Rajasthan Technical University

About: Rajasthan Technical University is a education organization based out in Kota, Rajasthan, India. It is known for research contribution in the topics: Photovoltaic system & PID controller. The organization has 716 authors who have published 1084 publications receiving 4530 citations. The organization is also known as: RTU.

Design and performance analysis of adaptive neuro fuzzy controller for load frequency control of multi-power system

Abstract: This paper presents an adaptive neuro fuzzy (ANFIS) based load frequency control for multi-area non-reheat turbine power system. It can handle the non-linearity of the system and react to situation faster than the conventional controller. The changes in frequency and Tie-line power are subjects be controlled for 3-area power system. The system response with proposed controller is compared to that of with conventional controllers presented in literature. The comparison is carried-out in terms of over-shoot, under-shoot and settling-time of frequency and tie-line power. It is proposed that the intelligent controller (ANFIS) provides encouraging result as compared that of with PID controllers in literature. The robustness of the proposed controller is considered for a wide range of change in load.

Application of Routh Stability Array method to reduce MIMO SMIB power system

Abstract: In this paper, a large - order system is reduced by using the Routh Stability Array (RSA) method to a reduced-order approximation. The reduced order transfer function is determined directly from the elements in a Routh stability array of the high-order denominator and numerator. The multi-input multi-output (MIMO) single-machine connected an infinite-bus (SMIB) power system model is considered to demonstrate the effectiveness of RSA. It is proved by simulation results of reduced models as compared to respective original models. It demonstrates the effectiveness, accuracy and simplicity of the RSA in the field of model order reduction.

Investigation of static and dynamic behavior of functionally graded piezoelectric actuated Poly-Si micro cantilever probe

Abstract: In the present paper a novel functionally graded piezoelectric (FGP) actuated Poly-Si micro cantilever probe is proposed for atomic force microscope. The shear piezoelectric coefficient d15 has much higher value than coupling coefficients d31 and d33, hence in the present work the micro cantilever beam actuated by d15 effect is utilized. The material properties are graded in the thickness direction of actuator by a simple power law. A three dimensional finite element analysis has been performed using COMSOL Multiphysics® (version 4.2) software. Tip deflection and free vibration analysis for the micro cantilever probe has been done. The results presented in the paper shall be useful in the design of micro cantilever probe and their subsequent utilization in atomic force microscopes.

An optimal feature selection method for histopathology tissue image classification using adaptive jaya algorithm

Abstract: Due to the complex background and heterogeneous structure, classification of histopathological tissue images is a challenging problem. Further, the selection of appropriate features is an important phase of classification process as irrelevant and redundant features may result in high computation and degrade the performance. Therefore, a new adaptive jaya algorithm has been introduced in this paper which is used to obtain the prominent feature set from the extracted features. The proposed adaptive jaya algorithm modifies the updation equation using the best and the worst solutions. For the feature extraction, a pre-trained AlexNet has been used due to its distinguishing performance in the image classification. Moreover, the performance of five different classifiers have been analyzed over the selected features in context of classifying the histopathological tissue images into four categories, namely epithelium tissue, nervous tissue, connective tissue, and muscular tissue. Experimental results validate that the proposed adaptive jaya algorithm attains better optimal values on CEC2015 functions. The proposed method eliminates 91.3% features from the extracted features which is maximum among other considered methods and also achieves high classification accuracy.