Research on Mixed Logic Dynamic Modeling and Finite Control Set Model Predictive Control of Multi-Inverter Parallel System

Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.

Research on AC Electronic Load with Energy Recovery Based on Finite Control Set Model Predictive Control

Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.

Density-based rough set model for hesitant node clustering in overlapping community detection

Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years.A notion of hesitant node（HN） is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure.However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model（DBRSM） is proposed by combining the virtue of densitybased algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further ＂growth＂ of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.

Model Predictive Control-Based Direct Torque Control for Matrix Converter-Fed Induction Motor with Reduced Torque Ripple

To reduce the torque ripple in motors resulting from the use of conventional direct torque control(DTC),a model predictive control(MPC)-based DTC strategy for a direct matrix converter-fed induction motor is proposed in this paper.Two new look-up tables are proposed,these are derived on the basis of the control of the electromagnetic torque and stator flux using all the feasible voltage vectors and their associated switching states.Finite control set model predictive control(FCS-MPC)has then been adopted to select the optimal switching state that minimizes the cost function related to the electromagnetic torque.Finally,the experimental results are shown to verify the reduced torque ripple performance of the proposed MPC-based DTC method.

Comprehensive Evaluation Model of Reservoir Operation Based on Improved Set Pair Analysis

A comprehensive evaluation model based on improved set pair analysis is established. Considering the complexity in decision-making process, the model combines the certainties and uncertainties in the schemes, i.e., identical degree, different degree and opposite degree. The relations among different schemes are studied, and the traditional way of solving uncertainty problem is improved. By using the gray correlation to determine the difference degree, the problem of less evaluation indexes and inapparent linear relationship is solved. The difference between the evaluation parameters is smaller in both the fuzzy comprehensive evaluation model and fuzzy matter-element method, and the dipartite degree of the evaluation result is unobvious. However, the difference between each integrated connection degree is distinct in the improved set pair analysis. Results show that the proposed method is feasible and it obtains better effects than the fuzzy comprehensive evaluation method and fuzzy matter-element method.

Finite Control Set Model Predictive Current Control of a Five-Phase PMSM with Virtual Voltage Vectors and Adaptive Control Set

This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set,virtual voltage vectors are adopted.As the third current harmonics can be much reduced by virtual voltage vectors automatically,the harmonic items in the cost function of conventional FCS-MPCC are not considered.Furthermore,an adaptive control set is proposed based on voltage prediction.Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method.Consequently,current ripples can be largely reduced and the system performs much better.At last,simulations are established to verify the steady and transient performance of the proposed FCS-MPCC,and experiments based on a 2 kW five-phase motor are carried out.The results have validated the performance improvement of the proposed control strategy.

FCS-MPC Strategy for PV Grid-Connected Inverter Based on MLD Model

In the process of grid-connected photovoltaic power generation,there are high requirements for the quality of the power that the inverter breaks into the grid.In this work,to improve the power quality of the grid-connected inverter into the grid,and the output of the system can meet the grid-connected requirements more quickly and accurately,we exhibit an approach toward establishing a mixed logical dynamical(MLD)model where logic variables were introduced to switch dynamics of the single-phase photovoltaic inverters.Besides,based on the model,our recent efforts in studying the finite control set model predictive control(FCS-MPC)and devising the output current full state observer are exciting for several advantages,including effectively avoiding the problem of the mixed-integer quadratic programming(MIQP),lowering the THD value of the output current of the inverter circuit,improving the quality of the power that the inverter breaks into the grid,and realizing the current output and the grid voltage same frequency and phase to meet grid connection requirements.Finally,the effectiveness of the mentioned methods is verified by MATLAB/Simulink simulation.

Liver Segmentation in CT Images Based on DRLSE Model

Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.

New Model Die Set for PET Plastic Jet-Moulding Machine

At the China International Food Packing Machinery Exhibition, the new model die set for PET plastic jet-mouldingmachine developed by the Zhejiang Province Taizhou Municipality Huangyan Sanyou Plastics Factory attracted the attention of numerous domestic and foreign clients. They rushed to the stand in great numbers for consultation and talks on ordering. According to the evaluation of the experts concerned, the die set is the most advanced one nationwide for PET plastic jet-moulding machinery.

