Solving Optimal Reactive Power Dispatch Problem by Population Distinction and Pandemic Virus Algorithms

2021 ◽  
pp. 33-48
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Vital Role ◽  
Loss Reduction ◽  
Optimal Reactive Power Dispatch ◽  
Pandemic Virus ◽  
Power Loss Reduction ◽  
Reactive Power Dispatch ◽  
Augmentation Procedure

In this paper Noble, Depraved and Abhorrent (NDA) optimization algorithm and United Kingdom B117 Pandemic Virus algorithm (UPA) are applied for solving the power loss lessening problem. Power loss reduction has been done by with and without considering the voltage stability. In both cases power loss reduction has been achieved effectively. In NDA approach population passages in the direction of the noble member and evades the depraved member. Then abhorrent member plays a vital role in modernizing the population. In a perplexing change, the abhorrent member guides the population in circumstances opposite to people crusade. Position of the members in population is modernized in three subsequent segments. In the preliminary segment, population transfers in the direction of the noble member. Then UPA method is based on the idea of hoi polloi protection as a stratagem to battle the B117 COVID-19 coronavirus pandemic. Spreading of B117 COVID-19 variant is more influenced by the infested persons unswervingly come across other public associates. Communal separation is endorsed by health specialists to protect other populaces from the B117 COVID-19 variant infection. Hoi polloi protection progression is the Preliminary augmentation procedure. Rendering to the Fundamental Facsimile rate, the genetic factor unchanged or prejudiced by communal separation. Authenticity of the NDA optimization algorithm and UPA algorithm is substantiated in IEEE 30 bus system (with and without L-index). Factual power loss lessening is reached. Proportion of actual power loss lessening is augmented

scholarly journals Real Power Loss Reduction by Rock Dove Optimization and Fuligo Septica Optimization Algorithms

2020 ◽  
Vol 7 (2) ◽  
pp. E1-E6
Author(s):  
L. Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Power Transmission ◽  
Transmission Loss ◽  
Loss Reduction ◽  
Exploration And Exploitation ◽  
Real Power ◽  
Fitness Value ◽  
Power Loss Reduction

This paper aims to use the Rock Dove (RD) optimization algorithm and the Fuligo Septica optimization (FSO) algorithm for power loss reduction. Rock Dove towards a particular place is based on the familiar (sight) objects on the traveling directions. In the formulation of the RD algorithm, atlas and range operator, and familiar sight operators have been defined and modeled. Every generation number of Rock Dove is reduced to half in the familiar sight operator and Rock Dove segment, which hold the low fitness value that occupying the lower half of the generation will be discarded. Because it is implicit that the individual’s Rock Dove is unknown with familiar sights and very far from the destination place, a few Rock Doves will be at the center of the iteration. Each Rock Dove can fly towards the final target place. Then in this work, the FSO algorithm is designed for real power loss reduction. The natural vacillation mode of Fuligo Septica has been imitated to develop the algorithm. Fuligo Septica connects the food through swinging action and possesses exploration and exploitation capabilities. Fuligo Septica naturally lives in chilly and moist conditions. Mainly the organic matter in the Fuligo Septica will search for the food and enzymes formed will digest the food. In the movement of Fuligo Septica it will spread like a venous network, and cytoplasm will flow inside the Fuligo Septica in all ends. THE proposed RD optimization algorithm and FSO algorithm have been tested in IEEE 14, 30, 57, 118, and 300 bus test systems and simulation results show the projected RD and FSO algorithm reduced the real power loss. Keywords: optimal reactive power, transmission loss, Rock Dove, Fuligo Septica.

scholarly journals AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT

2018 ◽  
Vol 6 (2) ◽  
pp. 146-156
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Power Loss Reduction ◽  
Reactive Power Dispatch

In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum. In order to assess the efficiency of proposed algorithm, it has been tested on Standard IEEE 30 system and compared to other standard algorithms. The simulation results demonstrate worthy performance of the Amended Particle Swarm Optimization Algorithm (APSOA) in solving optimal reactive power dispatch problem.

scholarly journals Passerine swarm optimization algorithm for solving optimal reactive power dispatch problem

2020 ◽  
Vol 9 (2) ◽  
pp. 101
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Passerine Bird ◽  
Swarm Optimization ◽  
Real Power ◽  
The Real ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

<span>This paper presents Passerine Swarm Optimization Algorithm (PSOA) for solving optimal reactive power dispatch problem. This algorithm is based on behaviour of social communications of Passerine bird. Basically, Passerine bird has three common behaviours: search behaviour, adherence behaviour and expedition behaviour. Through the shared communications Passerine bird will search for the food and also run away from hunters. By using the Passerine bird communications and behaviour, five basic rules have been created in the PSOA approach to solve the optimal reactive power dispatch problem. Key aspect is to reduce the real power loss and also to keep the variables within the limits. Proposed Passerine Swarm Optimization Algorithm (PSOA) has been tested in standard IEEE 30 bus test system and simulations results reveal about the better performance of the proposed algorithm in reducing the real power loss and enhancing the static voltage stability margin</span>

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.

scholarly journals TRUE POWER LOSS REDUCTION BY WOLF OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (11) ◽  
pp. 323-329
Author(s):  
K. Lenin
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Loss Reduction ◽  
Power Losses ◽  
Swarm Optimization ◽  
Power Loss Reduction ◽  
Solution Efficiency ◽  
Power Problem

In this paper wolf optimization algorithm (WOA) has been applied for solving reactive power problem. In order to enhance the search procedure the basic qualities of particle swarm optimization has been intermingled to improve the capability of the search to reach a global solution. Efficiency of the projected wolf optimization algorithm (WOA) is tested in standard IEEE 30 bus test system. Simulation study indicates wolf optimization algorithm (WOA) performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

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 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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