scholarly journals Real power loss diminution by predestination of particles wavering search algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 92
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Real Power ◽  
Gradient Based ◽  
Local Optimal Solution ◽  
Simulation Results ◽  
Power Problem ◽  
Steady Velocity

In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion.  Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.

scholarly journals Enhanced wormhole optimizer algorithm for solving optimal reactive power problem

2020 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Black Hole ◽  
Power Loss ◽  
High Probability ◽  
Inflation Rate ◽  
Reactive Power ◽  
Optimal Solution ◽  
Real Power ◽  
Local Optimal Solution ◽  
Simulation Results ◽  
Power Problem

In this paper Enhanced Wormhole Optimizer (EWO) algorithm is used to solve optimal reactive power problem. Proposed algorithm based on the Wormholes which exploits the exploration space. Between different universes objects are exchanged through white or black hole tunnels. Regardless of the inflation rate, through wormholes objects in all universes which possess high probability will shift to the most excellent universe. In the projected Enhanced Wormhole Optimizer (EWO) algorithm in order to avoid the solution to be get trapped into the local optimal solution Levy flight has been applied.  Projected Enhanced Wormhole Optimizer (EWO) algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the EWO algorithm reduced the real power loss efficiently.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY COYOTE SEARCH ALGORITHM

2018 ◽  
Vol 6 (10) ◽  
pp. 130-138
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Special Property ◽  
Test System ◽  
Active Power ◽  
Real Power ◽  
Simulation Results ◽  
Searching Ability ◽  
Power Problem

This paper presents Coyote Search Algorithm (CSA) for solving optimal reactive power problem. Coyote Search Algorithm is a new bio – inspired heuristic algorithm which based on coyote preying behaviour. The way coyote search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power problem. And the specialty of coyote is possessing both individual local searching ability & autonomous flocking movement and this special property has been utilized to formulate the search algorithm. The proposed Coyote Search Algorithm (CSA) has been tested on standard IEEE 57 bus test system and simulation results shows clearly about the good performance of the proposed algorithm in reducing the real power loss.

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 114-118
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Premature Convergence ◽  
Speed Of Convergence ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

scholarly journals AMPLIFIED NELDER-MEAD ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2018 ◽  
Vol 6 (11) ◽  
pp. 299-306
Author(s):  
K. Lenin
Keyword(s):  
Simulated Annealing ◽  
Power Loss ◽  
Reactive Power ◽  
Superior Performance ◽  
Real Power ◽  
Last Stage ◽  
Simulation Results ◽  
Power Problem ◽  
Nelder Mead Algorithm ◽  
Prime Solution

This paper presents Hybridization of Simulated Annealing with Nelder-Mead algorithm (SN) is proposed to solve optimal reactive power problem. The proposed Hybridized - Simulated Annealing, Nelder-Mead algorithm starts with a prime solution, which is produced arbitrarily and then the solution is disturbed into partitions. The vicinity zone is created, arbitrary numbers of partitions are selected and variables modernizing procedure is started in order to create a trail of neighbour solutions. This procedure helps the SN algorithm to explore the region around an existing iterate solution. The Nelder- Mead algorithm is used in the last stage in order to progress the most excellent solution found so far and hasten the convergence in the closing stage. The proposed Hybridization of Simulated Annealing with Nelder-Mead algorithm (SN) has been tested in standard IEEE 57,118 bus systems and simulation results show the superior performance of the proposed SN algorithm in reducing the real power loss and voltage profiles are within the limits.

scholarly journals ENHANCED SPIDER ALGORITHM FOR MINIMIZATION OF REAL POWER LOSS

2018 ◽  
Vol 6 (4) ◽  
pp. 301-311
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
High Quality ◽  
Quality Performance ◽  
Real Power ◽  
Social Spiders ◽  
The Real ◽  
Spider Web ◽  
Simulation Results ◽  
Power Problem

In this paper Enhanced Spider (ES) algorithm is proposed to solve reactive power Problem. Enthused by the spiders, a new Enhanced Spider (ES) algorithm is utilized to solve reactive power problem. The composition is primarily based on the foraging approach of social spiders, which make use of of the vibrations spread over the spider web to choose the position of prey. The simulation results demonstrate high-quality performance of Enhanced Spider (ES) algorithm in solving reactive power problem.  The projected Enhanced Spider (ES) algorithm has been tested in standard IEEE 57,118 bus systems and compared to other reported standard algorithms. Results show that Enhanced Spider (ES) algorithm is more efficient than other algorithms in reducing the real power loss.

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 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 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 ENHANCED MINE BLAST ALGORITHM FOR SOLVING REACTIVE POWER PROBLEM

2017 ◽  
Vol 5 (9) ◽  
pp. 206-216
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Convergence Speed ◽  
Superior Performance ◽  
Real Power ◽  
The Real ◽  
Test Systems ◽  
Simulation Results ◽  
Bomb Explosion ◽  
Power Problem

In this paper Enhanced Mine Blast (EMB) algorithm which based on mine bomb explosion concept is proposed to solve optimal reactive power problem.The clue of the projected Enhanced Mine Blast (EMB) algorithm is based on the examination of a mine bomb explosion, in which the thrown pieces of shrapnel crash with other mine bombs near the explosion area resulting in their explosion. In this paper convergence speed has been enhanced. Proposed Enhanced Mine Blast (EMB) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the projected Enhanced Mine Blast (EMB) algorithm in reducing the real power loss.

scholarly journals Diminution of real power loss by novel gentoo penguin algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 151
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Absorption Coefficient ◽  
Power Loss ◽  
Reactive Power ◽  
Low Cost ◽  
Test System ◽  
Gentoo Penguin ◽  
Heat Radiation ◽  
Real Power ◽  
Simulation Results ◽  
Power Problem

<span lang="EN-US">In this paper Gentoo Penguin Algorithm (GPA) is proposed to solve optimal reactive power problem. Gentoo Penguins preliminary population possesses heat radiation and magnetizes each other by absorption coefficient. Gentoo Penguins will move towards further penguins which possesses low cost (elevated heat concentration) of absorption. Cost is defined by the heat concentration, distance. Gentoo Penguins penguin attraction value is calculated by the amount of heat prevailed between two Gentoo penguins. Gentoo Penguins heat radiation is measured as linear. Less heat is received in longer distance, in little distance, huge heat is received. Gentoo Penguin Algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.</span>

Sign in / Sign up

Export Citation Format

Share Document