Optimal Placement and Sizing of Distributed Generation in Distribution System Using Modified Particle Swarm Optimization Algorithm

2018 ◽  
pp. 485-507 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Distribution System ◽  
Reactive Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

The electricity demand, fossil fuel depletion and environment issues increase the interest of power engineers to integrate small power generations i.e. called distributed generation (DGs) in the distribution system. The DG in distribution system has many positive effects such as it reduces the system power losses, improves the voltage profile and strengthen the voltage stability etc. The placement and sizing of DG play a major role in optimizing these parameters. Therefore, this chapter proposes a modified Particle Swarm Optimization (PSO) algorithm for finding the optimal placement and sizing of distributed generation in the radial distribution system. Two types of DGs such as an active power and reactive power DGs are tested on standard IEEE 33 radial bus system. Moreover, it can be realized that proposed method gives very effective results when both of active and reactive power DGs are integrated into the distribution system.

Multi-Objective VAR Dispatch Using Particle Swarm Optimization

2005 ◽  
Vol 4 (1) ◽  
Author(s):  
Mr. S. Durairaj ◽  
Dr. P.S. Kannan ◽  
Dr. D. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Reactive Power ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Voltage Profile ◽  
Control Variables ◽  
Swarm Optimization ◽  
Voltage Stability Enhancement

Reactive Power Dispatch (RPD) is one of the important tasks in the operation and control of power system. The objective is to apply Particle Swarm Optimization (PSO) algorithm for arriving optimal settings of RPD problem control variables. Incorporation of PSO as a derivative free optimization technique in solving RPD problem significantly relieves the assumptions imposed on the optimized objective functions. The proposed algorithm has been applied to find the optimal reactive power control variables in IEEE 30-bus system and in a practical Indian power system with different objectives that reflect loss minimization, voltage profile improvement and voltage stability enhancement. The results of this approach have been compared with the results of Genetic Algorithm (GA). The results are promising and show the effectiveness and robustness of the proposed approach.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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.

Swarm-Intelligence-Based Optimal Placement and Sizing of Distributed Generation in Distribution Network

2018 ◽  
pp. 29-48 ◽  
Author(s):  
Mahesh Kumar ◽  
Perumal Nallagownden ◽  
Irraivan Elamvazuthi ◽  
Pandian Vasant ◽  
Luqman Hakim Rahman
Keyword(s):  
Distributed Generation ◽  
Radial Distribution ◽  
Distribution System ◽  
Voltage Stability ◽  
Reactive Power ◽  
Distribution Network ◽  
Stability Index ◽  
Pso Algorithm ◽  
Distribution Networks ◽  
Optimal Placement

In the distribution system, distributed generation (DG) are getting more important because of the electricity demands, fossil fuel depletion and environment concerns. The placement and sizing of DGs have greatly impact on the voltage stability and losses in the distribution network. In this chapter, a particle swarm optimization (PSO) algorithm has been proposed for optimal placement and sizing of DG to improve voltage stability index in the radial distribution system. The two i.e. active power and combination of active and reactive power types of DGs are proposed to realize the effect of DG integration. A specific analysis has been applied on IEEE 33 bus system radial distribution networks using MATLAB 2015a software.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, which based on PSO algorithm, and the implementing framework of PSO and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

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