A Novel Hybrid Approach for Optimal Placement of Non-Dispatchable Distributed Generations in Radial Distribution System

The objective of the present paper is to study the optimum installation of Non-dispatchable Distributed Generations (NDG) in the distribution network of given sizes under the given scheme. The uncertainty of various random (uncertain) parameters like load, wind and solar operated DG besides uncertainty of fuel prices has been investigated by the three-point estimate method (3-PEM) and Monte Carlo Simulation (MCS) based methods. Nearly twenty percent of the total number of buses are selected as candidate buses for NDG placement on the basis of system voltage profile to limit the search space. Weibull probability density function (PDF) is considered to address uncertain characteristics of solar radiation and wind speed under different scenarios. Load uncertainty is described by Standard Normal Distribution Function (SNDF). To investigate the solution of optimal probabilistic load flow (OPLF) three-point PEM-based technique was applied. For optimization, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and GA-PSO hybrid-based Artificial Intelligent (AI) based optimization techniques are employed to achieve the optimum value of the multi-objectives function. The proposed multi-objective function comprises loss and different costs. The proposed methods have been applied to IEEE 33- bus radial distribution network. Simulation results obtained by these techniques are compared based on loss minimization capability, enhancement of system bus voltage profile and reduction of cost and fitness functions. The major findings of the present study are the PEM-based method which provides almost similar results as MCS based method with less computation time and as far as loss minimization capacity, voltage profile improvement etc. is concerned, the hybrid-based optimization methods are compared with GA and PSO based optimization techniques.

Reliability Analysis of Hydrodynamic System for Robot Configuration

The failure tree and J-M model method are lack of analysis of the importance of each component model, which leads to the low reliability of the analysis results. In view of this problem, a Monte Carlo method based on the shape of the English long-distance robot is proposed. In view of the configuration of the robot, the realization process of the robot shape fluid dynamics system is analyzed. The frequency of accident is determined by Monte Carlo simulation, which is used as the reliability index of the system. In MATLAB, the reliability of the shape fluid dynamic system of robot is analyzed by Monte Carlo method. The system importance name and parameters are determined. The parameter conforms to the statistical function of random variables of each corresponding probability distribution function. According to the parameters, the function of the structure is established. The system is divided into reliable state, failure state and limit state with 0 as the dividing point, and the actual failure probability of the system is calculated. The numerical solution of log domain is simulated by the method of statistical calculation of random variables, and the actual failure probability is expressed by normal distribution function. The experimental results show that the actual failure probability of the method is lower than 5% under any working load, and the reliability of the analysis results is high.

Generalized Methodology to Develop Mechanistically Informed Asphalt Mixture Layer Coefficients for AASHTO 1993 Pavement Design Approach

This paper presents a generalized framework for determining mechanistically informed layer coefficients (a-values) for asphalt mixtures in the AASHTO empirical pavement design approach. The layer coefficients influence the layer thicknesses and consequently the structural capacity of pavements. Therefore, it is critical to determine reliable mechanistically informed a-values. A set of 18 commonly used asphalt mixtures in New Hampshire was selected for investigation including different types of hot mix and cold central plant recycled mixtures that are used as wearing, binder, and base course layers. Laboratory characterization was conducted using the complex modulus, semi-circular bend, and direct tension cyclic fatigue testing methods. The mixtures were evaluated using three performance index parameters: complex modulus rutting index parameter, rate-dependent cracking index parameter, and a new continuum damage parameter ([Formula: see text]). The measured field performance of wearing course mixtures in terms of International Roughness Index was used to back-calculate the in situ performance-based layer coefficients (aIRI-values). Using a normal distribution function, the results from performance testing were incorporated with the aIRI-values to develop mechanistically informed mix-specific layer coefficients. In addition, a typical layer coefficient at specific reliability levels for each mix category including hot mix wearing course, hot mix binder and base course as well as cold central plant recycled mix course are proposed for New Hampshire. The recommended a-values are 0.48 for hot mix wearing, 0.41 for hot mixed binder and base, and 0.28 for cold recycled base mixtures; these are approximately 25% higher than the currently used a-values in New Hampshire.

Method of estimating the effective zone induced by rapid impact compaction

This paper proposes a method for estimating the effective zone, including effective depth and effective range of compaction degree, from rapid impact compaction (RIC) on sand layer whose fines content is less than 10%. The proposed method utilizes a string of microelectromechanical system accelerometers to monitor the acceleration at various depths and propagation distances during compaction. To interpret and extract useful information from monitored data, peak-over-threshold (POT) processing and normal distribution function were used to analyze the recorded acceleration. The mean and standard deviation of the threshold peak acceleration were used to evaluate the effective depth and the effective range of compaction degree during RIC compaction. Moreover, spatial contours were used to determine the correlation of the threshold peak acceleration against depth and propagation distance from the RIC impact point. These contours help indicating the distribution of the effect zone after compaction. Lastly, a proposed method is suggested for frequent use in trial tests to quickly determine RIC’s required depth and impact spacing.

