scholarly journals POLAR PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2018 ◽  
Vol 6 (6) ◽  
pp. 335-345
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Swarm Optimization ◽  
Polar Function ◽  
Standard Particle Swarm Optimization ◽  
Power Problem

This paper presents Polar Particle Swarm optimization (PPSO) algorithm for solving optimal reactive power problem. The standard Particle Swarm Optimization (PSO) algorithm is an innovative evolutionary algorithm in which each particle studies its own previous best solution and the group’s previous best to optimize problems. In the proposed PPSO algorithm that enhances the behaviour of PSO and avoids the local minima problem by using a polar function to search for more points in the search space in order to evaluate the efficiency of proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms. Simulation results demonstrate good performance of the Polar Particle Swarm optimization (PPSO) algorithm in solving an optimal reactive power problem.

scholarly journals REDUCTION OF ACTIVE POWER LOSS BY VOLITION PARTICLE SWARM OPTIMIZATION

2018 ◽  
Vol 6 (6) ◽  
pp. 346-356
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Reactive Power ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test System ◽  
Swarm Optimization ◽  
Fish School ◽  
Exploration Exploitation ◽  
Power Problem

This paper projects Volition Particle Swarm Optimization (VP) algorithm for solving optimal reactive power problem. Particle Swarm Optimization algorithm (PSO) has been hybridized with the Fish School Search (FSS) algorithm to improve the capability of the algorithm. FSS presents an operator, called as collective volition operator, which is capable to auto-regulate the exploration-exploitation trade-off during the algorithm execution. Since the PSO algorithm converges faster than FSS but cannot auto-adapt the granularity of the search, we believe the FSS volition operator can be applied to the PSO in order to mitigate this PSO weakness and improve the performance of the PSO for dynamic optimization problems. In order to evaluate the efficiency of the proposed Volition Particle Swarm Optimization (VP) algorithm, it has been tested in standard IEEE 30 bus test system and compared to other reported standard algorithms.  Simulation results show that Volition Particle Swarm Optimization (VP) algorithm is more efficient then other algorithms in reducing the real power losses with control variables are within the limits.

scholarly journals TAILORED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

2020 ◽  
Vol 5 (12) ◽  
pp. 246-255
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Neighbourhood Search ◽  
A Cell ◽  
Power Problem

This paper presents Tailored Particle Swarm Optimization (TPSO) algorithm for solving optimal reactive power problem. Particle Swarm optimization algorithm based on Membrane Computing is proposed to solve the problem. Tailored Particle Swarm Optimization (TPSO) algorithm designed with the framework and rules of a cell-like P systems, and particle swarm optimization with the neighbourhood search.  In order to evaluate the efficiency of the proposed algorithm, it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to other specified algorithms. Simulation results show that Tailored Particle Swarm Optimization (TPSO) algorithm is superior to other algorithms in reducing the real power loss.

scholarly journals Progressive Particle Swarm Optimization Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 1 (3) ◽  
pp. 125
Author(s):  
Kanagasabai Lenin ◽  
Bhumanapally Ravindhranath Reddy ◽  
Munagala Surya Kalavathi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Position Vector ◽  
Swarm Optimization ◽  
Power Problem

In this paper a Progressive particle swarm optimization algorithm (PPS) is used to solve optimal reactive power problem. A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are modernized from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance conception. This new-fangled perception helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation consequently. By this we plan to perk up the performance in terms of the optimal solution within a rational number of generations. The projected PPS has been tested on standard IEEE 30 bus test system and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss with control variables are within the limits.

An Improved Particle Swarm Optimization Algorithm

2012 ◽  
Vol 195-196 ◽  
pp. 1060-1065
Author(s):  
Chang Yuan Jiang ◽  
Shu Guang Zhao ◽  
Li Zheng Guo ◽  
Chuan Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Convergence Condition ◽  
Benchmark Function ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization

Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

scholarly journals DRAG & AVERSION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS

2017 ◽  
Vol 5 (11) ◽  
pp. 168-176
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Power Loss ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Modified Model ◽  
Real Power ◽  
Basic Particle ◽  
Power Problem

This paper projects Drag & Aversion Particle Swarm Optimization (DAPSO) algorithm is applied to solve optimal reactive power problem. In DAPSO the idea of decreasing and increasing diversity operators used to control the population into the basic Particle Swarm Optimization (PSO) model. The modified model uses a diversity measure to have the algorithm alternate between exploring and exploiting behavior. The results show that both Drag & Aversion Particle Swarm Optimization (DAPSO) prevents premature convergence to enhanced level but still keeps a rapid convergence. Proposed Drag & Aversion Particle Swarm Optimization (DAPSO) has been tested in standard IEEE 118 & practical 191 bus test systems. Real power loss has been considerably reduced and voltage profiles are within the limits.

scholarly journals ACTUAL POWER LOSS REDUCTION BY AUGMENTED PARTICLE SWARM OPTIMIZATION ALGORITHM

2018 ◽  
Vol 6 (9) ◽  
pp. 212-219
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Power Loss Reduction ◽  
Power Problem

This paper presents an advanced particle swarm optimization Algorithm for solving the reactive power problem in power system. Bacterial Foraging Optimization Algorithm (BFOA) has recently emerged as a very powerful technique for real parameter optimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposed a new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO) called advanced bacterial foraging-oriented particle swarm optimization (ABFPSO) algorithm for solving reactive power problem. The simulation results demonstrate good performance of the ABFPSO in solving an optimal reactive power problem. In order to evaluate the proposed algorithm, it has been tested on IEEE 57 bus system and compared to other algorithms.

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.

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

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