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This paper presents a heuristic algorithm PSO based simultaneous coordinated tuning of the SVC damping controller and power system stabilizer (PSS) in a two machine three bus power system. The coordinated tuning of PSS and SVC damping controller is converted to an optimization problem which is solved by particle swarm optimization (PSO) technique. The parameters like rotor angle deviation, settling time of disturbances post fault and transmission line active power are observed. The results obtained with PSO tuned SVC and PSS are compared with those of conventional PSS and Fuzzy controlled SVC and PSS. PSO tuned SVC and PSS gives better results. The effectiveness of each control method is verified using MATLAB. The results also show that the proposed coordinated controllers have increased power system oscillations damping capability. © 2016, Strojarski Facultet. All rights reserved. Source


Vasim Babu M.,Latha Mathavan Engineering College
Proceedings of 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies, ICACCCT 2012 | Year: 2012

Localizing and tracking moving stimuli or objects is an essential capability for a sensor network in many applications. Discrete power Management is an efficient way to construct a reliable and energy efficient network topology in WSN. At the same time Location awareness is also important for wireless sensor networks since many applications such as environment monitoring and Tracking. Hence. MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. In this paper we introduce the new power mapping algorithm based on discrete antithetic markov Monte carlo method which variance reduction method for increasing the accuracy of Markov chain Monte Carlo algorithm for computing the dominant eigen pair of a matrix also we are concentrate on power management technique in terms of discrete power levels which allocate the power based on the event of every sensor node. By using this discrete power mapping method we can analyze all the high level parameter of the wireless sensor network especially for RSSI and TOA,also we derive the mathematical model for discrete level distance measurement and reduce the distance error for more than three anchors and redundant distant measurements to account for the error in each individual measurement. © 2012 IEEE. Source


We consider n-job, m-machine lot streaming problem in a flow shop with equal size sub lots where the objective is to minimize the makespan and total flow time. Lot streaming (Lot sizing) is a technique that splits a production lot consisting of identical items into sub lots to improve the performance of a multi stage production system by over lapping the sub lots on successive machines. There is a scope for efficient algorithms for scheduling problems in m-machine flow shop with lot streaming. In recent years, much attention is given to heuristics and search techniques. To solve this problem, we propose a Differential Evolution Algorithm (DEA) and Particle Swarm Optimization (PSO) to evolve best sequence for makespan/total flow time criterion for m-machine flow shop involved with lot streaming and set up time. In this research, we propose the DEA and PSO algorithms for discrete lot streaming with equal sub lots. The proposed methods are tested and the performances were evaluated. The computational results show that the proposed algorithms are very competitive for the lot streaming flow shop scheduling problem. © 2011 Springer Science+Business Media, LLC. Source


Marimuthu S.,Latha Mathavan Engineering College
International Journal of Computational Intelligence Systems | Year: 2012

Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot streaming problem in a flow shop with equal and variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve flow shop problems with lot streaming. In this research, Firefly Algorithm (FA) and Artificial Immune System (AIS) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results shows that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem. © 2012 Copyright the authors. Source


Vasim Babu M.,Latha Mathavan Engineering College
Scientific World Journal | Year: 2015

A novel strategy of discrete energy consumption model for WSN based on quasi Monte Carlo and crude Monte Carlo method is developed. In our model the discrete hidden Markov process plays a major role in analyzing the node location in heterogeneous media. In this energy consumption model we use both static and dynamic sensor nodes to monitor the optimized energy of all sensor nodes in which every sensor state can be considered as the dynamic Bayesian network. By using this method the power is assigned in terms of dynamic manner to each sensor over discrete time steps to control the graphical structure of our network. The simulation and experiment result shows that our proposed methods are better in terms of localization accuracy and possess minimum computational time over existing localization method. © 2015 M. Vasim Babu and A. V. Ramprasad. Source

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