scholarly journals ACTIVE POWER LOSS REDUCTION BY FLOWER POLLINATION ALGORITHM

2020 ◽  
Vol 5 (12) ◽  
pp. 223-231
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Biological Evolution ◽  
Point Of View ◽  
Flower Pollination Algorithm ◽  
Real Power ◽  
Flower Pollination ◽  
Power Loss Reduction ◽  
Nature Inspired Algorithm ◽  
Survival Of The Fittest

This paper presents Flower Pollination (FP) algorithm for solving the optimal reactive power problem. Minimization of real power loss is taken as key intent. Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. The biological evolution point of view, the objective of the flower pollination is the survival of the fittest and the optimal reproduction of plants in terms of numbers as well as the largely fittest. In order to evaluate the performance of the proposed Flower Pollination (FP) algorithm, it has been tested on IEEE 57 bus system and compared to other standard reported algorithms. Simulation results show that FP algorithm is better than other algorithms in reducing the real power loss and voltage profiles are within the limits.

scholarly journals Tailored Flower Pollination (TFP) Algorithm for Diminution of Real Power Loss

2019 ◽  
Vol 8 (2) ◽  
pp. 94
Author(s):  
Dr.Lenin Kanagasabai
Keyword(s):  
Chaos Theory ◽  
Power Loss ◽  
Reactive Power ◽  
Flower Pollination Algorithm ◽  
Real Power ◽  
Flower Pollination ◽  
Test Systems ◽  
Shuffled Frog Leaping ◽  
Simulation Results ◽  
Power Problem

In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.

scholarly journals DECREASE OF REAL POWER LOSS BY ADAPTED ALGORITHM

2018 ◽  
Vol 6 (8) ◽  
pp. 41-50
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Flower Pollination Algorithm ◽  
Initial Population ◽  
Lévy Flight ◽  
Levy Flight ◽  
Real Power ◽  
Flower Pollination ◽  
Particular Solution

In this paper, Adapted Flower Pollination (AFP) algorithm is proposed to solve the optimal reactive power problem. Flower pollination algorithm has been improved by comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight. In the AFP algorithm, the initial population is generated using the circle map, frog leaping local search is performed by each solution and when rand>p, modified Levy flight with integration of inertia weight in global pollination is performed on that particular solution. Proposed AFP algorithm has been tested in standard IEEE 57 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.

scholarly journals Real power loss reduction by hyena optimizer algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 186
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Mathematical Modelling ◽  
Convergent Evolution ◽  
Power Loss ◽  
Social Relationship ◽  
Reactive Power ◽  
Scent Marking ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

<p><span>To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour &amp; Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal &amp; deeds are analogous to canines in several elements of convergent evolution. Hyenas catch the prey with their teeth rather than claws – possess hardened skin feet with large, blunt, no retractable claws are adapted for running and make sharp turns. However, the hyenas' grooming, scent marking, defecating habits, mating and parental behaviour are constant with the deeds of other feliforms. Mathematical modelling is formulated for the basic attributes of Hyena. Standard IEEE 14,300 bus test systems used to analyze the performance of Hyena Optimizer (HO) algorithm. Loss has been reduced with control variables are within the limits.</span></p>

Real Power Loss Reduction by Billfish and Red Mullet Optimization Algorithms

2021 ◽  
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Voltage Stability ◽  
Reactive Power ◽  
Smart Cities ◽  
Fitness Function ◽  
Loss Reduction ◽  
Real Power ◽  
Red Mullet ◽  
Power Loss Reduction ◽  
Continuous Power

In this paper Billfish Optimization Algorithm (BOA) and Red Mullet Optimization (RMO) Algorithm has been designed for voltage stability enhancement and power loss reduction. Electrical Power is one among vital need in the society and also it plays lead role in formation of smart cities. Continuous power supply is essential and mainly quality of the power should be maintained in good mode. In this work real power loss reduction is key objective. Natural hunting actions of Billfish over pilchards are utilized to model the algorithm. Candidate solutions in the projected algorithm are Billfish and population in the exploration space is arbitrarily engendered. Movement of Billfish is high, it will attack the pilchards vigorously and it can’t escape from the attack done by the group of Billfish. Then in this paper Red Mullet Optimization (RMO) Algorithm is proposed to solve optimal reactive power problem. Projected RMO algorithm modeled based on the behavior and characteristics of red mullet. As a group they hunt for the prey and in each group there will be chaser and blocker. When the prey approaches any one of the blocker red mullet then automatically it will turn as new chaser. So roles will interchangeable and very much flexible. At any time chaser will become blocker and any of the blocker will become a chaser with respect to prey position and conditions. Then in that particular area when all the preys are hunted completed then red mullet group will change the area. So there will be flexibility and changing the role quickly with respect to prey position. Alike to that with reference to the fitness function the particle will be chosen as chaser. By means of considering L (voltage stability) - index BOA, and RMO algorithms verified in IEEE 30- bus system. Then without L-index BOA and RMO algorithms is appraised in 30 bus test systems. Both BOA and RMO algorithms condensed the power loss proficiently with improvement in voltage stability and minimization of voltage deviation.

