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Articles by N. Kamaraj
Total Records ( 3 ) for N. Kamaraj
  R. Rajeswari , N. Kamaraj and K.S. Swarup
  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.
  S. Sutha and N. Kamaraj
  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.
  B. Dora Arul Selvi and N. Kamaraj
  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.
 
 
 
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