scholarly journals REDUCTION OF REAL POWER LOSS BY UNIFIED ALGORITHM

2017 ◽  
Vol 5 (3) ◽  
pp. 243-251
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Optimization Problems ◽  
Test System ◽  
Initial Population ◽  
Local Optima ◽  
Real Power ◽  
Variable Mesh ◽  
Unified Algorithm ◽  
Power Problem

In this paper, we propose a new Unified Algorithm (UA) by combination of Variable mesh optimization algorithm (VMO) with Differential Evolution (DE) for solving reactive power problem. VMO has mainly three search operators, one for global exploration and two for local optima exploitation. DE is a simple yet commanding evolutionary algorithm for solving optimization problems. In all iteration VMO serve as the initial population of DE and obtains a population of more quality with this population VMO begins a new cycle.  The proposed UA has been tested in standard IEEE 30 bus test system and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables 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 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 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 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>

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 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.

Hybridization of Firefly and Water Wave Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 21 ◽  
pp. 165-171 ◽  
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Water Wave ◽  
Power Loss ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Test System ◽  
Heuristic Technique ◽  
Simulation Results ◽  
Power Problem

In this paper, a hybrid algorithm as the combination of Firefly and Water Wave algorithm (FWW) has been proposed to solve the Reactive power problem. The firefly algorithm is a meta-heuristic technique which is widely used for solving the optimization problems. The water wave optimization algorithm is also a nature inspired based algorithm. Both algorithms collectively improved the performance of search. The water wave algorithm is work on the combinatorial optimization and utilized as application of firefly algorithm. Hence we merge these two algorithms and make a hybrid algorithm. Proposed FWW algorithm has been tested in standard IEEE 30 Bus test system and simulation results reveal the better performance of the proposed algorithm in reducing the real power loss and voltage profiles were found to be within the limits.

scholarly journals A REDUCTION OF REAL POWER LOSS BY ENRICHED GENETIC ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 167-176
Author(s):  
K. Lenin
Keyword(s):  
Genetic Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Real Power ◽  
Dynamical Parameters ◽  
Power Problem ◽  
Standard Genetic Algorithm

In this paper Enriched Genetic Algorithm (EGA) is proposed to solve the optimal reactive power problem. In order to overcome the drawbacks of standard genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, some improved mechanisms based on non-linear ranking selection, competition and selection among several crossover offspring and adaptive change of mutation scaling are adopted in the genetic algorithm, and dynamical parameters are adopted in PSO. The new population is produced through three approaches to improve the global optimization performance. Proposed algorithm has been tested in standard IEEE 57 bus test system and simulation results reveal 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.

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