scholarly journals Opposition based red wolf algorithm for solving optimal reactive power problem

2021 ◽  
Vol 10 (3) ◽  
pp. 193
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Global Optimum ◽  
Real Power ◽  
Red Wolf ◽  
Opposition Based Learning ◽  
Candidate Solution ◽  
Simulation Results ◽  
Power Problem

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>

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 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.

Propagation Algorithm for Solving Optimal Reactive Power Problem

2016 ◽  
Vol 24 ◽  
pp. 103-111
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Natural Phenomenon ◽  
Heuristic Optimization ◽  
Real Power ◽  
Test Systems ◽  
Propagation Algorithm ◽  
Simulation Results ◽  
Power Problem

This paper presents a nature inspired heuristic optimization algorithm based on lightning progression called the propagation algorithm (PA) to solve optimal reactive power problem. It is from the imitated natural phenomenon of lightning and the procedure of step frontrunner propagation using the theory of fast particles. Three particle kinds are established to distinguish the transition particles that produce the first step frontrunner population, the space particles that attempt to turn out to be the frontrunner, and the prime particle that epitomize the particle thrilled from best positioned step frontrunner. The proposed PA has been tested in standard IEEE 30,57,118 bus test systems 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.

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 Polar wolf optimization algorithm for solving optimal reactive power problem

2020 ◽  
Vol 9 (2) ◽  
pp. 107
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Social Hierarchy ◽  
Real Power ◽  
Test Systems ◽  
Final Level ◽  
First Case ◽  
Simulation Results ◽  
Power Problem

<span>This paper proposes polar wolf optimization (PWO) algorithm to solve the optimal reactive power problem. Proposed algorithm enthused from actions of polar wolves. Leader’s wolves which denoted as </span>x<sub>α</sub> <span>are accountable for taking judgment on hunting, resting place, time to awaken etc. second level is </span>x<sub>β</sub> <span>those acts when there is need of substitute in first case. Then </span>x<sub>γ</sub> <span>be as final level of the wolves. In the modeling social hierarchy is developed to discover the most excellent solutions acquired so far. Then the encircling method is used to describe circle-shaped vicinity around every candidate solutions. In order to agents work in a binary space, the position modernized accordingly. Proposed PWO algorithm has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show the projected algorithms reduced the real power loss considerably.</span>

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 Active power loss reduction by opposition based kidney search algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 217
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Loss Reduction ◽  
Opposition Based Learning ◽  
Candidate Solution ◽  
Power Loss Reduction ◽  
Power Problem

In this work Opposition based Kidney Search Algorithm (OKS) is used to solve the optimal reactive power problem. Kidney search algorithm imitates the various sequences of functions done by biological kidney. Opposition based learning (OBL) stratagem is engaged to commence the algorithm. This is to make certain high-quality of preliminary population and to expand the exploration steps in case of stagnation of the most excellent solutions. Opposition based learning (OBL) is one of the influential optimization tools to boost the convergence speed of different optimization techniques. The thriving implementation of the OBL engages evaluation of opposite population and existing population in the similar generation to discover the superior candidate solution of a given reactive power problem.  Proposed Opposition based Kidney Search Algorithm (OKS) has been tested in standard IEEE 14, 30, 57,118,300 bus test systems and simulation results show that the proposed algorithm reduced the real power loss efficiently.

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