A Prediction Method for Postconstruction Settlement of Pile-Soil Composite Subgrade Based on Fuzzy Comprehensive Evaluation

This paper presents a postconstruction settlement prediction method for pile-soil composite subgrade based on the multilevel fuzzy comprehensive evaluation principle. In this method, the variation range of postconstruction settlement can be obtained from a simple calculation based on the basic data of actual engineering. Firstly, according to the characteristics of influencing factors in the construction of soft soil subgrade, the evaluation index set and two-level factor index sets were selected. The grading standards of the evaluation index and factor index were determined according to the allowable value of the standard and the numerical simulation results. Secondly, each factor index was standardized, and the normal distribution function in the form of exponential was used to construct the standard membership function for the first and second factor indexes. Finally, the comprehensive evaluation matrix of postconstruction settlement of composite subgrade was constructed based on the entropy weight method. The variation range of postconstruction settlement was predicted by the principle of maximum membership. The example analysis shows that the predicted results of the prediction method and the field measurement method are in good agreement, indicating that the proposed method can realize the postconstruction settlement prediction of composite subgrade, and the results are more accurate and more instructive.

Copulaesque Versions of the Skew-Normal and Skew-Student Distributions

A recent paper presents an extension of the skew-normal distribution which is a copula. Under this model, the standardized marginal distributions are standard normal. The copula itself depends on the familiar skewing construction based on the normal distribution function. This paper is concerned with two topics. First, the paper presents a number of extensions of the skew-normal copula. Notably these include a case in which the standardized marginal distributions are Student’s t, with different degrees of freedom allowed for each margin. In this case the skewing function need not be the distribution function for Student’s t, but can depend on certain of the special functions. Secondly, several multivariate versions of the skew-normal copula model are presented. The paper contains several illustrative examples.

Approximating the Normal Sample Median Distribution in Process Control

The present work aims to propose an approximation of the sample median distribution with a normal parent distribution. Although the mean is usually used as the central tendency measure for normal samples, the median has also been used in engineering, process control in particular. The proposed method approximates the normal sample median distribution only using the normal distribution function. It outperforms Castagliola’s method for small samples and serves as an alternative approximation for trading off accuracy against computational complexity for large samples.

Distributions of Particle Sizes in Black Soil and Their Environmental Significance in Northeast China

In recent years, black soil has decreased and degenerated heavily due to complicated functions of natural and artificial factors. Hence, characterizing distributions of particle sizes in black soil and their environmental influencing factors is important for understanding black soil degradation. A total of 116 surface soil samples in the top 20 cm from a typical black soil region in northeastern China were collected, and the spatial distribution of particle size parameters were characterized. Particle size-sensitive components were extracted quantitatively using the log-normal distribution function, and their environmental implications were investigated. The contents of black soil mechanical composition ranged from 7.8% to 79.3% for clay, 17.7% to 80.3% for silt, and 0% to 73.7% for sand, respectively. Median particle size ranged from 1.71 to 142.67 μm, with a coefficient of variation of 60%, indicating silt accounted for the majority of the composition. Four environmentally sensitive components were identified, including long-distance transported airborne deposits of clay dust (C1), successions from local parent materials (C2), short-distance deposits of silt particles (C3), and a component strongly disturbed by human activities (C4). C1 and C2 had relatively low variations, with C1 exhibiting the smallest variation, and C2 contributing highest proportion, showing no significant differences across all samples. C3 widely existed across samples, suggesting common wind erosion within the black soil region. C3 and C4 varied spatially, which was caused by the low vegetation coverage and high human disturbance of agricultural topsoil. The results suggest that windbreaks should be encouraged to reduce wind erosion in the black soil regions.

Investments in Capital Asset at the Regions of Russia in 2019

The purpose of the study. It is known that the further development of the Russian regions requires significant investment in all areas of activity. Therefore, the problem of assessing the existing indicators of investment activity, characteristic of each of the regions, is put forward as an urgent one. At the same time, given the wide variety of Russian regions, a comparative analysis of absolute investment volumes is not appropriate. In this regard, we suggest using a comparison of specific indicators for the analysis. The purpose of our study is to assess the levels of specific investment in capital asset per capita in all regions of our country.Materials and methods. The study used the methodological approach proposed by the author, based on the consideration of specific indicators describing investment activity in the regions of Russia. The study included four stages. As initial information, we considered the official statistics provided on the ROSSTAT website, which characterize investments in the regions, as well as the number of their population in 2019. The study conducted a cluster analysis, as well as economic and mathematical modeling of the distribution of the considered indicators by the regions of the country.Results. The cluster analysis allowed us to identify five clusters that unite the regions of Russia with similar values of specific investments per inhabitant of the corresponding region. The first cluster includes four regions, the second cluster - five regions, the third cluster - thirteen regions, the fourth cluster - twenty-six regions and the fifth cluster - thirty-four regions. The cluster analysis showed that in nine regions in 2019, there was a high level of investment, due to the tasks of their strategic development to solve federal problems. For 73 regions there were relatively low values of specific investment, the distribution of empirical data was modeled using the normal distribution function.Conclusion. The scientific novelty of the study is related to the cluster analysis and the study of the distribution of specific investments by region. Regions with high and low values of specific investments in capital asset were identified. It is proved that the values of specific investments have a significant differentiation across the regions of the country. The results of our work have a certain theoretical and practical significance for the government, regional and local authorities. The methodological approach to assessing the level of investment presented in the article can be used in further research.