Real Power Loss Reduction by Cinnamon ibon Search Optimization Algorithm

2021 ◽  
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Search Algorithm ◽  
Loss Reduction ◽  
Real Power ◽  
Search Optimization ◽  
Power Loss Reduction ◽  
Voltage Stability Enhancement ◽  
Voltage Deviation ◽  
Stability Enhancement

In this paper Cinnamon ibon Search Optimization Algorithm (CSOA) is used for solving the power loss lessening problem. Key objectives of the paper are Real power Loss reduction, Voltage stability enhancement and minimization of Voltage deviation. Searching and scavenging behavior of Cinnamon ibon has been imitated to model the algorithm. Cinnamon ibon birds which are in supremacy of the group are trustworthy to be hunted by predators and dependably attempt to achieve a improved position and the Cinnamon ibon ones that are positioned in the inner of the population, drive adjacent to the nearer populations to dodge the threat of being confronted. The systematic model of the Cinnamon ibon search Algorithm originates with an arbitrary individual of Cinnamon ibon. The Cinnamon ibon search algorithm entities show the position of the Cinnamon ibon. Besides, the Cinnamon ibon bird is supple in using the cooperating plans and it alternates between the fabricator and the cadger. Successively the Cinnamon ibon identifies the predator position; then they charm the others by tweeting signs. The cadgers would be focussed to the imperilled regions by fabricators once the fear cost is more than the defence threshold. Likewise, the subterfuge of both the cadger and the fabricator is commonly used by Cinnamon ibon. The dispersion of the Cinnamon ibon location in the solution area is capricious. An impulsive drive approach was applied when dispossession of any adjacent Cinnamon ibon in the purlieu of the present population. This style diminishes the convergence tendency and decreases the convergence inexorableness grounded on the controlled sum of iterations. Authenticity of the Cinnamon ibon Search Optimization Algorithm (CSOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Proportion of actual power loss lessening is amplified.

scholarly journals Self-Adaptive Firefly-Algorithm-Based Unified Power Flow Controller Placement with Single Objectives

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Selvarasu Ranganathan ◽  
S. Rajkumar
Keyword(s):  
Power Loss ◽  
Power Flow ◽  
Unified Power Flow Controller ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
Voltage Stability Enhancement ◽  
Stability Enhancement ◽  
Power Flow Controller ◽  
Voltage Profile Improvement

The selection of positions for unified power flow controller (UPFC) placement in transmission network is an essential factor, which aids in operating the system in a more reliable and secured manner. This paper focuses on strengthening the power system performance through UPFC placement employing self-adaptive firefly algorithm (SAFA), which selects the best positions along with parameters for UPFC placement. Three single objectives of real power loss reduction, voltage profile improvement, and voltage stability enhancement are considered in this work. IEEE 14, 30, and 57 test systems are selected to accomplish the simulations and to reveal the efficacy of the proposed SAFA approach; besides, solutions are compared with two other algorithms solutions of honey bee algorithm (HBA) and bacterial foraging algorithm (BFA). The proposed SAFA contributes real power loss reduction, voltage profile improvement, and voltage stability enhancement by optimally choosing the placement for UPFC.

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 Heat Transfer and Simulated Coronary Circulation System Optimization Algorithms for Real Power Loss Reduction

2021 ◽  
Vol 8 (1) ◽  
pp. E1-E8
Author(s):  
L. Kanagasabai
Keyword(s):  
Heat Transfer ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Coronary Circulation ◽  
Circulation System ◽  
Loss Reduction ◽  
Real Power ◽  
Development Factor ◽  
Power Loss Reduction

In this paper, the heat transfer optimization (HTO) algorithm and simulated coronary circulation system (SCCS) optimization algorithm has been designed for Real power loss reduction. In the projected HTO algorithm, every agent is measured as a cooling entity and surrounded by another agent, like where heat transfer will occur. Newton’s law of cooling temperature will be updated in the proposed HTO algorithm. Each value of the object is computed through the objective function. Then the objects are arranged in increasing order concerning the objective function value. This projected algorithm time “t” is linked with iteration number, and the value of “t” for every agent is computed. Then SCCS optimization algorithm is projected to solve the optimal reactive power dispatch problem. Actions of human heart veins or coronary artery development have been imitated to design the algorithm. In the projected algorithm candidate solution is made by considering the capillaries. Then the coronary development factor (CDF) will appraise the solution, and population space has been initiated arbitrarily. Then in the whole population, the most excellent solution will be taken as stem, and it will be the minimum value of the Coronary development factor. Then the stem crown production is called the divergence phase, and the other capillaries’ growth is known as the clip phase. Based on the arteries leader’s coronary development factor (CDF), the most excellent capillary leader’s (BCL) growth will be there. With and without L-index (voltage stability), HTO and SCCS algorithm’s validity are verified in IEEE 30 bus system. Power loss minimized, voltage deviation also reduced, and voltage stability index augmented.

True Power Loss Reduction by Enhanced Tree Squirrel Search, Enhanced Salp Swarm, and Swim Bladder Operation-Based Shark Optimization Algorithms

2022 ◽  
pp. 65-90
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Population Diversity ◽  
Swim Bladder ◽  
Loss Reduction ◽  
Salp Swarm Algorithm ◽  
Search Optimization ◽  
Power Loss Reduction ◽  
Tree Squirrel ◽  
Swarm Algorithm

In this chapter, enhanced tree squirrel search optimization algorithm (ETSS), enhanced salp swarm algorithm (ESS), and swim bladder operation-based shark algorithm (SBS) have been applied to solve the power loss reduction problem. Enhanced tree squirrel search optimization algorithm (ETSS) utilizes the jumping exploration method and progressive exploration technique—both possess winter search strategy—in order to preserve the population diversity and to perk up the convergence speed. A new-fangled winter exploration strategy is implemented in the jumping exploration technique. In enhanced salp swarm algorithm (ESS) an inertia weight ω∈ [0, 1] is applied, which picks up the pace of convergence during the period of exploration. Then swim bladder operation-based shark algorithm (SBS) is proposed to solve the problem. Based on contracting and expanding actions of the swim bladder in shark, SBS algorithm has been modelled.

Sign in / Sign up

Export Citation Format

Share Document