L
Laxmi Srivastava
Researcher at Madhav Institute of Technology and Science
Publications - 139
Citations - 2478
Laxmi Srivastava is an academic researcher from Madhav Institute of Technology and Science. The author has contributed to research in topics: Electric power system & Artificial neural network. The author has an hindex of 24, co-authored 130 publications receiving 2123 citations. Previous affiliations of Laxmi Srivastava include Massachusetts Institute of Technology.
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Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch
TL;DR: This paper proposes to apply a novel self-organizing hierarchical particle swarm optimization (SOH_PSO) for the nonconvex economic dispatch (NCED) and shows that the proposed approach outperforms previous methods for NCED.
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Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch
TL;DR: The practical NCED problem is solved here using PSO with a novel parameter automation strategy in which time varying acceleration coefficients are employed to efficiently control the local and global search, such that premature convergence is avoided and global solutions are achieved.
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Fast voltage contingency screening using radial basis function neural network
TL;DR: In this paper, an approach based on radial basis function neural network (RBFN) was proposed to rank the contingencies expected to cause steady state bus voltage violations, where Euclidean distance-based clustering technique was employed to select the number of hidden units and unit centers for the RBF neural network.
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Reserve constrained multi-area economic dispatch employing differential evolution with time-varying mutation
TL;DR: In this paper, the performance of various differential evolution (DE) strategies enhanced with time-varying mutation to solve the reserve constrained multi-area economic dispatch (MAED) problem is evaluated.
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Particle swarm optimization with crazy particles for nonconvex economic dispatch
TL;DR: ''crazy particles'' are introduced and their velocities are randomized to maintain momentum in the search and avoid saturation and the performance of the PSO with crazy particles has been tested on two model test systems, compared with GA and classical PSO and found to be superior.