scholarly journals REAL POWER LOSS REDUCTION ENHANCED ARTIFICIAL BEE COLONY ALGORITHM

2018 ◽  
Vol 6 (3) ◽  
pp. 203-213
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Search Space ◽  
Loss Reduction ◽  
Real Power ◽  
Bee Colony ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Enhanced Artificial Bee Colony (EABC) algorithm is proposed for solving optimal reactive power problem. The projected method assimilates crossover operation from Genetic Algorithm (GA) with artificial bee colony (ABC) algorithm. The EABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space.  Projected EABC algorithm has been tested on has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm 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 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 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.

scholarly journals Real power loss reduction by hyena optimizer algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 186
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Mathematical Modelling ◽  
Convergent Evolution ◽  
Power Loss ◽  
Social Relationship ◽  
Reactive Power ◽  
Scent Marking ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

<p><span>To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour &amp; Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal &amp; deeds are analogous to canines in several elements of convergent evolution. Hyenas catch the prey with their teeth rather than claws – possess hardened skin feet with large, blunt, no retractable claws are adapted for running and make sharp turns. However, the hyenas' grooming, scent marking, defecating habits, mating and parental behaviour are constant with the deeds of other feliforms. Mathematical modelling is formulated for the basic attributes of Hyena. Standard IEEE 14,300 bus test systems used to analyze the performance of Hyena Optimizer (HO) algorithm. Loss has been reduced with control variables are within the limits.</span></p>

scholarly journals ACTIVE POWER LOSS REDUCTION BY AMPLIFIED ANT COLONY ALGORITHM

2018 ◽  
Vol 6 (7) ◽  
pp. 132-141
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Ant Colony Algorithm ◽  
Reactive Power ◽  
Active Power ◽  
Ant Colony ◽  
Superior Performance ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Simulation Results ◽  
Power Problem

In this paper, Amplified Ant Colony (AAC) algorithm has been proposed for solving optimal reactive power problem. Mutation of Genetic algorithm (GA) is used in Ant Colony Algorithm (ACA) and the output of the GA is given as an input to the ACA. The proposed Amplified Ant Colony (AAC) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Amplified Ant Colony (AAC) algorithm in reducing the real power loss & voltage profiles are within the limits.

scholarly journals DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM

2017 ◽  
Vol 5 (10) ◽  
pp. 63-71
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Artificial Bee Colony ◽  
Reactive Power ◽  
Real Power ◽  
Abc Algorithm ◽  
The Real ◽  
Bee Colony ◽  
Search Speed ◽  
Convergence Performance ◽  
Power Problem

Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test systems and simulations results reveal about the better performance of the proposed Refined ABC algorithm (RABC) algorithm in reducing the real power loss and the voltage profiles within the limits.

scholarly journals Factual power loss lessening by synthetic supportive exploration algorithm

2021 ◽  
Vol 10 (2) ◽  
pp. 102
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Food Source ◽  
Reactive Power ◽  
Food Sources ◽  
Loss Reduction ◽  
Real Power ◽  
Power Loss Reduction ◽  
Power Problem

In this work an innovative synthetic supportive exploration (SSE) algorithm is utilized for solving optimal reactive power problem. Projected algorithm is based on communication between two simulated fabulous creatures as both of them intermingle and voyage to altered zones to find comprehensive minimum. In a definite zone according to the climate altering conditions amount of food can be found will be varied. Due to this reason, fabulous creatures develop seasonal exodus deeds to find out improved food sources. Earlier to exodus fabulous creatures will divide into subgroups in order to find an improved food source. Coordination of sub-groups will determine the performance of the search. Communication and exploration are the two key deeds of the fabulous creatures. Also, the two fabulous creatures make a decision on the marauder and prey by the sub fabulous creature. Proposed synthetic supportive exploration (SSE) algorithm has been tested in IEEE 14 and 300 bus systems. Real power loss power loss reduction achieved.

scholarly journals WIDE-RANGING VICINITY ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2017 ◽  
Vol 5 (10) ◽  
pp. 361-368
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Space ◽  
Global Search ◽  
The Other ◽  
Superior Performance ◽  
Real Power ◽  
Optimal Value ◽  
Simulation Results ◽  
Power Problem

In this paper, Wide-ranging vicinity Algorithm (WVA) is proposed to solve optimal reactive power problem. Wide-ranging vicinity Algorithm equally improves the local & global search. From the global search space a set of arbitrary solutions are primarily generated and then the most excellent solution will give the optimal value. After that, the algorithm will iterate, & there will be two sets of generated solutions in iteration’s, one from the global search space, the other from the set of solutions & it will be produced from the vicinity of the most excellent solution. The proposed Wide-ranging vicinity Algorithm (WVA) has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly the superior performance of the proposed Wide-ranging vicinity Algorithm (WVA) in reducing the real power loss & 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 ACTIVE POWER LOSS REDUCTION BY SYNTHESIZED ALGORITHM

2018 ◽  
Vol 6 (5) ◽  
pp. 149-156
Author(s):  
K. Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Harmony Search ◽  
Pso Algorithm ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
De Algorithm ◽  
Power Loss Reduction ◽  
Power Problem

In this paper, Synthesized Algorithm (SA) proposed to solve the optimal reactive power problem. Proposed Synthesized Algorithm (SA) is a combination of three well known evolutionary algorithms, namely Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, and Harmony Search (HS) algorithm. It merges the general operators of each algorithm recursively. This achieves both good exploration and exploitation in SA without altering their individual properties. In order to evaluate the performance of the proposed SA, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results show’s that Synthesized Algorithm (SA) successfully reduces the real power loss and voltage profiles are within the limits.

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