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.

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.

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 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 chaotic based predator-prey brain storm optimization algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 218
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Local Optimum ◽  
Loss Reduction ◽  
Optimal Parameters ◽  
Predator Prey ◽  
Brain Storm Optimization ◽  
Power Loss Reduction ◽  
Power Problem

<div data-canvas-width="34.43688268494255">In this paper chaotic predator-prey brain storm optimization (CPB) algorithm is proposed to solve optimal reactive power problem. In this work predator-prey brain storm optimization position cluster centers to perform as predators, consequently it will move towards better and better positions, while the remaining ideas perform as preys; hence get away from their adjacent predators. In the projected CPB algorithm chaotic theory has been applied in the modeling of the algorithm. In the proposed algorithm main properties of chaotic such as ergodicity and irregularity used to make the algorithm to jump out of the local optimum as well as to determine optimal parameters CPB algorithm has been tested in standard IEEE 57 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.</div>

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 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 Power loss reduction by arctic wolf optimization algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 111
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
The Arctic ◽  
Gray Wolf ◽  
Loss Reduction ◽  
Power Loss Reduction ◽  
Average Size ◽  
Power Problem ◽  
North Western

This paper proposes Arctic Wolf optimization (AWO) algorithm to solve the optimal reactive power problem. Deeds of the Arctic wolf have been imitated to formulate the proposed algorithm. Arctic wolf also identified as the white wolf or polar wolf is a breed of gray wolf inhabitant from Melville Island to Ellesmere Island. It is average size, very smaller when compared to north western wolf, it possess whiter coloration, narrower braincase and big carnassials. Particle swarm optimization, Genetic algorithm has been used to improve the Exploration &amp; Exploitation ability of the algorithm by utilizes flag vector &amp; position, velocity update properties. Proposed Arctic Wolf optimization (AWO) algorithm has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithms reduced the real power loss considerably.

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 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 ENHANCED ACCLIMATIZED BACTERIAL EXPLORATION ALGORITHM

2018 ◽  
Vol 6 (3) ◽  
pp. 182-190
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Loss Reduction ◽  
Swarm Optimization ◽  
E Coli ◽  
Search Optimization ◽  
Power Loss Reduction ◽  
Speed Up ◽  
Power Problem

This paper presents Enhanced Acclimatized Bacterial Exploration (EBE) algorithm to solve reactive power problem. Bacterial Search Optimization Algorithm has recently emerged as a very powerful technique based on the behaviour of E-coli bacteria. In order to speed up the convergence of Bacterial search Optimization Algorithm, this paper proposed a new hybridization between Bacterial Search Optimization Algorithm (BSO) and Particle Swarm Optimization (PSO). In order to evaluate the proposed Enhanced Acclimatized Bacterial Exploration (EBE) algorithm, it has been tested in standard IEEE 118 & practical 191 bus test systems and compared to other standard algorithms.

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