Real Power Loss Minimization By Mutual Mammal Behavior Algorithm

This paper presents a Mutual Mammal Behavior (MM) algorithm for solving Reactive power problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization is taken is taken as main objective. Generator terminal voltages, reactive power generation...

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Main Author: Dr.K.Lenin
Format: Text
Language:unknown
Published: Zenodo 2017
Subjects:
Online Access:https://dx.doi.org/10.5281/zenodo.601477
https://zenodo.org/record/601477
id ftdatacite:10.5281/zenodo.601477
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spelling ftdatacite:10.5281/zenodo.601477 2023-05-15T15:45:14+02:00 Real Power Loss Minimization By Mutual Mammal Behavior Algorithm Dr.K.Lenin 2017 https://dx.doi.org/10.5281/zenodo.601477 https://zenodo.org/record/601477 unknown Zenodo https://dx.doi.org/10.5281/zenodo.583889 Open Access Creative Commons Attribution 4.0 https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess CC-BY Reactive Power; Transmission Loss; Mutual Mammal Behavior; Optimization. Text Journal article article-journal ScholarlyArticle 2017 ftdatacite https://doi.org/10.5281/zenodo.601477 https://doi.org/10.5281/zenodo.583889 2021-11-05T12:55:41Z This paper presents a Mutual Mammal Behavior (MM) algorithm for solving Reactive power problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization is taken is taken as main objective. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. A Meta heuristic algorithm for global optimization called the Mutual Mammal Behavior (MM) is introduced. Mammal groups like Carnivores, African lion, Cheetah, Dingo Fennec Fox, Moose, Polar Bear, Sea Otter, Blue Whale, Bottlenose Dolphin exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These Mutual behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of Mammals which interact with each other based on the biological laws of Mutual motion. MM powerful stochastic optimization technique has been utilized to solve the reactive power optimization problem. In order to evaluate up the performance of the proposed algorithm, it has been tested on Standard IEEE 57,118 bus systems. Proposed MM algorithm out performs other reported standard algorithm’s in reducing real power loss. Text Blue whale DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Reactive Power; Transmission Loss; Mutual Mammal Behavior; Optimization.
spellingShingle Reactive Power; Transmission Loss; Mutual Mammal Behavior; Optimization.
Dr.K.Lenin
Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
topic_facet Reactive Power; Transmission Loss; Mutual Mammal Behavior; Optimization.
description This paper presents a Mutual Mammal Behavior (MM) algorithm for solving Reactive power problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization is taken is taken as main objective. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. A Meta heuristic algorithm for global optimization called the Mutual Mammal Behavior (MM) is introduced. Mammal groups like Carnivores, African lion, Cheetah, Dingo Fennec Fox, Moose, Polar Bear, Sea Otter, Blue Whale, Bottlenose Dolphin exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These Mutual behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of Mammals which interact with each other based on the biological laws of Mutual motion. MM powerful stochastic optimization technique has been utilized to solve the reactive power optimization problem. In order to evaluate up the performance of the proposed algorithm, it has been tested on Standard IEEE 57,118 bus systems. Proposed MM algorithm out performs other reported standard algorithm’s in reducing real power loss.
format Text
author Dr.K.Lenin
author_facet Dr.K.Lenin
author_sort Dr.K.Lenin
title Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
title_short Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
title_full Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
title_fullStr Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
title_full_unstemmed Real Power Loss Minimization By Mutual Mammal Behavior Algorithm
title_sort real power loss minimization by mutual mammal behavior algorithm
publisher Zenodo
publishDate 2017
url https://dx.doi.org/10.5281/zenodo.601477
https://zenodo.org/record/601477
genre Blue whale
genre_facet Blue whale
op_relation https://dx.doi.org/10.5281/zenodo.583889
op_rights Open Access
Creative Commons Attribution 4.0
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
op_rightsnorm CC-BY
op_doi https://doi.org/10.5281/zenodo.601477
https://doi.org/10.5281/zenodo.583889
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