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 Particle Swarm Optimization Algorithm Based Reactive Power Optimization in Distribution Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3792-3796
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Fitness Function ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Loss Index

Minimization of power loss is the first priority of the power companies. Generally power loss is directly proportional to the reactive power demand and minimization of this is known as reactive power optimization (RPO). In this paper we are trying to minimize the reactive power loss with help of distributed generation. Distributed generation provides active as well as reactive power locally so, there is no need of taking the reactive power from the generator consequently reactive power loss minimizes. Now problem arises that where to place the distributed generation to have minimum power loss. To find the optimal location of the distributed generation, we have used particle swarm optimization algorithm (PSO). For that we have defined the fitness function as well as constraints. Constraints limits the value of variable within the defined range. Fitness function is sum of real power loss index, reactive power loss index and voltage deviation index. We have also used genetic algorithm just to compare the results and to find which one is better out of genetic algorithm and PSO. RPO increases the power transfer capability, reduces the line loss and boost the system stability therefore it can be applied in the distribution network.

A Novel Hybrid Optimization Algorithm Based on Multi-Agent and Particle Swarm

2011 ◽  
Vol 460-461 ◽  
pp. 512-517
Author(s):  
De Jia Shi ◽  
Wei Jin Jiang ◽  
Xiao Ling Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Agent Interactions ◽  
Fitness Value ◽  
Multi Agent

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAI.SO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of I.SO. MAPSO realizes the purpose of' minimizing the value of' objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

scholarly journals A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow

2019 ◽  
Vol 11 (14) ◽  
pp. 3862 ◽  
Author(s):  
Imene Cherki ◽  
Abdelkader Chaker ◽  
Zohra Djidar ◽  
Naima Khalfallah ◽  
Fadela Benzergua
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Particle Swarm ◽  
Power Losses ◽  
Swarm Optimization ◽  
Transformation Ratio ◽  
Reactive Power Flow ◽  
Optimal Reactive Power Flow

In this paper, the problem of the Optimal Reactive Power Flow (ORPF) in the Algerian Western Network with 102 nodes is solved by the sequential hybridization of metaheuristics methods, which consists of the combination of both the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). The aim of this optimization appears in the minimization of the power losses while keeping the voltage, the generated power, and the transformation ratio of the transformers within their real limits. The results obtained from this method are compared to those obtained from the two methods on populations used separately. It seems that the hybridization method gives good minimizations of the power losses in comparison to those obtained from GA and PSO, individually, considered. However, the hybrid method seems to be faster than the PSO but slower than GA.

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 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.

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