Bamboo Plant Intellect Deeds Optimization Algorithm for Solving Optimal Reactive Power Problem

2020 ◽  
pp. 665-672
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Reactive Power ◽  
Bamboo Plant ◽  
Power Problem

scholarly journals Shrinkage of Real Power Loss by Enriched Brain Storm Optimization Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Human Being ◽  
New Approach ◽  
Real Power ◽  
Brain Storm Optimization ◽  
Course Of Action ◽  
Simulation Results ◽  
Power Problem

<p class="Author">This paper proposes Enriched Brain Storm Optimization (EBSO) algorithm is used for soving reactive power problem. Human being are the most intellectual creature in this world. Unsurprisingly, optimization algorithm stimulated by human being inspired problem solving procedure should be advanced than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, we commence a new Enriched brain storm optimization algorithm, which was enthused by the human brainstorming course of action. In the projected Enriched Brain Storm Optimization (EBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process. The most important view of the vibrant clustering strategy is that; regularly execute the k-means clustering after a definite number of generations, so that the swapping of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Enriched Brain Storm Optimization (EBSO) algorithm, has been tested standard IEEE 118 &amp; practical 191 bus test systems and compared to other standard reported algorithms. Simulation results show that Enriched Brain Storm Optimization (EBSO) algorithm is superior to other algorithms in reducing the real power loss.</p>

scholarly journals TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2020 ◽  
Vol 5 (12) ◽  
pp. 246-255
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neighbourhood Search ◽  
A Cell ◽  
Power Problem

This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search.  In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation results show that Tailored Particle Swarm Optimization (TPSO) algorithm is superior to other algorithms in reducing the real power loss.

scholarly journals REDUCTION OF TRUE POWER LOSS BY IMPROVED ALGORITHM

2018 ◽  
Vol 6 (8) ◽  
pp. 105-113
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
New Approach ◽  
Real Power ◽  
Brain Storm Optimization ◽  
Course Of Action ◽  
Simulation Results ◽  
Power Problem

This paper proposes Improved Brain Storm Optimization (IBSO) algorithm is used for solving reactive power problem. predictably, optimization algorithm stimulated by human being inspired problem-solving procedure should be highly developed than the optimization algorithms enthused by collective deeds of ants, bee, etc. In this paper, a new Improved brain storm optimization algorithm defined, which was stimulated by the human brainstorming course of action. In the projected Improved Brain Storm Optimization (IBSO) algorithm, the vibrant clustering strategy is used to perk up the k-means clustering process & exchange of information wrap all ideas in the clusters to accomplish suitable searching capability. This new approach leads to wonderful results with little computational efforts. In order to evaluate the efficiency of the proposed Improved Brain Storm Optimization (IBSO) algorithm, has been tested standard IEEE 30 bus test system and compared to other standard reported algorithms. Simulation results show that Improved Brain Storm Optimization (IBSO) algorithm is superior to other algorithms in reducing the real power loss.

scholarly journals Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 1 (3) ◽  
pp. 125
Author(s):  
Kanagasabai Lenin ◽  
Bhumanapally Ravindhranath Reddy ◽  
Munagala Surya Kalavathi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Position Vector ◽  
Swarm Optimization ◽  
Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.

scholarly journals Solving optimal reactive power problem by enhanced fruit fly optimization algorithm and status of material algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 100
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Reactive Power ◽  
Collision Operator ◽  
Fruit Fly ◽  
Test System ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Vector Operator ◽  
Solid Liquid ◽  
Power Problem

