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A novel merchant optimization algorithm for solving optimal reactive power problem

Lenin Kanagasabai

Journal of Automation Mobile Robotics and Intelligent Systems

2021

Vol. 15, No. 1

51--56

In this paper Merchant Optimization Algorithm (MOA) is proposed to solve the optimal reactive power problem. Projected algorithm is modeled based on the behavior of merchants who gain in the market through various mode and operations. Grouping of the traders will be done based on their specific properties, and by number of candidate solution will be computed to individual merchant. First Group named as “Ruler candidate solution” afterwards its variable values are dispersed to the one more candidate solution and it named as “Serf candidate solution” In standard IEEE 14, 30, 57 bus test systems Merchant Optimization Algorithm (MOA) have been evaluated. Results show the proposed algorithm reduced power loss effectively.

Timber wolf optimization algorithm for real power loss diminution

Lenin Kanagasabai

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 1

24--28

In this paper Timber Wolf optimization (TWO) algorithm is proposed to solve optimal reactive power problem. Timber Wolf optimization (TWO) algorithm is modeled based on the social hierarchy and hunting habits of Timber wolf towards finding prey. Based on their fitness values social hierarchy has been replicated by classifying the population of exploration agents. Exploration procedure has been modeled by imitating the hunting actions of timber wolf by using searching, encircling, and attacking the prey. There are three fittest candidate solutions embedded as α, β and γ to lead the population toward capable regions of the exploration space in each iteration of Timber Wolf optimization. Proposed Timber Wolf optimization (TWO) algorithm has been tested in standard IEEE 14, 30 bus test systems and simulation results show the projected algorithm reduced the real power loss efficiently.

Active power loss reduction by novel feral cat swarm optimization algorithm

Lenin Kanagasabai

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 2

25--29

In this paper Feral Cat Swarm Optimization (FCS) Algorithm is proposed to solve optimal reactive power problem. Projected methodology has been modeled based on the activities of the feral cats. They have two main phases primarily “seeking mode”, “tracing mode”. In the proposed FCS algorithm, population of feral cats are created and arbitrarily scattered in the solution space, with every feral cat representing a solution. Produced population is alienated into two subgroups. One group will observe their surroundings which come under the seeking mode and another group moving towards the prey which will come under the tracing mode. New-fangled positions, fitness functions will be calculated subsequent to categorization of feral cats for seeking mode and tracing mode, through that cat with the most excellent solution will be accumulated in the memory. Feral Cat Swarm Optimization (FCS) Algorithm has been tested in standard IEEE 30 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.