scholarly journals REDUCTION OF ACTIVE POWER LOSS BY IMPROVED FROG LEAPING ALGORITHM

2017 ◽  
Vol 5 (9) ◽  
pp. 44-51
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Active Power ◽  
Swarm Optimization ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper presents Improved Frog Leaping (IFL) algorithm for solving optimal reactive power problem.  Comprehensive exploration capability of Particle Swarm Optimization (PSO) and   good local search ability of Frog Leaping Algorithm (FLA) has been hybridized to solve the reactive power problem and it overcomes the shortcomings of premature convergence. In order to evaluate the validity of the proposed Improved Frog Leaping (IFL) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard algorithms. Simulation results show that proposed Improved Frog Leaping (IFL) algorithm has reduced the real power loss considerably and voltage profiles are within the limits.

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 Integrated Algorithm for Decreasing Active Power Loss

2018 ◽  
Vol 7 (1) ◽  
pp. 33
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Firefly Algorithm ◽  
Reactive Power ◽  
Cuckoo Search ◽  
Active Power ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Real Power ◽  
Simulation Results ◽  
Power Problem

This paper projects an Integrated Algorithm (IA) for solving optimal reactive power problem. Quick convergence of the Cuckoo Search (CS), the vibrant root change of the Firefly Algorithm (FA), and the incessant position modernization of the Particle Swarm Optimization (PSO) has been combined to form the Integrated Algorithm (IA).  In order to evaluate the efficiency of the proposed Integrated Algorithm (IA), it has been tested in standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show that Integrated Algorithm (IA) is considerably reduced the real power loss and voltage profile 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 SUPERLATIVE ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

2017 ◽  
Vol 5 (10) ◽  
pp. 101-111
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Optimization Methods ◽  
Direction Finding ◽  
Active Power ◽  
Real Power ◽  
The Real ◽  
Spinner Dolphin ◽  
Result Show ◽  
Power Problem

This paper proposes Spinner Dolphin Algorithm (SDA) for solving optimal reactive power problem. Echolocation is the genetic sonar used by Spinner dolphin & it used by few kinds of other animals for direction-finding, hunting in diverse environments. This ability of Spinner dolphin is imitated in this paper to develop a new-fangled procedure for solving optimal reactive power problem. Spinner Dolphin Algorithm (SDA) takes reward of the overriding rules and outperforms many vigorous optimization methods. The new-fangled approach SDA leads to exceptional results with small computational efforts. In order to evaluate the efficiency of the proposed algorithm, it has been tested Standard IEEE 57,118 bus systems and compared to other specified algorithms. Simulation result show that Spinner Dolphin Algorithm (SDA) is advanced to other algorithms in reducing the real power loss and voltage profiles are within the limits

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 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.

scholarly journals REDUCTION OF REAL POWER LOSS BY LAVA HERON OPTIMIZATION ALGORITHM

2017 ◽  
Vol 5 (8) ◽  
pp. 85-93
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Saline Water ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper presents a new Lava Heron Optimization (LHO) Algorithm for solving reactive power problem. This algorithm is inspired by the grab skill of the Lava Heron bird. Lava heron bird live in on the freshwater or saline water, swampy marshes or wetlands with tuft of trees mostly in low lying areas, where there are abundant convenience of fishes as their prey. By using the prey catching skill of the Lava Heron bird algorithm has been framed and utilized to minimize the real power loss. Proposed Lava Heron Optimization (LHO) Algorithm has been tested in standard IEEE 57,118 bus systems and simulation results demonstrate the commendable performance of the projected Lava Heron Optimization (LHO) Algorithm in reducing the real power loss.

scholarly journals DRAG & AVERSION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2017 ◽  
Vol 5 (11) ◽  
pp. 168-176
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Model ◽  
Real Power ◽  
Basic Particle ◽  
Power Problem

This paper projects Drag & Aversion Particle Swarm Optimization (DAPSO) algorithm is applied to solve optimal reactive power problem. In DAPSO the idea of decreasing and increasing diversity operators used to control the population into the basic Particle Swarm Optimization (PSO) model. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. The results show that both Drag & Aversion Particle Swarm Optimization (DAPSO) prevents premature convergence to enhanced level but still keeps a rapid convergence. Proposed Drag & Aversion Particle Swarm Optimization (DAPSO) has been tested in standard IEEE 118 & practical 191 bus test systems. Real power loss has been considerably reduced and voltage profiles are within the limits.

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 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>

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