<div data-canvas-width="397.2735184438622">This paper proposes enhanced fruit fly optimization algorithm (EFF) and status of material algorithm (SMA) to solve the optimal reactive power problem. Fruit fly optimization algorithm is based on the food finding behavior of the fruit fly. There are two steps in food finding procedure of fruit fly: At first it smells the food source by means of osphresis organ and it flies in that direction; afterwards, when it gets closer to the food site, through its sensitive vision it will find the food. At the beginning of the run by diminishing the inertia weight from a large value to a small value, will lead to enhance the global search capability and more local search ability will be in process the end of the run of the EFF algorithm. Then SMA is projected to solve the problem. Three state of material are solid, liquid, and gas. For evolution procedure direction vector operator assign a direction to every molecule consecutively to guide the particle progression. Collision operator imitates the collisions factor in which molecules are interacting to each other. Proposed enhanced EFF, SMA has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithms reduced the real power loss considerably.</div>

scholarly journals Two bio-inspired algorithms for solving optimal reactive power problem

2020 ◽  
Vol 9 (3) ◽  
pp. 180
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Sperm Motility ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Spontaneous Movement ◽  
Real Power ◽  
The Real ◽  
Test Systems ◽  
Power Problem

<span>In this work two ground-breaking algorithms called; Sperm Motility (SM) algorithm &amp; Wolf Optimization (WO) algorithm is used for solving reactive power problem. In sperm motility approach spontaneous movement of the sperm is imitated &amp; species chemo attractant, sperms are enthralled in the direction of the ovum. In wolf optimization algorithm the deeds of wolf is imitated in the formulation &amp; it has a flag vector also length is equivalent to the whole sum of numbers in the dataset the optimization. Both the projected algorithms have been tested in standard IEEE 57,118, 300 bus test systems. Simulated outcomes reveal about the reduction of real power loss &amp; with variables are in the standard limits. Almost both algorithms solved the problem efficiently, yet wolf optimization has slight edge over the sperm motility algorithm in reducing the real power loss.</span>

scholarly journals A Novel Merchant Optimization Algorithm for Solving Optimal Reactive Power Problem

2021 ◽  
pp. 51-56
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Test Systems ◽  
Candidate Solution ◽  
The One ◽  
Power Problem

In this paper Merchant Optimization Algorithm (MOA) is proposed to solve the optimal reactive power problem. Projected algorithm is modeled based on the behavior of merchants who gain in the market through various mode and operations. Grouping of the traders will be done based on their specific properties, and by number of candidate solution will be computed to individual merchant. First Group named as “Ruler candidate solution” afterwards its variable values are dispersed to the one more candidate solution and it named as “Serf candidate solution” In standard IEEE 14, 30, 57 bus test systems Merchant Optimization Algorithm (MOA) have been evaluated.  Results show the proposed algorithm reduced power loss effectively.

scholarly journals Solving optimal reactive power problem by improved variable mesh optimization algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 279
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Differential Evolution ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Iteration Process ◽  
Test System ◽  
Mesh Optimization ◽  
Local Optima ◽  
De Algorithm ◽  
Variable Mesh ◽  
Power Problem

<p>In this work Improved Variable Mesh Optimization Algorithm (IVM) has been applied to solve the optimal reactive power problem. Projected Improved VMO algorithm has been modeled by hybridization of Variable mesh optimization algorithm with Clearing-Based Niche Formation Technique, Differential Evolution (DE) algorithm. Mesh formation and exploration has been enhanced by the hybridization. Amongst of niche development process, clearing is a renowned method in which general denominator is the formation of steady subpopulations (niches) at all local optima (peaks) in the exploration space. In Differential Evolution (DE) population is formed by common sampling within the stipulated smallest amount and maximum bounds. Subsequently DE travel into the iteration process where the progressions like, mutation, crossover, and selection, are followed. Proposed Improved Variable Mesh Optimization Algorithm (IVM) has been tested in standard IEEE 14,300 bus test system and simulation<br />results show the projected algorithm reduced the real power loss extensively.</p>

scholarly journals Enhanced whale optimization algorithm for active power loss diminution

2020 ◽  
Vol 9 (1) ◽  
pp. 19
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Active Power ◽  
Whale Optimization Algorithm ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Power Problem

In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.

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