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Search Results: 1 - 10 of 198134 matches for " N. Kamaraj "
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Real Power Contingency Ranking Using Wavelet Transform Based Artificial Neural Network (WNN)
S. Sutha,N. Kamaraj
International Journal of Electrical and Power Engineering , 2012,
Abstract: In deregulated operating regime power system security is an issue that needs due thoughtfulness from researchers in the horizon of unbundling of generation and transmission. Real power contingency ranking is an inherent part of security assessment. The target of contingency ranking and screening is to rapidly and precisely grade the decisive contingencies from a large list of plausible contingencies and rank them according to their severity for further rigorous analysis. In the proposed work, Wavelet Transform Based Artificial Neural Networks (WNN) is used for real power contingency ranking of the system. The results from offline AC load flow calculation are used to train the WNN for estimating the performance index. The effectiveness of the purported method is exhibited by contingency ranking on IEEE 14 bus, IEEE 5 bus systems and comparisons are made with conventional method. Good calculation accuracy, faster analysis times are obtained by using WNN.
Measurement of Relative Efficiency of State Owned Electric Utilities in INDIA Using Data Envelopment Analysis
R. Meenakumari,N. Kamaraj
Modern Applied Science , 2009, DOI: 10.5539/mas.v2n5p61
Abstract: In this paper two different DEA models were applied to evaluate the relative efficiency of State Owned Electric Utilities (SOEUs) in India. The DEA method was applied to find the overall efficiency, Technical Efficiency and Scale Efficiency. The Most Productive Scale Size (MPSS) is calculated for the scale inefficient utility. The results and discussions of this paper can be used to assist the authorities to pave the way for the improvement in technical and scale efficiency.
Application of Differential Evolution for Congestion Management in Power System
Sujatha Balaraman,Kamaraj N
Modern Applied Science , 2010, DOI: 10.5539/mas.v4n8p33
Abstract: In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. This paper proposes an algorithm for congestion management in a pool based electricity market based on differential evolution (DE). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost. Numerical results on test system namely IEEE 30 Bus System is presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the DE is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.
Particle Swarm Optimization Applications to Static Security Enhancement Using Multi Type Facts Devices
S. Sutha,N. Kamaraj
Journal of Artificial Intelligence , 2008,
Abstract: This study presents the application of particle swarm optimization algorithm to find the optimal location of multi type FACTS devices in a power system in order to eliminate or alleviate the line over loads. The optimizations are performed on the parameters, namely, the location of the devices, their types, their settings and installation cost of FACTS devices for double contingencies. The selection of UPFC and TCSC suitable location uses the criteria on the basis of improved system security. The effectiveness of the proposed method is tested for IEEE 6 bus and IEEE 30 bus test systems.
Optimized Image Steganalysis through Feature Selection using MBEGA
S. Geetha,N. Kamaraj
Computer Science , 2010,
Abstract: Feature based steganalysis, an emerging branch in information forensics, aims at identifying the presence of a covert communication by employing the statistical features of the cover and stego image as clues/evidences. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviours, optimizing the performance of steganalysers becomes an important open problem. This paper is focussed at fine tuning the performance of six promising steganalysers in this field, through feature selection. We propose to employ Markov Blanket-Embedded Genetic Algorithm (MBEGA) for stego sensitive feature selection process. In particular, the embedded Markov blanket based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive power in classifier model. Observations show that the proposed method is superior in terms of number of selected features, classification accuracy and computational cost than their existing counterparts.
CONGESTION MANAGEMENT IN DEREGULATED POWER SYSTEMS USING REAL CODED GENETIC ALGORITHM
Sujatha Balaraman,N.Kamaraj
International Journal of Engineering Science and Technology , 2010,
Abstract: In this paper, an efficient method has been proposed for transmission line over load alleviation in deregulated power system using real coded genetic algorithm (RCGA). For secure operation of power system, the network loading has to be maintained within specified limits. Transmission line congestion initiates the cascading outages which forces the system to collapse. Accurate prediction and alleviation of line overloads is the suitable corrective action to avoid network collapse. In this paper an attempt is made to explore the use of real coded genetic algorithm to find the optimal generation rescheduling for relieving congestion. The effectiveness of the proposed algorithm has been analyzed on IEEE 30 bus test system. The results obtained by the proposed method are found to be quite encouraging when compared with Simulated Annealing (SA) and hence it will be useful in electrical restructuring.
