Burla, India
Burla, India

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Panda S.,VSSUT | Yegireddy N.K.,VSSUT | Mohapatra S.K.,CVRCE
International Journal of Electrical Power and Energy Systems | Year: 2013

In this paper coordination scheme to improve the stability of a power system by optimal design of power system stabilizer and static synchronous series compensator (SSSC)-based controller is presented. Time delays owing to sensor and signal transmission delays are included in the design. The coordinated design problem is formulated as an optimization problem and hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach. © 2013 Elsevier Ltd. All rights reserved.

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.

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.

Tripathy M.,V.S.S.U.T. | 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.

Das P.K.,VSSUT | Behera H.S.,VSSUT | Panigrahi B.K.,Indian Institute of Technology Delhi
Swarm and Evolutionary Computation | Year: 2016

This paper proposed a new methodology to determine the optimal trajectory of the path for multi-robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved gravitational search algorithm (IGSA). The proposed approach embedded the social essence of IPSO with motion mechanism of IGSA. The proposed hybridization IPSO-IGSA maintain the efficient balance between exploration and exploitation because of adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocity simultaneously. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all robots to their respective destination in the environment. The robot on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO-IGSA. Finally the analytical and experimental results of the multi-robot path planning were compared to those obtained by IPSO-IGSA, IPSO, IGSA in a similar environment. The Simulation and the Khepera environment result show outperforms of IPSO-IGSA as compared with IPSO and IGSA with respect to optimize the path length from predefine initial position to designation position ,energy optimization in the terms of number of turn and arrival time. © 2016 Elsevier B.V.

Sahu R.K.,VSSUT | Panda S.,VSSUT | Padhan S.,VSSUT
International Journal of Electrical Power and Energy Systems | Year: 2015

In this paper, a novel hybrid Firefly Algorithm and Pattern Search (hFA-PS) technique is proposed for Automatic Generation Control (AGC) of multi-area power systems with the consideration of Generation Rate Constraint (GRC). Initially a two area non-reheat thermal system with Proportional Integral Derivative (PID) controller is considered and the parameters of PID controllers are optimized by Firefly Algorithm (FA) employing an Integral Time multiply Absolute Error (ITAE) objective function. Pattern Search (PS) is then employed to fine tune the best solution provided by FA. The superiority of the proposed hFA-PS based PID controller has been demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI/PID controllers for the same interconnected power system. Furthermore, sensitivity analysis is performed to show the robustness of the optimized controller parameters by varying the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to multi area multi source hydro thermal power system with/without considering the effect of physical constraints such as time delay, reheat turbine, GRC, and Governor Dead Band (GDB) nonlinearity. The controller parameters of each area are optimized under normal and varied conditions using proposed hFA-PS technique. It is observed that the proposed technique is able to handle nonlinearity and physical constraints in the system model.© 2014 Elsevier Ltd. All rights reserved.

Dora L.,V.S.S.U.T | Rath N.P.,V.S.S.U.T
Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010 | Year: 2010

Face recognition has received an increased attention from several years in the field of image analysis, pattern recognition, and computer vision. In this paper we propose a method to the problem of face recognition. The proposed method consists of two stages. In the first stage regularized linear discriminant analysis is used to extract the most significant and discriminant features and then in the second stage, these features are used by probabilistic reasoning model for classification of unknown face images. Here two databases, the ORL database and the UMIST database are used for experiments and to show the performance of the proposed method. © 2010 IEEE.

Barisal A.K.,VSSUT | Prusty R.C.,VSSUT
Applied Soft Computing Journal | Year: 2015

This paper presents an evolutionary hybrid algorithm of invasive weed optimization (IWO) merged with oppositional based learning to solve the large scale economic load dispatch (ELD) problems. The oppositional invasive weed optimization (OIWO) is based on the colonizing behavior of weed plants and empowered by quasi opposite numbers. The proposed OIWO methodology has been developed to minimize the total generation cost by satisfying several constraints such as generation limits, load demand, valve point loading effect, multi-fuel options and transmission losses. The proposed algorithm is tested and validated using five different test systems. The most important merit of the proposed methodology is high accuracy and good convergence characteristics and robustness to solve ELD problems. The simulation results of the proposed OIWO algorithm show its applicability and superiority when compared with the results of other tested algorithms such as oppositional real coded chemical reaction, shuffled differential evolution, biogeography based optimization, improved coordinated aggregation based PSO, quantum-inspired particle swarm optimization, hybrid quantum mechanics inspired particle swarm optimization, modified shuffled frog leaping algorithm with genetic algorithm, simulated annealing based optimization and estimation of distribution and differential evolution algorithm. © 2014 Elsevier B.V. All rights reserved.

The static and parametric stability of an asymmetric tapered sandwich beam resting on a variable Pasternak foundation subjected to a pulsating axial load with thermal gradient under two different boundary conditions is investigated. The complete mathematical modeling of the system has been derived by the application of Hamilton's principle which helps in getting the admissible path for the system. The equations of motion and boundary conditions obtained from the Hamilton's equation are non-dimensionalized. A set of Hill's equation are obtained from the non-dimensional equations of motion by the application of generalized Galerkin's method. The zones of parametric instability are obtained using Saito-Otomi conditions. The effects of taper, elastic foundation, thermal gradient, core-loss factor, geometric parameter, modulus ratios and shear parameter on static buckling loads and parametric regions of instability are investigated. © 2016 Elsevier Ltd.

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.

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