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Burla, India

Tripathy M.,VSSUT | Mishra S.,Indian Institute of Technology Delhi
IET Generation, Transmission and Distribution | Year: 2011

An interval type-2 (IT2) fuzzy controller-based thyristor controlled series capacitor (TCSC) has been proposed for improving power system stability. To overcome the limitation of time-consuming iterative method generally used for type-reduction, this study utilises the concept of uncertainty bounds for type-reduction. It is found that the IT2TCSC controllers along with power system stabiliser (PSS) in the system, damp out the speed and power oscillations following different critical faults satisfactorily. From simulation studies, it is further established that the damping performance of IT2TCSC is considerably better compared to its fixed gain Bacteria Swam based truned PSS and TCSC counterpart. Moreover, the performance of IT2TCSC does not deteriorate even under uncertainty in the input signal to the controller. © 2011 The Institution of Engineering and Technology. Source

Mishra P.,VSSUT
Journal of Solid Waste Technology and Management | Year: 2014

The present experimental investigation deals with the mechanical behavior of bagasse fiber reinforced epoxy composites at cryogenic temperature. Bagasse fibers of 10, 15 and 20 wt % were reinforced with epoxy matrix to prepare composite. These were exposed to liquid Nitrogen temperature. Three point bend tests were carried out at a range of 2mm/min to 500mm/min cross head speed to evaluate the sensitivity of mechanical response during these loading conditions. The mechanical performances of these composites at cryogenic temperature were compared with room temperature property. These composites are found to be loading rate sensitive. Source

Biswal B.,GMRIT | Behera H.S.,VSSUT | Bisoi R.,Soa University | Dash P.K.,Soa University
Swarm and Evolutionary Computation | Year: 2012

This paper presents a new approach for processing various non-stationary power quality waveforms through a Fast S-Transform with modified Gaussian window to generate timefrequency contours for extracting relevant feature vectors for automatic disturbance pattern classification. The extracted features are then clustered using Bacterial Foraging Optimization Algorithm (BFOA) based Fuzzy decision tree to give improved classification accuracy in comparison to the Fuzzy decision tree alone. To circumvent the problem of premature convergence of BFOA and to improve classification accuracy further, a hybridization of BFOA (Bacterial Foraging Optimization Algorithm) with another very popular optimization technique of current interest called Differential Evolution (DE) is presented in this paper. For robustness the mutation loop of the DE algorithm has been made variable in a stochastic fashion. This hybrid algorithm (Chemotactic Differential Evolution Algorithm (CDEA)) is shown to overcome the problems of slow and premature convergence of BFOA and provide significant improvement in power signal pattern classification. © 2011 Elsevier B.V. All rights reserved. Source

Barisal A.K.,VSSUT
International Journal of Electrical Power and Energy Systems | Year: 2013

This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA-MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results. © 2012 Elsevier Ltd. All rights reserved. Source

Sahoo H.K.,IIIT | Dash P.K.,Soa University | Rath N.P.,VSSUT
Applied Soft Computing Journal | Year: 2013

This paper proposes NARX (nonlinear autoregressive model with exogenous input) model structures with functional expansion of input patterns by using low complexity ANN (artificial neural network) for nonlinear system identification. Chebyshev polynomials, Legendre polynomials, trigonometric expansions using sine and cosine functions as well as wavelet basis functions are used for the functional expansion of input patterns. The past input and output samples are modeled as a nonlinear NARX process and robust H∞ filter is proposed as the learning algorithm for the neural network to identify the unknown plants. H∞ filtering approach is based on the state space modeling of model parameters and evaluation of Jacobian matrices. This approach is the robustification of Kalman filter which exhibits robust characteristics and fast convergence properties. Comparison results for different nonlinear dynamic plants with forgetting factor recursive least square (FFRLS) and extended Kalman filter (EKF) algorithms demonstrate the effectiveness of the proposed approach. © 2013 Elsevier B.V. All rights reserved. Source

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