A Disturbance Localization Method for Power System Based on Group Sparse Representation and Entropy Weight Method

This paper addresses the problem of complex and challenging disturbance localization in the current power system operation environment by proposing a disturbance localization method for power systems based on group sparse representation and entropy weight method.Three different electrical quantities are selected as observations in the compressed sensing algorithm.The entropy weighting method is employed to calculate the weights of different observations based on their relative disturbance levels.Subsequently,by leveraging the topological information of the power system and pre-designing an overcomplete dictionary of disturbances based on the corresponding system parameter variations caused by disturbances,an improved Joint Generalized Orthogonal Matching Pursuit(J-GOMP)algorithm is utilized for reconstruction.The reconstructed sparse vectors are divided into three parts.If at least two parts have consistent node identifiers,the node is identified as the disturbance node.If the node identifiers in all three parts are inconsistent,further analysis is conducted considering the weights to determine the disturbance node.Simulation results based on the IEEE 39-bus system model demonstrate that the proposed method,utilizing electrical quantity information from only 8 measurement points,effectively locates disturbance positions and is applicable to various disturbance types with strong noise resistance.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

Estimating Hansen solubility parameters of organic pigments by group contribution methods

The Hansen solubility parameters(HSP)are frequently used for solvent selection and characterization of polymers,and are directly related to the suspension behavior of pigments in solvent mixtures.The performance of currently available group contribution(GC)methods for HSP were evaluated and found to be insufficient for computer-aided product design(CAPD)of paints and coatings.A revised and,for this purpose,improved GC method is presented for estimating HSP of organic compounds,intended for organic pigments.Due to the significant limitations of GC methods,an uncertainty analysis and parameter confidence intervals are provided in order to better quantify the estimation accuracy of the proposed approach.Compared to other applicable GC methods,the prediction error is reduced significantly with average absolute errors of 0.45 MPa^(1/2),1.35 MPa^(1/2),and 1.09 MPa^(1/2) for the partial dispersion(δD),polar(δP)and hydrogen-bonding(δH)solubility parameters respectively for a database of 1106 compounds.The performance for organic pigments is comparable to the overall method performance,with higher average errors forδD and lower average errors forδP andδH.

Solvent Selection for Extractive Distillation by the Universal Quasichemical Functional Group Activity Coefficient(UNIFAC) Group Contribution Method

A method to select solvent for extractive distillation is proposed by UNIFAC group contribution. Solventselectivity can be divided into two parts: the partial combinatorial solvent selectivity and the partial residual solventselectivity. The properties of partial combinatorial and residual solvent selectivity are demonstrated. In most cases,the partial residual solvent selectivity is predominant. The candidate groups of solvent can be selected by groupinteraction parameter using UNIFAC group interaction parameter table as a guide.

Evaluation of working fluids for organic Rankine cycles using group-contribution methods and second-law-based models

The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.

Position Group Contribution Method for Predicting the Normal Boiling Point of Organic Compounds

A new position group contribution model is proposed for the estimation of normal boiling data of organic compounds involving a carbon chain from C2 to C18.The characteristic of this method is the use of position distribution function.It could distinguish most of isomers that include cis-or trans-structure from organic compounds.Contributions for hydrocarbons and hydrocarbon derivatives containing oxygen,nitrogen,chlorine,bromine and sulfur,are given.Compared with the predictions,results made use of the most common existing group contribution methods,the overall average absolute difference of boiling point predictions of 417 organic compounds is 4.2 K;and the average absolute percent derivation is 1.0%,which is compared with 12.3 K and 3.2% with the method of Joback,12.1 K and 3.1% with the method of Constantinou-Gani.This new position contribution groups method is not only much more accurate but also has the advantages of simplicity and stability.

Molecular reconstruction model based on structure oriented lumping and group contribution methods

Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.

Estimation of Acentric Factor of Organic Compounds with Corresponding States Group Contribution Method

A new estimation method was proposed by combining the corresponding state principle with the group contribution method through introducing the concept of assumed-critical properties. Combining it with the Reidel equation, a new acentric factor correlation equation (CSGC-Reidel) was developed. Contribution values of 70 groups were obtained by correlating acentric factor data of 228 organic compounds of 14 type substances including saturated hydrocarbons, unsaturated hydrocarbons, cyclanes, aromatics, oxygen compounds, nitrogen compounds,halohydrocarbons, etc. The average error of acentric factor is 3.52% between the literature data and the predicated with the new estimation method.

