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 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.

Real Power Loss Reduction by Enhanced Trailblazer Optimization Algorithm

2021 ◽  
pp. 77-93
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Local Optimum ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Real Power ◽  
Tlbo Algorithm ◽  
Power Loss Reduction ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In this paper Teaching learning based Trailblazer optimization algorithm (TLBOTO) is used for solving the power loss lessening problem. Trailblazer optimization algorithm (TOA) is alienated into dual phases for exploration: trailblazer phase and adherent phase. Both phases epitomize the exploration and exploitation phase of TOA correspondingly. Nevertheless, in order to avoid the solution falling in local optimum in this paper Teaching-learning-based optimization (TLBO) is integrated with TOA approach. Learning segment of the TLBO algorithm is added to the adherent phase. Proposed Teaching learning based Trailblazer optimization algorithm (TLBOTO) augment exploration capability of the algorithm and upsurge the convergence speed. Algorithm's exploration competences enhanced by linking the teaching phase and learning. Exploration segment of the trailblazer algorithm identifies the zone with the pre-eminent solution. Subsequently inducing the teaching process, the trailblazer performs as a teacher to teach additional entities and engender a new-fangled entity. The new-fangled unit is equated with the trailblazer, and with reference to the greedy selection norm, the optimal one is designated as the trailblazer to endure exploration. The location of trailblazer is modernized. Legitimacy of the Teaching learning based Trailblazer optimization algorithm (TLBOTO) is substantiated in IEEE 30 bus system (with and devoid of L-index). Actual power loss lessening is reached. Proportion of actual power loss lessening is augmented

scholarly journals Power loss reduction by gryllidae optimization algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 179
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
The Other ◽  
Loss Reduction ◽  
Real Power ◽  
Test Systems ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

<p><span lang="EN-US">This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.</span></p>

scholarly journals ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 149-156
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Harmony Search ◽  
Pso Algorithm ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
De Algorithm ◽  
Power Loss Reduction ◽  
Power Problem

In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show’s that Synthesized Algorithm (SA) successfully reduces the real power loss and voltage profiles are within the limits.

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.

Real Power Loss Reduction by Cinnamon ibon Search Optimization Algorithm

2021 ◽  
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Search Algorithm ◽  
Loss Reduction ◽  
Real Power ◽  
Search Optimization ◽  
Power Loss Reduction ◽  
Voltage Stability Enhancement ◽  
Voltage Deviation ◽  
Stability Enhancement

In this paper Cinnamon ibon Search Optimization Algorithm (CSOA) is used for solving the power loss lessening problem. Key objectives of the paper are Real power Loss reduction, Voltage stability enhancement and minimization of Voltage deviation. Searching and scavenging behavior of Cinnamon ibon has been imitated to model the algorithm. Cinnamon ibon birds which are in supremacy of the group are trustworthy to be hunted by predators and dependably attempt to achieve a improved position and the Cinnamon ibon ones that are positioned in the inner of the population, drive adjacent to the nearer populations to dodge the threat of being confronted. The systematic model of the Cinnamon ibon search Algorithm originates with an arbitrary individual of Cinnamon ibon. The Cinnamon ibon search algorithm entities show the position of the Cinnamon ibon. Besides, the Cinnamon ibon bird is supple in using the cooperating plans and it alternates between the fabricator and the cadger. Successively the Cinnamon ibon identifies the predator position; then they charm the others by tweeting signs. The cadgers would be focussed to the imperilled regions by fabricators once the fear cost is more than the defence threshold. Likewise, the subterfuge of both the cadger and the fabricator is commonly used by Cinnamon ibon. The dispersion of the Cinnamon ibon location in the solution area is capricious. An impulsive drive approach was applied when dispossession of any adjacent Cinnamon ibon in the purlieu of the present population. This style diminishes the convergence tendency and decreases the convergence inexorableness grounded on the controlled sum of iterations. Authenticity of the Cinnamon ibon Search Optimization Algorithm (CSOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Proportion of actual power loss lessening is amplified.

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 Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

2021 ◽  
Vol 8 (1) ◽  
pp. E1-E8
Author(s):  
L. Kanagasabai
Keyword(s):  
Heat Transfer ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Coronary Circulation ◽  
Circulation System ◽  
Loss Reduction ◽  
Real Power ◽  
Development Factor ◽  
Power Loss Reduction

In this paper, the heat transfer optimization (HTO) algorithm and simulated coronary circulation system (SCCS) optimization algorithm has been designed for Real power loss reduction. In the projected HTO algorithm, every agent is measured as a cooling entity and surrounded by another agent, like where heat transfer will occur. Newton’s law of cooling temperature will be updated in the proposed HTO algorithm. Each value of the object is computed through the objective function. Then the objects are arranged in increasing order concerning the objective function value. This projected algorithm time “t” is linked with iteration number, and the value of “t” for every agent is computed. Then SCCS optimization algorithm is projected to solve the optimal reactive power dispatch problem. Actions of human heart veins or coronary artery development have been imitated to design the algorithm. In the projected algorithm candidate solution is made by considering the capillaries. Then the coronary development factor (CDF) will appraise the solution, and population space has been initiated arbitrarily. Then in the whole population, the most excellent solution will be taken as stem, and it will be the minimum value of the Coronary development factor. Then the stem crown production is called the divergence phase, and the other capillaries’ growth is known as the clip phase. Based on the arteries leader’s coronary development factor (CDF), the most excellent capillary leader’s (BCL) growth will be there. With and without L-index (voltage stability), HTO and SCCS algorithm’s validity are verified in IEEE 30 bus system. Power loss minimized, voltage deviation also reduced, and voltage stability index augmented.

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