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 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 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.

A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

2021 ◽  
pp. 014233122110295
Author(s):  
Jaouher Chrouta ◽  
Fethi Farhani ◽  
Abderrahmen Zaafouri
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Selection Strategy ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Suggested Approach ◽  
Inertia Weight

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

Optimization of Reactive Power Based on Dynamic Learning Factor Multi-Objective Particle Swarm Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3984-3987
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Yao Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Local Optimum ◽  
Dynamic Learning ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Bus System

. Particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particle swarm optimization algorithm based on dynamic learning factors. The two accelerations are changed with searching stage, so as to enhance the early globle search ability and the late local search ability, then to avoid local optimum; minimum particle angle method and crowded distance method are uesd to determine the global extremum in instalments, so as to improve the convergence speed and accuracy of multi-objective pareto solutions. Take the IEEE 30 bus system IEEE 118 bus system as example, the proposed method is compared with adaptive chaos particle swarm optimization (ACPSO) and NSGA-II, simulation results show that the method put forward in this paper has better convergence accuracy.

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 Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

2021 ◽  
pp. 31-42
Author(s):  
Christophe Bananeza ◽  
Sylvère Mugemanyi ◽  
Théogène Nshimyumukiza ◽  
Jean Marie Vianney Niyodusenga ◽  
Jean De Dieu Munyaneza
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Active Power ◽  
Local Optimum ◽  
Reactive Power Optimization ◽  
Power Losses ◽  
Swarm Optimization ◽  
Active Power Losses

The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor.  The standard PSO has a better exploitation ability but it has a very poor exploration  ability. Consequently, to maintain the balance between these two abilities during the  search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

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