Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Implementation of Static VAR Compensator for Improvement of Power System Stability

Abstract: Static VAR compensator (SVC) is incorporated in Newton Raphson method in which Power Flow Solution is a solution of the network under steady state conditions subjected to certain constraints under which the system operates. The power flow solution gives the nodal voltages and phase angles given a set of power injections at buses and specified voltages at a few, both the models of SVC i.e.SVC Susceptance and Firing Angle Models are discussed. It is also shown that the power system losses are decreased after incorporating the SVC in this N-R method. The results are generated for 24-Bus system. The reactors are thyristor-controlled and the capacitors can be either fixed or controlled. Advanced load flow models for the SVC are presented in this paper. The models are incorporated into existing load flow (LF) Newton Raphson algorithm. The new models depart from the generator representation of the SVC and are based instead on the variable susceptance concept. The SVC state variables are combined with the nodal voltage magnitudes and angles of the network in a single frame of reference for a unified, iterative solution through Newton methods. The algorithm for Load Flow exhibit very strong convergence characteristics, regardless of the network size and the number of controllable devices. Results are presented which demonstrate the process of the new SVC models.

A sinusoidal oscillator using single Operational Transresistance Amplifier

Abstract: This paper illustrates a sinusoidal oscillator based on Operational Transresistance Amplifier (OTRA). The proposed oscillator circuit consists of one OTRA and few passive components to generate the oscillations. The condition of oscillation and frequency of oscillation of the proposed circuit is controlled by a single grounded resistor. CMOS gpdk 180 nm technology was used to test the overall performance of the proposed circuit. The OTRA circuit has been built using commercially available current feedback operational amplifier (AD844 AN) and later passive components are connected externally and tested for waveform generation. The simulated and experimental results are presented and the tunability of the grounded resistor is given. The results obtained demonstrate excellent agreement with theoretical values.

Structural, electrical, and dielectric properties of nickel-doped spinel LiMn 2 O 4 nanorods

Abstract: Spinel pure and Ni-doped LiMn2O4 nanorods were synthesized by a rapid microwave-assisted hydrothermal process followed by a solid-state reaction method. Their structural, morphological, electrical, and dielectric properties were investigated by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, Raman spectroscopy, transmission electron microscopy (TEM), and impedance spectroscopy techniques. Powder XRD studies revealed that all the synthesized samples have well-defined cubic crystal structure and the Ni2+ doping in manganese sites did not affect spinel LiMn2O4 structure. TEM images of pure and Ni-doped LiMn2O4 samples clearly showed the formation of well-dispersed nanorods with uniform distribution. The Ni2+ doping did not affect the nanorod morphology of pure LiMn2O4. The spinel LiMn2O4 nanorods showed an electrical conductivity of 3.13 × 10−4 S cm−1, at room temperature. The A.C conductivity studies revealed that the pure and Ni-doped LiMn2O4 nanorods obey Jonscher’s power law. The dielectric studies revealed that the dielectric constant of the samples decreases with frequency, which is due to decrease in charge accumulation at the interface.

Automatic speech emotion detection using hybrid of gray wolf optimizer and naïve Bayes

Abstract: Over the past decade, automatic speech emotion detection has been a great challenge in the human–computer interaction area. Generally, individuals express their feelings explicitly or implicitly through words, facial expressions, gestures, or writing. Different datasets such as speech, text, and visuals are used to explore emotions. Here, seven emotions such as neutrality, happiness, sadness, fear, surprise, disgust, and anger are detected using speech signals. To perform speech emotion recognition, several datasets are available. SAVEE and TESS datasets are used here. In most of the earlier works, separate databases were used to identify emotions. But here, SAVEE and TESS databases are merged to create a new database and identified their emotions. Our main objective is to use this robust dataset to characterize their emotions. For this purpose, we have proposed a new machine learning algorithm. Initially, Mel-frequency cepstral coefficients are utilized to extract the features from the voice signal datasets. Finally, a hybrid of gray wolf optimizer and naive Bayes machine learning algorithm was proposed for classification. From the results, our proposed classification algorithm provides better performance compared to current machine learning.

Applying several soft computing techniques for prediction of bearing capacity of driven piles

Abstract: Pile as a type of foundation is a structure which can transfer heavy structural loads into the ground. Determination and proper prediction of pile bearing capacity are considered as a very important issue in preliminary design of geotechnical structures. This study attempts to develop several intelligent techniques for prediction of pile bearing capacity in cohesionless soil. To show the effects of fuzzy inference system and imperialism competitive algorithm (ICA) on a pre-developed artificial neural network (ANN), two hybrid ANN models namely ICA-ANN and adoptive neuro-fuzzy inference system (ANFIS) were considered and developed to estimate pile bearing capacity. Then, results of these techniques were compared with those of ANN model and the best one among them was chosen according to the results of performance indices. Several parameters (i.e., internal friction angle of soil located in shaft and tip, effective vertical stress at pile toe, pile area, and pile length) were set as model inputs, while the output is the total driven pile bearing capacity. As a result of the developed models, coefficient of determination (R2) values of (0.895, 0.905), (0.945, 0.958), and (0.967, 0.975) were obtained for training and testing data sets of ANN, ICA-ANN, and ANFIS models, respectively. The results showed that both hybrid models are able to predict bearing capacity with high degree of accuracy; however, ANFIS receives more applicable based on used performance indices and it can be utilized for further researchers and engineers in practice.