Position Group Contribution Method for Estimation of Melting Point of Organic Compounds

A new method is proposed based on the position group contribution additivity for the prediction of melting points of covalent compounds. The characteristics of this method are the use of position distribution func-tion, which could distinguish between most isomers including cis or trans structure of organic compounds. Contri-butions for hydrocarbons and hydrocarbon derivatives containing oxygen, nitrogen, chlorine, bromine and sulfur, are given. Results are compared with those by the most commonly used estimating methods. The average derivation for prediction of normal melting temperature of 730 compounds is 14.46 K, compared to 29.33 K with the method of Joback, and 27.81 K with the method of Constantinou-Gani. The present method is not only more accurate, but also much simpler and more stable.

Estimation of Octanol-Water Partition Coefficient of Chloride Hydrocarbon by Group Contribution Method

A novel method named two-level group contribution （GC-K） method for the estimation of octanol-water partition coefficient （Kow） of chloride hydrocarbon is presented. The equation includes only normal boiling points and molecular weight of compounds. Group contribution parameters of 12 first-level groups and 7 second-level groups for Kow are obtained by correlating experimental data of three types including 57 compounds. By comparing the estimation results of the first-level with that of the two-level groups, it was observed that the latter is better with the addition of the modification of proximity effects. When compared with Marrero＇s three-level group contribution approach and atom-fragment contribution method （AFC）, the accuracy of the average relative error of GC-K by first-level groups is 7.20% and is preferred to other methods.

A Group Contribution Method for the Correlation of Static Dielectric Constant of Ionic Liquids

Static dielectric constant is a key parameter to estimate the electro-viscous effect which plays important roles in the flow and convective heat transfer of fluids with ions in microfluidic devices such as micro reactors and heat exchangers.A group contribution method based on 27 groups is developed for the correlation of static dielectric constant of ionic liquids in this paper.The ionic liquids considered include imidazolium,pyridinium,pyrrolidinium,alkylammonium,alkylsulfonium,morpholinium and piperidinium cations and various anions.The data collected cover the temperature ranges of 278.15-343.15 K and static dielectric constant ranges of 9.4-85.6.The results of the method show a satisfactory agreement with the literature data with an average absolute relative deviation of 7.41%,which is generally of the same order of the experimental data accuracy.The method proposed in this paper provides a simple but reliable approach for the prediction of static dielectric constant of ionic liquids at different temperatures.

PREDICTION OF THE TEMPERATURE DEPENDENCE OF LIQUID DIFFUSION COEFFICIENTS AT INFINITE DILUTION BY A GROUP CONTRIBUTION METHOD

1 INTRODUCTIONSince experimental determination of the mutual liquid diffusion coefficients atinfinite dilution is rather complicated,it is important,therefore,to be able to esti-mate the coefficients under various temperatures from one available coefficient ata certain temperature such as 298K.Empirical equations for this purpose are avail-able in many forms.Typical examples are the Wilke-Chang equation and the Tynequation,given

A symmetric substructuring method for analyzing the natural frequencies of conical origami structures

Conical origami structures are characterized by their substantial out-of-plane stiffness and energy-absorptioncapacity.Previous investigations have commonly focused on the static characteristics of these lightweight struc-tures.However,the efficient analysis of the natural vibrations of these structures is pivotal for designing conicalorigami structures with programmable stiffness and mass.In this paper,we propose a novel method to analyzethe natural vibrations of such structures by combining a symmetric substructuring method(SSM)and a gener-alized eigenvalue analysis.SSM exploits the inherent symmetry of the structure to decompose it into a finiteset of repetitive substructures.In doing so,we reduce the dimensions of matrices and improve computationalefficiency by adopting the stiffness and mass matrices of the substructures in the generalized eigenvalue analysis.Finite element simulations of pin-jointed models are used to validate the computational results of the proposedapproach.Moreover,the parametric analysis of the structures demonstrates the influences of the number of seg-ments along the circumference and the radius of the cone on the structural mass and natural frequencies of thestructures.Furthermore,we present a comparison between six-fold and four-fold conical origami structures anddiscuss the influence of various geometric parameters on their natural frequencies.This study provides a strategyfor efficiently analyzing the natural vibration of symmetric origami structures and has the potential to contributeto the efficient design and customization of origami metastructures with programmable stiffness.

