Real Power Loss Reduction by Enhanced Russian Haliaeetus Pelagicus Optimization

In this paper Enhanced Russian Haliaeetus pelagicus Optimization Algorithm is applied for solving the Power loss lessening problem. Russian Haliaeetus pelagicus Optimization Algorithm is modeled based on the natural deeds of Russian Haliaeetus pelagicus. A spiral trajectory for exploration and a straight-line lane for assails done by Russian Haliaeetus pelagicus for hunting. It shows proclivity to sail in preliminary phase of hunting and efficiently changeover to further proclivity to assail in the concluding phases. Russian Haliaeetus pelagicus conserve proclivity for both sail and assail in each instant of the voyage. Sail vector is computed based on the assail vector. Sail vector is a tangent to the loop and vertical to the assail vector. The sail can be linear pace of Russian Haliaeetus pelagicus in comparison the prey. The sail vector in n-dimensions is situated within the tangent plane in loop in order compute the sail vector. In Enhanced Russian Haliaeetus pelagicus Optimization Algorithm exterior archive, prey precedence condition, and picking of prey are added through multi-objective mode. The fundamental plan is to keep capable solutions in an exterior archive and modernize when procedure continues. Exploration agents are moved in the direction of the stored entities. If the new-fangled solution is conquered by one or more of the present archives' entities, then the new-fangled solution is removed. If the new-fangled solution is not ruled over the present entities of the stored one and the records are not occupied, basically append the new-fangled position to the store. Prudence of the Enhanced Russian Haliaeetus pelagicus Optimization Algorithm is corroborated in IEEE 30 bus system (with and devoid of L-index). True power loss lessening is reached. Ratio of true power loss lessening is augmented

[1]  M. Keerio,et al.  Multi-Objective Optimal Reactive Power Dispatch Considering Probabilistic Load Demand Along with Wind and Solar Power Integration , 2020, 2020 2nd International Conference on Smart Power & Internet Energy Systems (SPIES).

[2]  T. K. Sunil Kumar,et al.  An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization , 2020, Energies.

[3]  Minh Quan Duong,et al.  Optimal Reactive Power Flow for Large-Scale Power Systems Using an Effective Metaheuristic Algorithm , 2020, J. Electr. Comput. Eng..

[4]  Seyedali Mirjalili,et al.  Equilibrium optimizer: A novel optimization algorithm , 2020, Knowl. Based Syst..

[5]  K. Mandal,et al.  Optimal Reactive Power Dispatch for Voltage Security using JAYA Algorithm , 2020, 2020 International Conference on Convergence to Digital World - Quo Vadis (ICCDW).

[6]  S. Pasandideh,et al.  Sine–cosine crow search algorithm: theory and applications , 2019, Neural Computing and Applications.

[7]  Thang Trung Nguyen,et al.  Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm , 2019, TELKOMNIKA (Telecommunication Computing Electronics and Control).

[8]  Lingling Chen,et al.  Multi-population coevolutionary dynamic multi-objective particle swarm optimization algorithm for power control based on improved crowding distance archive management in CRNs , 2019, Comput. Commun..

[9]  A. N. Hussain,et al.  Modified Particle Swarm Optimization for Solution of Reactive Power Dispatch , 2018 .

[10]  A. Rezaee Jordehi,et al.  Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems , 2017, Appl. Soft Comput..

[11]  Tarek Bouktir,et al.  Ant lion optimizer for solving optimal reactive power dispatch problem in power systems , 2017 .

[12]  Harish Pulluri,et al.  An enhanced self-adaptive differential evolution based solution methodology for multiobjective optimal power flow , 2017, Appl. Soft Comput..

[13]  Amir Hossein Gandomi,et al.  Chaotic gravitational constants for the gravitational search algorithm , 2017, Appl. Soft Comput..

[14]  Xin-She Yang,et al.  New directional bat algorithm for continuous optimization problems , 2017, Expert Syst. Appl..

[15]  Kadir Abaci,et al.  Optimal reactive-power dispatch using differential search algorithm , 2017 .

[16]  Florin Capitanescu,et al.  An archive-based multi-objective evolutionary algorithm with adaptive search space partitioning to deal with expensive optimization problems: Application to process eco-design , 2016, Comput. Chem. Eng..

[17]  Weerakorn Ongsakul,et al.  Optimal Reactive Power Dispatch Using Improved Pseudo-gradient Search Particle Swarm Optimization , 2016 .

[18]  Stephen P. Boyd,et al.  A simple effective heuristic for embedded mixed-integer quadratic programming , 2015, 2016 American Control Conference (ACC).

[19]  Ranjit Roy,et al.  Particle Swarm Optimization Based Optimal Reactive Power Dispatch , 2015, 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).

[20]  Abdelghani Bekrar,et al.  Hybrid PSO-tabu search for the optimal reactive power dispatch problem , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[21]  Provas Kumar Roy,et al.  Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization , 2013 .

[22]  M. Kalantar,et al.  Optimal reactive power dispatch based on harmony search algorithm , 2011 .

[23]  Chaohua Dai,et al.  Seeker Optimization Algorithm for Optimal Reactive Power Dispatch , 2009, IEEE Transactions on Power Systems.

[24]  P. Subbaraj,et al.  Optimal reactive power dispatch using self-adaptive real coded genetic algorithm , 2009 .

[25]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[26]  Zhaoyang Qu,et al.  Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm , 2020, IEEE Access.