Mitigation of voltage sag, swell and power factor correction using solid-state transformer b

This paper presents a novel topology of solid-state transformer (SST). In the design process, the AC/DC, DC/AC and AC/AC converters have been integrated to achieve higher efficiency. To obtain higher efficiency from other SST with DC-link topologies, the AC/DC and DC/AC converters have been integrated in one matrix converter. The proposed SST performs typical functions and has advantages such as power factor correction, voltage sag and swell elimination, voltage flicker reduction and protection capability in fault situations. In addition, it has other benefits such as light weight, low volume and elimination of hazardous liquid dielectrics because it uses medium frequency transformer. The operation and some performances of the proposed SST have been verified by the simulation results

Prediction Of Time-Dependent Bearing Capacity Of Pile Driven In Cohesive Soil Using Group Method Of Data Handling

Evaluating the ultimate bearing capacity of piles has been always an important concern for geotechnical engineers. Pile setup is a term which refers to an increase in bearing capacity of pile after a specific time. This increase is mainly considered relevant to the dissipation of excess pore water pressure created as a result of disturbance of the soil around the pile. Many researches have been centered on the investigation of pile setup and the factors influencing that. Results indicate that soil and pile properties can affect the occurrence and intensity of this phenomenon. The application of artificial intelligence such as artificial neural networks and evolutionary algorithms are considered as efficient and powerful methods for prediction and function finding purposes. Group Method of Data Handling is an intelligent approach that operates in a similar pattern to artificial neural networks. In this system, dual combinations of input variables are created in the form of Kolmogorov-Gabor polynomials. Based on the evaluation criteria such as Root Mean Squared Error (RMSE) and determination coefficient (R^2), the polynomials with higher accuracy are selected and introduced to the next layer as inputs. This repetitive approach is used to reach the best polynomials predicting the target variable of the project. GMDH is a self-organized system in which the number of required layers and neurons are determined during the running process. In this paper, a dataset obtained from the literature review and contains information about 170 test piles derived in clay and mixed soil, is used in which the ultimate bearing capacity is considered as a target, while the other ones are independent variables. It is noticeable that to evaluate the efficiency of the ultimate model, data is randomly divided into training and testing data which the former includes 118 and the latter has used 52 data. The results of this study indicate that the initial bearing capacity of pile (End Of Driving) and undrained shear strength ( S_u) have a significant effect on the time-dependent increase in bearing capacity of pile. The ultimate model obtained from the GMDH system with the RMSE of 742.85 and the (R^2) of 0.768 is an acceptable equation that can be used in the process of pile design

Selection of DC voltage magnitude using Fibonacci series for new hybrid asymmetrical multilevel inverter with minimum PIV

Multilevel inverters are suggested to obtain high quality output voltage. In this paper, a new hybrid configuration is proposed, obtained by cascading one four switches H-bridge cell with a family of multilevel inverters. In addition, by the use of specific sequence for value of DC sources named Fibonacci series, asymmetrical topology of proposed inverter is introduced. Main advantages are that proposed inverter has least Peak Inverse Voltage (PIV) than other conventional multilevel converters in both symmetric and asymmetric modes. Also, this topology doubles the number of output levels using only one cascaded four switches H-bridge cell. The PCI-1716 DAQ using PC has been used to generate switching pulses in experimental results. For presenting valid performance of proposed configuration, simulation results carried out by MATLAB/SIMULINK software and the validity of the proposed multilevel inverter is verified by experimental results

A new low cost cascaded transformer multilevel inverter topology using minimum number of components with modified selective harmonic elimination modulation

In this paper, a novel cascaded transformer multilevel inverter is proposed. The number of the switching devices is reduced in the proposed topology. This topology comprises of a DC source, several single phase low-frequency transformers, two main power switches and some bidirectional switching devices. In this topology, only one bidirectional switch is employed for each transformer. However, in conventional cascaded transformer multilevel inverter, four switching devices are required for each transformer. Therefore, more output voltage levels can be obtained using fewer switching components. Reduction in the number of switching devices which also means reduction in the number of gate drivers results in smaller size and low implementation cost. Switching power losses are also reduced in this topology. Selective harmonic elimination (SHE) technique is applied to the proposed inverter to obtain a high quality output voltage. Simulation and experimental results are also provided to verify the feasibility of the proposed converter

Dynamic stability enhancement of power system based on a typical unified power flow controllers using imperialist competitive algorithm

This paper presents dynamic model of power system installed with a novel UPFC that consist of two shunt converters and a series capacitor. In this configuration, a series capacitor is used between two shunt converters to inject desired series voltage. As a result, it is possible to control the active and reactive power flow. The main advantage of the proposed UPFC in comparison with the conventional configuration is injection of a series voltage waveform with a very low total harmonic distortion (THD). In addition, a linearized Phillips–Heffron model is obtained and a supplementary controller for the modeling of proposed UPFC to damp low frequency oscillations with considering four alternative damping controllers is recommended. The problem of robustly novel UPFC based damping controller is formulated as an optimization problem according to the time domain-based objective function, which are solved using particle swarm optimization (PSO) and Imperialist Competitive Algorithm (ICA) techniques

2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA

Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r2=0.862, q2=0.644) and CoMSIA (r2=0.851, q2=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R2train= 0.843, Q2 LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity. © 2015 Bentham Science Publishers

QSAR study of mGlu5 inhibitors by genetic algorithm-multiple linear regressions

In this study, the quantitative structure-Activity relationship (QSAR) model for some pyrazole/imidazole amide derivatives as mGlu5 inhibitors was developed. The data set was split into the training and test subsets, randomly. The most relevant variables were selected using the genetic algorithm (GA) variable selection method. Multiple linear regression (MLR) method was used as a linear model to predict the activity of mGlu5 inhibitors based on compounds in training set. The external set of nine compounds selected out of 47 compounds, and used to evaluate the predictive ability of QSAR model. The built model could give high statistical quantities (R train 2 = 0.837, Q 2 = 0.759, R test 2 = 0.919) in which proved that the GA-MLR model was a useful tool to predict the inhibitory activity of pyrazole/imidazole amide derivatives. The results suggested that the atomic masses, atomic van der Waals volumes, atomic electronegativities, and the number of imines (aromatic) are the most important independent factors that contribute to the mGlu5 inhibition activity of pyrazole/imidazole amides derivatives. © 2013 Springer Science+Business Media New York

QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions

The predictive analysis based on quantitative structure activity relationships (QSAR) on benzimidazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R 2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation. [Figure not available: see fulltext.] © 2015 Indian Academy of Sciences

Damping of power system oscillations using imperialist competition algorithm in power system equipped by HVDC

In this paper, a linear model of power system equipped by HVDC parallel installed with HVAC is investigated. The HVDC system is mainly composed by voltage source converters (VSC-HVDC). So, there are four adjustable variables to control the power system characteristics. In this regards, one of these variables can be used as a supplementary controller input, in order to damp the oscillations efficiently. In this work the Singular Value Decomposition(SVD) method is used to realize the most effective control input of VSC-HVDC, with the aim of low frequency oscillation damping. Besides, optimum values for supplementary controller gains, are found using imperialist competition algorithm. To confirm strength of the proposed controller, the designed controller is tested in wide range of operating condition and compared with conventional scheme. The main advantage of the proposed procedure is greatly improving the dynamic response of the system. In addition, the overshoots, undershoots and the settling times are dramatically reduced by applying the proposed method. Simulation results show the good operation of proposed method so as to damp power system oscillations