scholarly journals Real Power Loss Reduction by Rock Dove Optimization and Fuligo Septica Optimization Algorithms

2020 ◽  
Vol 7 (2) ◽  
pp. E1-E6
Author(s):  
L. Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Power Transmission ◽  
Transmission Loss ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Real Power ◽  
Fitness Value ◽  
Power Loss Reduction

This paper aims to use the Rock Dove (RD) optimization algorithm and the Fuligo Septica optimization (FSO) algorithm for power loss reduction. Rock Dove towards a particular place is based on the familiar (sight) objects on the traveling directions. In the formulation of the RD algorithm, atlas and range operator, and familiar sight operators have been defined and modeled. Every generation number of Rock Dove is reduced to half in the familiar sight operator and Rock Dove segment, which hold the low fitness value that occupying the lower half of the generation will be discarded. Because it is implicit that the individual’s Rock Dove is unknown with familiar sights and very far from the destination place, a few Rock Doves will be at the center of the iteration. Each Rock Dove can fly towards the final target place. Then in this work, the FSO algorithm is designed for real power loss reduction. The natural vacillation mode of Fuligo Septica has been imitated to develop the algorithm. Fuligo Septica connects the food through swinging action and possesses exploration and exploitation capabilities. Fuligo Septica naturally lives in chilly and moist conditions. Mainly the organic matter in the Fuligo Septica will search for the food and enzymes formed will digest the food. In the movement of Fuligo Septica it will spread like a venous network, and cytoplasm will flow inside the Fuligo Septica in all ends. THE proposed RD optimization algorithm and FSO algorithm have been tested in IEEE 14, 30, 57, 118, and 300 bus test systems and simulation results show the projected RD and FSO algorithm reduced the real power loss. Keywords: optimal reactive power, transmission loss, Rock Dove, Fuligo Septica.

scholarly journals REAL POWER LOSS REDUCTION BY ENRICHED GENETIC ALGORITHM

2017 ◽  
Vol 5 (8) ◽  
pp. 18-25
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Optimization Problem ◽  
Reactive Power ◽  
Selection Procedure ◽  
Selection Method ◽  
Loss Reduction ◽  
Reactive Power Optimization ◽  
Real Power ◽  
Power Loss Reduction

In this paper, Enriched Genetic Algorithm (EGA) utilized to solve reactive power optimization problem. In the proposed algorithm Stochastic Universal Selection (SS) is utilized to improve the selection procedure. The selection method in Genetic algorithm (GA) plays a significant role in the runtime to get the optimized solution as well as in the superiority of the solution. In this work, an enriched selection technique is presented which uphold both fast runtime and elevated quality solution. Proposed EGA algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the advanced performance of the proposed algorithm in reducing the real power loss.

scholarly journals Real power loss reduction by dolphin swarm algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 285
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Spinner Dolphin ◽  
Simulation Results ◽  
Swarm Algorithm ◽  
Power Problem

<p>In this work Spinner Dolphin Swarm Algorithm (SDSA) has been applied to solve the optimal reactive power problem. Dolphins have numerous remarkable natural distinctiveness and living behavior such as echolocation, information interactions, collaboration, and partition of labor. Merging these natural distinctiveness and living behavior with swarm intelligence has been modeled to solve the reactive power problem. Proposed Spinner Dolphin Swarm Algorithm (SDSA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</p>

scholarly journals Real power loss reduction by arctic char algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 261
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Arctic Char ◽  
The Arctic ◽  
Real Power ◽  
Power Loss Reduction ◽  
Evolutionary Operators ◽  
Stochastic Mode ◽  
Power Problem

<span lang="IN">This </span><span>work presents Arctic Char </span><span lang="EN-GB">Algorithm (ACA) for solving optimal reactive power problem.</span><span> In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Deeds of Arctic char have been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). Projected Arctic Char </span><span lang="EN-GB">Algorithm (ACA) </span><span>has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.</span>

scholarly journals Real Power Loss Reduction by Cultivation of Soil Optimization Algorithm

2020 ◽  
Vol 7 (1) ◽  
pp. E1-E5
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Power Transmission ◽  
Transmission Loss ◽  
Real Power ◽  
Nutrient Contents ◽  
The Poor ◽  
Power Loss Reduction

In this paper, the optimal reactive power problem has been solved by the cultivation of soil optimization (CSO) algorithm. The reduction of real power loss is a key objective of this work. The projected CSO algorithm has been modeled based on the quality of soil which has been used in the cultivation of various crops season to season. With respect to the quality of the soil in the cultivation land, there will be a change in the poor-quality soil since there will up the gradation of the poor soil is done through by adding the nutrient contents. Depend upon the needs and about the type of cultivation farmers will improve the quality of the soil by adding valuable and various types of fertilizers (natural and artificial) such that it will enhance the fertile and growth (green) of the crops. Time to time farmers will choose appropriate nutrient contents that will be mixed with the soil in order to enhance the fertility of the soil. In standard IEEE 14, 30, 57 bus test systems Cultivation of Soil Optimization (CSO) algorithm has been tested. The CSO algorithm reduced the real power loss and control variables are within the limits. Keywords: optimal reactive power, transmission loss, cultivation soil optimization algorithm.

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