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Institution

University of Tabriz

EducationTabriz, Iran
About: University of Tabriz is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 12141 authors who have published 20976 publications receiving 313982 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a new dc/dc converter is proposed which can produce boosted multiple dc link voltages by using the novel switched-capacitor converter (SCC) and with reduced number of switches.
Abstract: In this paper, initially a new dc/dc converter is proposed which can produce boosted multiple dc link voltages by using the novel switched-capacitor converter (SCC) and with reduced number of switches. In the proposed SCC, voltage of all capacitors is charged by binary asymmetrical pattern as self-balancing and without using any auxiliary circuits. The proposed SCC will boost the input dc power supply voltage without transformer by switching the capacitors in series and in parallel. Next, a new single phase switched-capacitor multilevel inverter (SCMLI) topology which uses the proposed SCC units as virtual dc links have been proposed. The proposed topologies reduce the number of power switches, diodes, isolated dc power supplies, size, and the cost of the system in comparison with conventional similar topologies. For example, by contribution of proposed SCMLI structure, 49 and 137 output voltage levels are made by only 14 and18 power switches and 3 and 4 isolated dc power supplies, respectively. To confirm the performance of proposed topology, various simulation results by PSCAD/EMTDC software and experimental tests are given.

213 citations

Journal ArticleDOI
TL;DR: In this article, a probabilistic framework based on a scenario method, which is considered for load demand and price signals, is employed to overcome the uncertainties in the optimal energy management of the MG.

211 citations

Journal ArticleDOI
TL;DR: The results indicated the utility of the sequential path model for determining the interrelationships among grain yield and related traits in maize.
Abstract: Knowledge of interrelationships between grain yield and its contributing components will improve the efficiency of breeding programs through the use of appropriate selection indices. Previous path analyses studies in maize (Zea mays L.) treated yield components as first-order variables. The present study, based on evaluation of 90 experimental maize hybrids (comprising one diallel and one line x tester set) at two locations in India, utilizes a sequential path model for analysis of genetic associations among grain yield and its related traits by ordering the various variables in first-, second-, and third-order paths on the basis of their maximum direct effects and minimal collinearity. The sequential path model showed distinct advantages over the conventional path model in discerning the actual effects of different predictor variables. Two first-order variables, namely 100-grain weight and total number of kernels per ear, revealed highest direct effects on total grain weight (p = 0.74 and p = 0.78, respectively), while ear length, ear diameter, number of kernel rows, and number of kernels per row were found to fit as second-order variables. All direct effects were found to be significant, as indicated by bootstrap analysis. Test for the goodness-of-fit revealed that the sequential path model provided better fit to various datasets analyzed in the study. Correlations between the predicted values of various response variables in the second season dataset based on the path coefficients of the first season were high, except for ear length and number of kernels per row. The applicability of the model has been confirmed through analysis of two additional datasets during 2000. The results indicated the utility of the sequential path model for determining the interrelationships among grain yield and related traits in maize.

209 citations

Journal ArticleDOI
TL;DR: The presented results supported the view that biochar can contribute to protect common bean seedlings against NaCl stress by alleviating the oxidative stress.

209 citations

Journal ArticleDOI
TL;DR: A new structure for switched-capacitor multilevel inverters (SCMLIs) which can generate a great number of voltage levels with optimum number of components for both symmetric and asymmetric values of dc-voltage sources is presented.
Abstract: The aim of this paper is to present a new structure for switched-capacitor multilevel inverters (SCMLIs) which can generate a great number of voltage levels with optimum number of components for both symmetric and asymmetric values of dc-voltage sources. The proposed topology consists of a new switched-capacitor dc/dc converter (SCC) that has boost ability and can charge capacitors as self-balancing by using the proposed binary asymmetrical algorithm and series–parallel conversion of power supply. The proposed SCC unit is used in new configuration as a submultilevel inverter (SMLI) and then, these proposed SMLIs are cascaded together and create a new cascaded multilevel inverter (MLI) topology that is able to increase the number of output voltage levels remarkably without using any full H-bridge cell and also can pass the reverse current for inductive loads. In this case, two half-bridge modules besides two additional switches are employed in each of SMLI units instead of using a full H-bridge cell that contribute to reduce the number of involved components in the current path, value of blocked voltage, the variety of isolated dc-voltage sources, and as a result, the overall cost by less number of switches in comparison with other presented topologies. The validity of the proposed SCMLI has been carried out by several simulation and experimental results.

208 citations


Authors

Showing all 12238 results

NameH-indexPapersCitations
Ozgur Kisi7347819433
Alireza Khataee6852520805
Mehdi Shahedi Asl631978437
Mohammad Hossein Ahmadi6047711659
Gerard Ledwich5668615375
Thomas Blaschke5634817021
Ali Nokhodchi553229087
Danial Jahed Armaghani552128400
Behnam Mohammadi-Ivatloo514829704
Mohammad Norouzi5115918934
Ebrahim Babaei5045510615
Abolghasem Jouyban5070012247
Abolfazl Akbarzadeh5025311256
Yadollah Omidi492948076
Vahid Vatanpour471949313
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202351
2022222
20212,299
20202,382
20192,148
20181,714