Multiple Contingency Analysis for optimal placement and estimate the value of SVC for power loss reduction employing Particle Swarm Optimization

2019 ◽  
Author(s):  
Mohammed Shuaib Iqbal ◽  
B.C. Sujatha
Keyword(s):  
Particle Swarm Optimization ◽  
Power Loss ◽  
Particle Swarm ◽  
Optimal Placement ◽  
Loss Reduction ◽  
Contingency Analysis ◽  
Swarm Optimization ◽  
Power Loss Reduction

scholarly journals Factual power loss reduction by dynamic membrane evolutionary algorithm

2021 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Lenin Kanagabasai
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Dynamic Membrane ◽  
Active Membranes ◽  
Power Loss Reduction ◽  
Gaussian Probability Distribution

<p class="papertitle">This  paper  presents Dynamic  Membrane  Evolutionary  Algorithm  (DMEA) has   been   applied   to   solve   optimal   reactive   power   problem.Proposed methodology  merges  the  fusion  and  division  rules  of  P  systems  with  active membranes  and  with  adaptive  differential  evolution  (ADE),  particle  swarm optimization  (PSO)  exploration  stratagem.  All  elementary  membranes  are amalgamated  into  one  membrane  in  the  computing  procedure.  Furthermore, integrated  membrane are alienated into the elementary  membranes 1, 2,_ m. In particle  swarm  optimization  (PSO) 𝑪<sub>𝟏</sub>, 𝑪<sub>𝟐</sub> (acceleration  constants) are vital parameters to augment the explorationability  of  PSO in the  period  ofthe optimization procedure.In this work, Gaussian probability distribution isinitiated to engenderthe accelerating coefficients of PSO.Proposed Dynamic Membrane  Evolutionary  Algorithm  (DMEA) has  been  tested  in  standard IEEE  14,  30,  57, 118, 300  bus  test  systems  and  simulation  results  show  the projected algorithm reduced the real power loss comprehensively.</p>

scholarly journals ACTIVE POWER LOSS REDUCTION BY BETTER-QUALITY PARTICLE SWARM OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (1) ◽  
pp. 329-337
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Power Loss ◽  
Reactive Power ◽  
Particle Swarm ◽  
Selection Strategy ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Evolutionary Selection ◽  
Power Loss Reduction ◽  
Cauchy Mutation

In this paper Better-Quality Particle Swarm Optimization (BPSO) algorithm is proposed to solve the optimal reactive power Problem. Proposed algorithm is obtained by combining particle swarm optimization (PSO), Cauchy mutation and an evolutionary selection strategy. The idea is to introduce the Cauchy mutation into PSO in the hope of preventing PSO from trapping into a local optimum through long jumps made by the Cauchy mutation. In order to evaluate the efficiency of the proposed Better-Quality Particle Swarm Optimization (BPSO) algorithm, it has been tested on IEEE 57 bus system. Simulation Results show’s that BPSO is more efficient than other reported algorithms in reducing the real power loss.

scholarly journals An Improved Particle Swarm Optimization Algorithm Using Eagle Strategy for Power Loss Minimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power System ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Eagle Strategy

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.

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