D. Devaraj
Kalasalingam University
178 Papers
481 Citations
D. Devaraj is an academic researcher from Kalasalingam University. The author has contributed to research in topics: Computer science & Electric power system. The author has an hindex of 28, co-authored 166 publications. Previous affiliations of D. Devaraj include Indian Institute of Technology Madras.
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Papers
Genetic-algorithm-based optimal power flow for security enhancement
D. Devaraj,B. Yegnanarayana +1 more
- 14 Nov 2005
TL;DR: In this paper, a GA-based OPF algorithm for identifying the optimal values of generator active-power output and the angle of the phase-shifting transformer is presented, where the locations of phase shifters are selected based on sensitivity analysis.
307
Genetic algorithm based reactive power dispatch for voltage stability improvement
D. Devaraj,J. Preetha Roselyn +1 more
TL;DR: In this paper, an improved GA approach for voltage stability enhancement is presented, which is based on the minimization of the maximum of L-indices of load buses of a modern energy control centre.
174
Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection
TL;DR: A novel intrusion detection system with genetic-algorithm-based feature selection and multiple support vector machine classifiers for wireless mesh networks are proposed and demonstrates that the proposed system exhibits a high accuracy of attack detection and is suitable for intrusion detection in wirelessMesh networks.
164
Improved genetic algorithm for multi-objective reactive power dispatch problem
TL;DR: In this article, an improved GA approach for solving the multi-objective reactive power dispatch problem is presented, where loss minimization and maximization of voltage stability margin are taken as the objectives.
120
Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification.
M. Jansi Rani,D. Devaraj +1 more
TL;DR: A Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification is proposed and results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods.
98