Finite control set model predictive control of three-port converter for interfacing a PV-battery energy storage system to a three-phase stand-alone AC system

This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations,single-stage conversion,high power density and reduced power losses due to the lower number of switches.The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports.It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current(AC)systems.The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter.Each leg of the three-phase converter will act as a bidirectional direct current(DC)/DC converter as well as an inverter simultaneously.Only six switches manage the power transfer between all the connected ports of photovoltaic-battery energy storage system linked to the stand-alone AC load.The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller.The system is validated in simulation(1-kW rating)and experimental environment(200-W rating).The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board.Two independent case studies are carried out to validate the efficacy of the system.The first scenario is for a change in solar irradiance,while the second scenario is for a change in the output load.

Transient study of droplet oscillation characteristics driven by an electric field

Electrowetting technology,a microfluidic technology,has attracted more and more attention in recent years and has broad prospects in terms of microdroplet drive.In this paper,the dynamic contact angle theory is used to develop a numerical model to predict the droplet dynamic contact behavior and internal flow field under electrowetting.In particular,based on the established computational model of droplet force balance,the dynamic process of a droplet under electrowetting is analyzed,including the perspective of pressure variation and force balance inside the droplet.The results show that when the alternating current frequency increases from 50 Hz to 500 Hz,the amplitude of the oscillation waveform after droplet stabilization is 0.036 mm,0.016 mm,0.013 mm and 0.002 mm,while the relevant droplet oscillation period T is 11 ms,4 ms,2 ms and 1 ms,respectively.It is also found that the initial phase angle does not affect the droplet oscillation amplitude.In addition,the pressure on the droplet surface under alternating current electrowetting increases rapidly to the maximum value with resonant waveform oscillation,and the droplet will present different resonance modes under voltage stimulation.The higher the resonance mode is,the smaller the droplet oscillation amplitude is and the streamline at the interface will present an eddy current,in which the number of vortices matches the resonance mode.A high resonance mode corresponds to a small droplet amplitude,while there are more vortices with a smaller size.

Computation of Bond Dissociation Energies for Removal of Nitrogen Dioxide Groups in Certain Aliphatic Nitro Compounds

Bond dissociation energies for removal of nitrogen dioxide groups in 10 aliphatic nitro compounds, including nitromethane, nitroethylene, nitroethane, dinitromethane, 1-nitropropane, 2-nitropropane, 1-nitrobutane, 2-methyl-2-nitropropane, nitropentane, and nitrohexane, are calculated using the highly accurate complete basis set （CBS-Q） and the three hybrid density functional theory （DFT） methods B3LYP, B3PW91 and B3P86 with 6-31G^＊＊ basis set. By comparing the computed bond dissociation energies and experimental results, we find that the B3LYP/6-31G^＊＊ and B3PW91/6-31G^＊＊ methods are incapable of predicting the satisfactory bond dissociation energy （BDE）. However, B3P86/6-31G^＊＊ and CBS-Q computations are capable of giving the calculated BDEs, which are in extraordinary agreement with the experimental data. Nevertheless, since CBS-Q computational demands increase rapidly with the number of containing atoms in molecules, larger molecules soon become prohibitively expensive. Therefore, we suggest to take the B3P86/6-31G^＊＊ method as a reliable method of computing the BDEs for removal of the NO2 groups in the aliphatic nitro compounds.

Wavelength Selection for Near-Infrared Spectroscopic Analysis of Teicoplanin

Teicoplanin (TCP) is a multiple drug-resistant lipoglycopeptide antibiotic produced by fermenting Actinoplanes teichomyceticus. In this study, a mixture of TCP with the Tris-HCl buffer (TCP-Tris- HCl) was used to simulate TCP fermentation broth. The reagent-free, rapid and simultaneous quantitative analysis models for TCP and Tris in the TCP-Tris-HCl mixtures were established by near-infrared (NIR) spectroscopy. The equidistant combination partial least squares (EC-PLS) method and the equivalent model sets were proposed, the simplest equivalent model with the smallest number of wavelengths were further selected. The initial wavelength, number of wavelengths, number of wavelength gaps, number of PLS factors were 1520 nm, 28, 5, 5 for TCP and 1084 nm, 13, 6, 4 for Tris, respectively. Compared with the optimal EC-PLS models, the simplest equivalent models adopted fewer wavelengths. Thus, the redundant wavelengths were removed, the models were further simplified. The root-mean-square errors (SEP) and correlation coefficients (RP) for prediction were 0.043 mg·mL-1 and 0.9998 for TCP, and 0.222 mg·mL-1 and 0.9989 for Tris, respectively. The results indicate that NIR method can be applied to highly accurate quantitative analysis for TCP and provide valuable references for further application to TCP fermentation broth.

