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>

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 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 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 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 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 Tailored Flower Pollination (TFP) Algorithm for Diminution of Real Power Loss

2019 ◽  
Vol 8 (2) ◽  
pp. 94
Author(s):  
Dr.Lenin Kanagasabai
Keyword(s):  
Chaos Theory ◽  
Power Loss ◽  
Reactive Power ◽  
Flower Pollination Algorithm ◽  
Real Power ◽  
Flower Pollination ◽  
Test Systems ◽  
Shuffled Frog Leaping ◽  
Simulation Results ◽  
Power Problem

In this paper, Tailored Flower Pollination (TFP) algorithm is proposed to solve the optimal reactive power problem. Comprising of the elements of chaos theory, Shuffled frog leaping search and Levy Flight, the performance of the flower pollination algorithm has been improved. Proposed TFP algorithm has been tested in standard IEEE 118 &amp; practical 191 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss.

scholarly journals Real power loss reduction by tundra wolf algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 100
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Loss Reduction ◽  
Real Power ◽  
Test Systems ◽  
Power Loss Reduction ◽  
Extensive Range ◽  
Global Optimal ◽  
Simulation Results ◽  
Power Problem

In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range.  Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.

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