the sparse grid optimization method applied in the least squares estimation of the circuit parameters that characterize the condition of a fault incipient. The kernel of the method is the efficient search for the objective function minimum on the grid created by using the hyperbolic cross points (HCPs). The system cost and complexity are minimized since the proposed method only requires voltage and current signals recorded at a machine terminal without any invasive or additional hardware circuitry. The proposed HCP algorithm is robust to supply voltage unbalance and motor loading state. The validity and effectiveness of the proposed scheme is experimentally tested on a three-phase 800-W 380-V induction motor.

The detection of epileptic seizure signals based on fuzzy entropy

BackgroundEntropy is a nonlinear index that can reflect the degree of chaos within a system. It is often used to analyze epileptic electroencephalograms (EEG) to detect whether there is an epileptic attack. Much research into the state inspection of epileptic seizures has been conducted based on sample entropy (SampEn). However, the study of epileptic seizures based on fuzzy entropy (FuzzyEn) has lagged behind. New methodsWe propose a method of state inspection of epileptic seizures based on FuzzyEn. The method first calculates the FuzzyEn of EEG signals from different epileptic states, and then feature selection is conducted to obtain classification features. Finally, we use the acquired classification features and a grid optimization method to train support vector machines (SVM). ResultsThe results of two open-EEG datasets in epileptics show that there are major differences between seizure attacks and non-seizure attacks, such that FuzzyEn can be used to detect epilepsy, and our method obtains better classification performance (accuracy, sensitivity and specificity of classification of the CHB-MIT are 98.31%, 98.27% and 98.36%, and of the Bonn are 100%, 100%, 100%, respectively). Comparisons with existing method(s)To verify the performance of the proposed method, a comparison of the classification performance for epileptic seizures using FuzzyEn and SampEn is conducted. Our method obtains better classification performance, which is superior to the SampEn-based methods currently in use. ConclusionsThe results indicate that FuzzyEn is a better index for detecting epileptic seizures effectively. The FuzzyEn-based method is preferable, exhibiting potential desirable applications for medical treatment.

Optimized Icosahedral Grids: Performance of Finite-Difference Operators and Multigrid Solver

Abstract This paper discusses the generation of icosahedral hexagonal–pentagonal grids, optimization of the grids, how optimization affects the accuracy of finite-difference Laplacian, Jacobian, and divergence operators, and a parallel multigrid solver that can be used to solve Poisson equations on the grids. Three different grid optimization methods are compared through an error convergence analysis. The optimization process increases the accuracy of the operators. Optimized grids up to 1-km grid spacing over the earth have been created. The accuracy, performance, and scalability of the multigrid solver are demonstrated.

CLASSIFICATION OF SKIN AUTOFLUORESCENCE SPECTRUM USING SUPPORT VECTOR MACHINE IN TYPE 2 DIABETES SCREENING

Advanced glycation end products (AGEs) are a complex and heterogeneous group of compounds that have been implicated in diabetes related complifications. Skin autofluorescence was recently introduced as an alternative tool for skin AGEs accumulation assessment in diabetes. Successful optical diagnosis of diabetes requires a rapid and accurate classification algorithm. In order to improve the performance of noninvasive and optical diagnosis of type 2 diabetes, support vector machines (SVM) algorithm was implemented for the classification of skin autofluorescence from diabetics and control subjects. Cross-validation and grid-optimization methods were employed to calculate the optimal parameters that maximize classification accuracy. Classification model was set up according to the training set and then verified by the testing set. The results show that radical basis function is the best choice in the four common kernels in SVM. Moreover, a diagnostic accuracy of 82.61%, a sensitivity of 69.57%, and a specificity of 95.65% for discriminating diabetics from control subjects were achieved using a mixed kernel function, which is based on liner kernel function and radical basis function. In comparison with fasting plasma glucose and HbA1c test, the classification method of skin autofluorescence spectrum based on SVM shows great potential in screening of diabetes.

Isothermal annealing of a 620 nm optical absorption band in Brazilian topaz crystals

Isothermal decay behaviors, observed at 515, 523, 562, and 693K, for an optical absorption band at 620nm in gamma-irradiated Brazilian blue topaz were analyzed using a kinetic model consisting of O− bound small polarons adjacent to recombination centers (electron traps). The kinetic equations obtained on the basis of this model were solved using the method of Runge–Kutta and the fit parameters describing these defects were determined with a grid optimization method. Two activation energies of 0.52±0.08 and 0.88±0.13eV, corresponding to two different structural configurations of the O− polarons, explained well the isothermal decay curves using first-order kinetics expected from the kinetic model. On the other hand, thermoluminescence (TL) emission spectra measured at various temperatures showed a single band at 400nm in the temperature range of 373–553K in which the 620nm optical absorption band decreased in intensity. Monochromatic TL glow curve data at 400nm extracted from the TL emission spectra observed were found to be explained reasonably by using the knowledge obtained from the isothermal decay analysis. This suggests that two different structural configurations of O− polarons are responsible for the 620nm optical absorption band and that the thermal annealing of the polarons causes the 400nm TL emission band.

Acoustic Emission Signal Classification Based On Support Vector Machine

A classification of acoustic emission signals ha s great significance. The best parameters of the RBF kernel had obtained by using grid optimization method, and the classifier had built to achieve the identification and classification of acoustic emission signals. The simulation results show that support vector machine can effectively distinguish different acoustic emission signal and noise signal. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1387

A Study on the Mathematic Model of Nox Emission in Coal-Fired Boiler with Opposed Combustion Pattern

The perfect mathematic model is critical to reseaech the NOx emission charateristic. In this paper, a new statistical learning algorithm SVM(support vector machine) was used to establish the model, based on the mechanism analysis of the NOx emission characteristics, and grid optimization method was applied to determine the model parameters. The model was tested on a 660MW power plant ,and the result indicated that SVM was a good tool for building NOx emission model and had better generalization ability and higher calculation speed comparing with BP modeling approaches.

A Comparison of Grid Quality of Optimized Spherical Hexagonal–Pentagonal Geodesic Grids

Abstract Construction and optimization methods of spherical hexagonal–pentagonal geodesic grids are investigated. The objective is to compare grid structures on common ground. The distinction between two types of hexagonal–pentagonal grids is made. Three conventional grid optimization methods are summarized. In addition, three new optimization methods are proposed. Six desirable conditions for an ideal grid are described, and the grid optimization methods are organized in view of such conditions. Interval uniformity, area uniformity, isotropy, and bisection of cell faces are systematically investigated for optimized grids. There are compensations of preferable grid features in each optimization method, and an optimal method cannot be decided based only on the research of grid features. It is suggested that grid optimization methods should be selected based on research of numerical schemes.