Categorizing Power System Stability Using Clustering Based Support Vector Machines
B. Dora Arul Selvi,N. Kamaraj
International Journal of Soft Computing , 2012,
Abstract: The current deregulation trend and the participation of many players are contributing to the decrease in security margin. This seeks the development of reliable and faster security monitoring methods. Support Vector Machines, a Neural Network Technology has been as presented an important contributor for reaching the goals of online Transient stability assessment. The training complexity of SVM is highly dependent on the size of data set. Since the power systems are of high dimensionality, feature extraction techniques must be implemented to make the application feasible. This study presents a new Clustering Based SVM to identify the stability status of power system. Here we have applied an exclusive clustering algorithm and an overlapping clustering algorithm, which scan the entire data set only once to provide SVM with high quality samples that carry the statistical summaries of the data such that the summaries maximize the benefit of learning the SVM. Transient stability of New England 39 Bus system is assessed by SVM trained with complete input feature set. The aspects of training time and classification accuracy are compared to the results obtained from CB-SVM.This shows that CB-SVM is highly useful for very large data sets while also generating high degree of classification accuracy.
Optimized Image Steganalysis Through Feature Selection Using MBEGA
S.Geetha,N.Kamaraj
International Journal of Computer Networks & Communications , 2010,
Abstract: Feature based steganalysis, an emerging branch in information forensics, aims at identifying the presenceof a covert communication by employing the statistical features of the cover and stego image asclues/evidences. Due to the large volumes of security audit data as well as complex and dynamic propertiesof steganogram behaviours, optimizing the performance of steganalysers becomes an important openproblem. This paper is focussed at fine tuning the performance of six promising steganalysers in this field,through feature selection. We propose to employ Markov Blanket-Embedded Genetic Algorithm (MBEGA)for stego sensitive feature selection process. In particular, the embedded Markov blanket based memeticoperators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improvethe solution and fine-tune the search. Empirical results suggest that MBEGA is effective and efficient ineliminating irrelevant and redundant features based on both Markov blanket and predictive power inclassifier model. Observations show that the proposed method is superior in terms of number of selectedfeatures, classification accuracy and computational cost than their existing counterparts.
Optimum Simultaneous Allocation of Renewable Energy DG and Capacitor Banks in Radial Distribution Network  [PDF]
Sivasangari Rajeswaran, Kamaraj Nagappan
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.711302
Abstract: Nowadays the optimal allocation of distributed generation (DG) in the distribution network becomes the popular research area in restructuring of power system. The capacitor banks introduced in the distribution networks for reactive power compensation also have the capacity to minimize the real and reactive power losses occurred in the system. Hence, this research integrates the allocation of renewable energy DG and capacitor banks in the radial distribution network to minimize the real power loss occurred in the system. A two-stage methodology is used for simultaneous allocation of renewable DG and capacitor banks. The optimum location of renewable energy DG and capacitor banks is determined using the distributed generation sitting index (DGSI) ranking method and the optimum sizing of DG and capacitor banks is found out for simultaneous placement using weight improved particle swarm optimization algorithm (WIPSO) and self adaptive differential evolution algorithm (SADE). This two-stage methodology reduces the burden of SADE and WIPSO algorithm, by using the DGSI index in determining the optimal location. Hence the computational time gets reduced which makes them suitable for online applications. By using the above methodology, a comprehensive performance analysis is done on IEEE 33 bus and 69 bus RDNs and the results are discussed in detail.
Fault Diagnosis of Parallel Transmission Lines Using Wavelet Based ANFIS
R. Rajeswari,N. Kamaraj,K.S. Swarup
International Journal of Electrical and Power Engineering , 2012,
Abstract: A new scheme to enhance the solution of the problems associated with parallel transmission line protection is presented in this study. This study demonstrates a novel application of wavelet transform to identify faults in parallel transmission line. The discrimination scheme which can automatically recognize the type of fault is proposed using ANFIS. The scheme can be separated into 2 stages, the time-frequency analysis of transients by wavelet transform and the pattern recognition to identify the type of fault. By using the actual fault data, it is shown that the proposed method provides satisfactory results for identifying the faults.
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