Estimation of Formation Enthalpies of Organic Pollutants from a New Structural Group Contribution Method

Chemical stability and reactivity of organic pollutants is dependent to their formation enthalpies. The main objective of this study is to provide simple straightforward strategy for prediction of the formation enthalpies of wide range organic pollutants only from their structural functional groups. Using such an extended dataset cornprising 1694 organic chemicals from 77 diverse material classes benefits the generalizability and reliability of the study. The new suggested collection of 12 functional groups and a simple linear regression lead to promising statis- tics of R2= 0.958, Q2 =0.956, and AEE= 57 kJ.mo1-1 for the whole dataset. Moreover, unknown experimental formation enthalpies for 27 organic pollutants are estimated by the presented approach. The resultant model needs no technical software/calculations, and thus can be easily applied by a non-specialist user.

A new group contribution-based method for estimation of flash point temperature of alkanes

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is constructed.Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component,which is in line with the concept of group contribution method in thermodynamics.To realize this,a Lewis-mode group contribution method(LGC)has been developed in this work,which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework(LGC-msBNN-EA).Thus,different optical properties of molecules are afforded accurately and efficientlyby using only a small data set for training.Moreover,by employing the EA model designed specifically for LGC,structural search is well achievable.The origins of the satisfying performance of the framework are discussed in detail.Considering that such a framework combines chemical principles and data-driven tools,most likely,it will be proven to be rational and efficient to complete mission regarding structure design in related fields.

Grouping response method for equivalent static wind loads based on a modified LRC method

Wind loading is one of the most important loads for controlling the design of large-span roof structures. Equivalent static wind loads, which can generally aim at determining a specific response, are widely used by structural designers. A method for equivalent static wind loads applicable to multi-responses is proposed in this paper. A modified load- response-correlation （LRC） method corresponding to a particular peak response is presented, and the similarity algorithm implemented for the group response is described. The main idea of the algorithm is that two responses can be put into one group if the value of one response is close to that of the other response, when the structure is subjected to equivalent static wind loads aiming at the other response. Based on the modified LRC, the grouping response method is put forward to construct equivalent static wind loading. This technique can simultaneously reproduce peak responses for some grouped responses. To verify its computational accuracy, the method is applied to an actual large-span roof structure. Calculation results show that when the similarity of responses in the same group is high, equivalent static wind loads with high accuracy and reasonable magnitude of equivalent static wind distribution can be achieved.

Extended Group Foliation Method and Functional Separation of Variables to Nonlinear Wave Equations

Generalized functional separation of variables to nonlinear evolution equations is studied in terms of the extended group foliation method, which is based on the Lie point symmetry method. The approach is applied to nonlinear wave equations with variable speed and external force. A complete classification for the wave equation which admits functional separable solutions is presented. Some known results can be recovered by this approach.

Limit analysis of vertical anti-pulling screw pile group under inclined loading on 3D elastic-plastic finite element strength reduction method

Based on the functional theory, catastrophe theory, simultaneity principle and the idea of strength reduction method （SRM）, the bearing capacity functional anti SRM of pile group foundation were established, and the criteria of ultimate load and the concept of safety storage coefficient （Css） were advanced. The inclined ultimate loads by the static loading test, load increment method （LIM） and SRM are compared. Theoretically, the ultimate load of piles does not change with the loading levels when it is calculated by SRM. When the one strength reduction parameter is applied in the calculation boundary, there are calculating errors because the bearing capacity action of soils happened in the finite zone. The inclined 10adings are 108, 132 and 144 kN, and SSC are 1.07, 0.94 and 0.79, respectively, so the calculation values of ultimate loads are about 115.56, 124.08 and 113.76 kN, respectively. The error between calculations and observation values is less than 6%. But .the error between calculations of LIM and observations is 20%. Because of the effect of inclined loading, the push-rotation phenomenon of screw pile group appears. Under this testing, the ultimate bearing capacity of piles is mostly determined by the horizontal ultimate bearing capacity, and the effect of the vertical component of inclined load should also be considered.