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Lenin Kanagasabai
Prasad V. Potluri Siddhartha Institute of Technology
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Chaotic based Pteropus algorithm for solving optimal reactive power problem Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (212.242 KB) | DOI: 10.11591/ijaas.v9.i4.pp265-269

Abstract

In this work, a Chaotic based Pteropus algorithm (CPA) has been proposed for solving optimal reactive power problem. Pteropus algorithm imitates deeds of the Pteropus. Normally Pteropus while flying it avoid obstacles by using sonar echoes, particularly utilize time delay. To the original Pteropus algorithm chaotic disturbance has been applied and the optimal capability of the algorithm has been improved in search of global solution. In order to augment the population diversity and prevent early convergence, adaptively chaotic disturbance is added at the time of stagnation. Furthermore exploration and exploitation capability of the proposed algorithm has been improved. Proposed CPA technique has been tested in standard IEEE 14,300 bus systems & real power loss has been considerably reduced.
Real power loss reduction by arctic char algorithm Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 9, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (191.423 KB) | DOI: 10.11591/ijaas.v9.i4.pp261-264

Abstract

This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Deeds of Arctic char have been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively.
Two bio-inspired algorithms for solving optimal reactive power problem Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.573 KB) | DOI: 10.11591/ijaas.v9.i3.pp180-185

Abstract

In this work two ground-breaking algorithms called; Sperm Motility (SM) algorithm & Wolf Optimization (WO) algorithm is used for solving reactive power problem. In sperm motility approach spontaneous movement of the sperm is imitated & species chemo attractant, sperms are enthralled in the direction of the ovum. In wolf optimization algorithm the deeds of wolf is imitated in the formulation & it has a flag vector also length is equivalent to the whole sum of numbers in the dataset the optimization. Both the projected algorithms have been tested in standard IEEE 57,118, 300 bus test systems. Simulated outcomes reveal about the reduction of real power loss & with variables are in the standard limits. Almost both algorithms solved the problem efficiently, yet wolf optimization has slight edge over the sperm motility algorithm in reducing the real power loss.
Passerine swarm optimization algorithm for solving optimal reactive power dispatch problem Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 9, No 2: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (774.656 KB) | DOI: 10.11591/ijaas.v9.i2.pp101-109

Abstract

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
Opposition based red wolf algorithm for solving optimal reactive power problem Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (312.766 KB) | DOI: 10.11591/ijaas.v10.i3.pp193-197

Abstract

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.
True power loss reduction by mountain zebra, augmented bat, and improved kidney search algorithms Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.364 KB) | DOI: 10.11591/ijaas.v10.i3.pp205-211

Abstract

In this paper optimal reactive power problem is solved by mountain zebra algorithm (MZA), augmented bat algorithm (AB), and improved kidney search (IKS) algorithm. In the proposed algorithm, an intermediate state has been established at first, and then explores the intermediate state in order to obtain the global optima. Iterative local search implemented in this proposed algorithm. This technique enhances the search procedure in rapid mode. Then in this work, IKS algorithm has been proposed for solving optimal reactive power problem. In initial phase, a random population of probable solutions is created and re-absorption, secretion, excretion are imitated in the search process to check various conditions entrenched to the algorithm. The algorithm has been built to advance the search even a potential solution moved to waste (W) and it will be brought back to the filtered blood (FB). Glomerular filtration rate (GFR) test is utilized to verify the fitness of kidneys. Better efficiency of the proposed MZA, AB, and IKS algorithm confirmed by successful evaluation in standard IEEE 14-bus, 118-bus, and 300-bus test systems. The results show that active power loss has been reduced.
Real power loss diminution by rain drop optimization algorithm Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (355.936 KB) | DOI: 10.11591/ijaas.v10.i2.pp149-155

Abstract

In this work, the rain drop optimization (RDO) algorithm is projected to reduce power loss. Proceedings of rain drop have been imitated to model the RDO algorithm. The natural action of rain drop is flowing downwards from the peak and it may form small streams during the headway from the mountain or hill. As by gravitation principal rain drop flow as a stream as a river from the peak of mountains or hill then it reaches the sea as global optimum. Proposed rain drop optimization (RDO) algorithm evaluated in IEEE 30, bus test system. Power loss reduction, voltage deviation minimization, and voltage stability improvement have been achieved.
Real power loss reduction by hyena optimizer algorithm Lenin Kanagasabai
International Journal of Advances in Applied Sciences Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (507.076 KB) | DOI: 10.11591/ijaas.v9.i3.pp186-191

Abstract

To solve optimal reactive power problem this paper projects Hyena Optimizer (HO) algorithm and it inspired from the behaviour of Hyena. Collaborative behaviour & Social relationship between Hyenas is the key conception in this algorithm. Hyenas a form of carnivoran mammal & 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.
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