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.

scholarly journals REDUCTION OF REAL POWER LOSS BY ADVANCED PARTICLE SWARM OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (2) ◽  
pp. 166-181
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Test System ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Real Power

This paper presents Advanced Particle Swarm Optimization (APSO) algorithm for solving optimal reactive power problem. In this work Biological Particle swarm Optimization algorithm utilized to solve the problem by eliminating inferior population & keeping superior population, to make full use of population resources and speed up the algorithm convergence. Projected Advanced Particle Swarm Optimization (APSO) algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the superior performance of the proposed Advanced Particle Swarm Optimization (APSO) algorithm in reducing the real power loss and static voltage stability margin (SVSM) Index has been enhanced.

scholarly journals AMENDED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REAL POWER LOSS REDUCTION AND STATIC VOLTAGE STABILITY MARGIN INDEX ENHANCEMENT

2018 ◽  
Vol 6 (2) ◽  
pp. 146-156
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Power Loss Reduction ◽  
Reactive Power Dispatch

In this paper, Amended Particle Swarm Optimization Algorithm (APSOA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) for solving the optimal reactive power dispatch Problem. PSO is one of the most widely used evolutionary algorithms in hybrid methods due to its simplicity, convergence speed, an ability of searching Global optimum. GSA has many advantages such as, adaptive learning rate, memory-less algorithm and, good and fast convergence. Proposed hybridized algorithm is aimed at reduce the probability of trapping in local optimum. In order to assess the efficiency of proposed algorithm, it has been tested on Standard IEEE 30 system and compared to other standard algorithms. The simulation results demonstrate worthy performance of the Amended Particle Swarm Optimization Algorithm (APSOA) in solving optimal reactive power dispatch problem.

scholarly journals HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2018 ◽  
Vol 6 (12) ◽  
pp. 121-127
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Power Loss ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Real Power

In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in reducing the real power loss.

Sign in / Sign up

Export Citation Format

Share Document