Bound analysis of target displacement for seismic performance assessment of buildings

The earthquake demand spectrum is often assumed as the average spectrum in the seismic design code or based upon probabilistic models to take into account the uncertainties of ground motion. The average spectrum can not deal with the uncertainty of earthquake excitation. Furthermore, results of probabilistic analysis are suspicious when information describing the uncertainty is inadequate. Considering the uncertainties of peak acceleration of ground motion and characteristic period of response spectra using a double bounded convex set model, the bounds of the design spectrum and the demand spectrum were obtained based on Chinese seismic code. Then the convex analysis method was integrated into the capacity spectrum procedure to study the bound characteristics of target displacement. Finally, the target displacement was derived with an interval, and it is more objective and robust with respect to probabilistic perspective.

FCS-MPC for four-switch three-phase AC-DC converters with DC-link capacitor voltage balancing

Due to the decrease in the number of switches for the four-switch three-phase alternating current-direct current(FSTP AC-DC)converter,it can easily lead to DC-link capacitor voltage imbalance and the system stability reduction.In order to solve these problems,a finite control set model predictive control(FCS-MPC)for FSTP AC-DC converters with DC-link capacitor voltage balancing is proposed.In this strategy,in order to facilitate calculation,theαβcoordinate system model is established and all voltage vectors are evaluated by establishing a cost function.During the whole process,phase locked loop(PLL)and complex modulation strategy are not required.In the new established cost function,the additional objective term of suppressing capacitor voltage fluctuation is to eliminate effectively the capacitor voltages oscillations and deviations and improve the system reliability.The simulation results show that the proposed strategy can keep the capacitor voltage balancing and has good dynamic and static performance.

AO-MW-PLS method applied to rapid quantification of teicoplanin with near-infrared spectroscopy

Teicoplanin(TCP)is an important lipoglycopeptide antibiotic produced by fermenting Acti-noplanes teichomyceticus.The change in TCP concentration is important to measure in the fermentation process.In this study,a reagent-free and rapid quantification method for TCP in the TCP-Tris-HCl mixture samples was developed using near infrared(NIR)spectroscopy by focusing our attention on the fermentation process for TCP.The absorbance optimization(AO)partial least squares(PLS)was proposed and integrated with the moving window(MW)PLS,which is called AO-MW-PLS method,to select appropriate wavebands.Amodel set that includes various wavebands that were equivalent to the optimal AO-MW-PLS waveband was,proposed based on statistical considerations.The public region of all equivalent wavebands was just one of the equivalent wavebands.The obtained public regions were 1540-1868 nm for TCP and 1114-1310 nm for Tris.The root-mean-square error and correlation coeficient for leave-one-out cross validation were 0.046 mg mL^(-1)and 0.9998 mg mL^(-1)for TCP,and 0.235 mg mL^(-1)and 0.9986 mg mL^(-1)for Tris,respectively.All the models achieved highly accurate prediction effects,and the selected wavebands provided valuable references for designing specialized spectrometers.This study provided a valuable reference for further application of the proposed methods to TCP fermentation broth and to other spectroscopic analysis fields.

Contour tracking using weighted structure tensor based variational level set

A novel contour tracking method using weighted structure tensor based variational level set is proposed in this paper.The image is first converted to weighted structure tensor field by extracting apositive definite symmetric covariance matrix for each pixel.Then,a level set method is employed to represent object contour implicitly which separates the image domain into two areas each modeled by tensor field based Gaussian mixture model separately.By solving agradient flow equation of energy functional with respect to the level set,the object contour will converge to its real profile in the newly arrived frame.Experimental results on several video sequences demonstrate the better performance of our method than the other two contour tracking algorithms.

Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory

Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.

Study of Impact in Soil for Sewage Sludge Application

Land utilization is considered as a positive and effective method for sewage sludge disposal. This method takes use of a large number of rich nutrients for soil and crop. The application of sewage sludge in soil increases the concentration of heavy metals. Compared with 15-30 cm layer of soil, 0-15 cm layer increased significantly, especially for Cu, Zn and Ni. The concentration of Cd in soil is considered as the main limiting factor, which determines the years of land utilization. If the largest continuous application is 30 t/（hm2.a） to the soil, the largest application period will be 11.53 years. In this paper, set pair analysis model are used in safety evaluation of the soil. After one year application of sewage, the soil can be still safe. Finally, according to the process of the subject put forward some suggestions.

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.