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Srivilliputtur, India

Kalasalingam University is located in Krishnankoil near Madurai in Tamil Nadu, India. The campus is close to the ancient temple town of Srivilliputhur. The tower of the Srivilliputhur temple is used in the emblem of the Government of Tamil Nadu. Wikipedia.

Devaraj D.,Kalasalingam University | Preetha Roselyn J.,SRM University
International Journal of Electrical Power and Energy Systems | Year: 2011

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks. Source

Murugan P.,Kalasalingam University
IET Generation, Transmission and Distribution | Year: 2012

The transmission expansion planning (TEP) problem consists of determining when and where new circuits are needed and should be installed to serve the growing demand for electric power. TEP is a hard, large-scale, non-linear, mixed-integer and non-convex combinatorial problem. Finding the solution for the TEP problem is very difficult as the number of options to be analysed and compared increases exponentially with the size of the network. This study presents an application of the modified particle swarm optimisation technique, with a novel initialisation (population monitored for complementary magnitudes initialisation), for improved performance (in terms of success rate and convergence) to the highly complex TEP problem. Here, the DC model of the transmission network is considered. The success of the proposed methodology has been tested on the Garver 6-bus system, IEEE 24-bus system and Southern Brazilian 46-bus system and validated. © The Institution of Engineering and Technology 2012. Source

Subbaraj P.,Kalasalingam University | Rengaraj R.,SSN College of Engineering | Salivahanan S.,SSN College of Engineering
Applied Soft Computing Journal | Year: 2011

In this paper, a new optimization algorithm, namely Taguchi self-adaptive real-coded genetic algorithm (TSARGA) is proposed and implemented to solve economic dispatch (ED) problem with valve-point loading. The TSARGA combines the self-adaptive real-coded genetic algorithm with Taguchi method which can exploit the potential offspring. The self-adaptation is achieved by means of simulated binary crossover (SBX). Moreover, powerful exploration capability is achieved through tournament selection by creating tournaments between two solutions. The better solution is chosen and placed in the mating pool leading to better convergence and reduced computational burden. The systematic reasoning ability of the Taguchi method is incorporated after SBX operations to select the potential genes to achieve polynomial mutation, and consequently, enhance the robustness of the solution. The proposed TSARGA is effectively applied to solve the ED problem with valve-point loading with 6, 13 and 40-generator systems. The proposed method yields solutions towards global optimum and it compares far better with other methods in terms of solution quality, handling constraints and computation time. © 2010 Elsevier B.V. All rights reserved. Source

Mahadevan K.,Kalasalingam University | Kannan P.S.,Thiagarajar College of Engineering
Applied Soft Computing Journal | Year: 2010

Reactive power dispatch (RPD) is an optimization problem that reduces grid congestion by minimizing the active power losses for a fixed economic power dispatch. RPD reduces power system losses by adjusting the reactive power control variables such as generator voltages, transformer tap-settings and other sources of reactive power such as capacitor banks and provides better system voltage control, resulting in an improved voltage profile, system security, power transfer capability and over all system operation. In this paper, RPD problem is solved using particle swarm optimization (PSO). To overcome the drawback of premature convergence in PSO, a learning strategy is introduced in PSO, and this approach called, comprehensive learning particle swarm optimization (CLPSO) is also applied to this problem and a comparison of results is made between these two. Three different test cases have been studied such as minimization of real power losses, improvement of voltage profile and enhancement of voltage stability through a standard IEEE 30-bus and 118-bus test systems and their results have been reported. The study results show that the approaches developed are feasible and efficient. © 2009 Elsevier B.V. All rights reserved. Source

Rajakarunakaran S.,Ramco Institute of Technology | Arumuga Prabhu V.,Kalasalingam University
Journal of Loss Prevention in the Process Industries | Year: 2015

A method is presented for analysis of reliability of complex engineering systems using information from fault tree analysis and uncertainty/imprecision of data. Fuzzy logic is a mathematical tool to model inaccuracy and uncertainty of the real world and human thinking. The method can address subjective, qualitative, and quantitative uncertainties involving risk analysis. Risk analysis with all the inherent uncertainties is a prime candidate for Fuzzy Logic application. Fuzzy logic combined with expert elicitation is employed in order to deal with vagueness of the data, to effectively generate basic event failure probabilities without reliance on quantitative historical failure data through qualitative data processing.The proposed model is able to quantify the fault tree of LPG refuelling facility in the absence or existence of data. This paper also illustrates the use of importance measures in sensitivity analysis. The result demonstrates that the approach is an apposite for the probabilistic reliability approach when quantitative historical failure data are unavailable. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process. © 2014 Elsevier Ltd. Source

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