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All Journal IAES International Journal of Robotics and Automation (IJRA) International Journal of Advances in Applied Sciences
Lenin Kanagasabai
Jawaharlal Nehru Technological University
Automotive Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : IAES International Journal of Robotics and Automation (IJRA)

 1 
REAL POWER LOSS REDUCTION BY ENRICHED BLACK FISH OPTIMIZATION ALGORITHM Kanagasabai, Lenin
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v9i4.pp%p

Abstract

 In this paper Enriched Black Fish Algorithm (EBA) has been utilized to solve the optimal reactive power problem. Bubble net hunting tactic has been imitated to form the Black Fish Algorithm (BA). Modernized solution is mainly depended on the current best candidate solution in Black fish optimization algorithm (BA). An inertia weight ? ? [1, 0] is introduced into Black fish optimization algorithm alike in particle swarm optimization algorithm, to acquire the Enriched black fish optimization algorithm (EBA). Roulette wheel selection method has been used to perk up the convergence rate of proposed enriched black fish optimization algorithm (EBA). The proposed EBA has been tested in standard IEEE 57,118 bus systems and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limit
Advanced teaching-learning-based optimization algorithm for actual power loss reduction Kanagasabai, Lenin
IAES International Journal of Robotics and Automation (IJRA) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (475.732 KB) | DOI: 10.11591/ijra.v9i1.pp46-50

Abstract

In this work Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) is proposed to solve the optimal reactive power problem. Teaching-Learning-Based Optimization (TLBO) optimization algorithm has been framed on teaching learning methodology happening in classroom. Algorithm consists of “Teacher Phase”, “Learner Phase”. In the proposed Advanced Teaching-Learning-Based Optimization algorithm non-linear inertia weighted factor is introduced into the fundamental TLBO algorithm to manage the memory rate of learners. In order to control the learner’s mutation arbitrarily during the learning procedure a non-linear mutation factor has been applied. Proposed Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.
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