Parametric Performance Evaluation of Different Types of Particle Swarm Optimization Techniques Applied in Distributed Generation System

2013 ◽  
pp. 349-356
Author(s):  
S. Kumar ◽  
S. Sau ◽  
D. Pal ◽  
B. Tudu ◽  
K. K. Mandal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Evaluation ◽  
Distributed Generation ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Generation System ◽  
Swarm Optimization ◽  
Different Types

scholarly journals Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction

2020 ◽  
Vol 20 (1) ◽  
pp. 16 ◽  
Author(s):  
Eshan Karunarathne ◽  
Jagadeesh Pasupuleti ◽  
Janaka Ekanayake ◽  
Dilini Almeida
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Premature Convergence ◽  
Optimal Placement ◽  
Swarm Optimization ◽  
Comparison Results

With the technological advancements, Distributed Generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle Swarm Optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, “Comprehensive Learning Particle Swarm Optimization (CLPSO)” to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles’ historical best information and learning probability value are used to update a particle’s velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO.

scholarly journals Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1795
Author(s):  
Manuel Cedillo-Hernandez ◽  
Antonio Cedillo-Hernandez ◽  
Francisco J. Garcia-Ugalde
Keyword(s):  
Fourier Transform ◽  
Particle Swarm Optimization ◽  
Discrete Fourier Transform ◽  
Image Watermarking ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Robust Watermarking ◽  
Swarm Optimization ◽  
General Terms ◽  
Operation Parameters

Robust digital image watermarking is an information security technique that has been widely used to solve several issues related mainly with copyright protection as well as ownership authentication. In general terms, robust watermarking conceals a small signal called a “watermark” in a host image in a form imperceptible to human vision. The efficiency of conventional robust watermarking based on frequency domain depend directly on the results of performance in terms of robustness and imperceptibility. According to the application scenario and the image dataset, it is common practice to adjust the key parameters used by robust watermarking methods in an experimental form; however, this manual adjustment may involve exhaustive tasks and at the same time be a drawback in practical scenarios. In recent years, several optimization techniques have been adopted by robust watermarking to allowing adjusting in an automatic form its key operation parameters, improving thus its performance. In this context, this paper proposes an improved robust watermarking algorithm in discrete Fourier transform via spread spectrum, optimizing the key operation parameters, particularly the amounts of bands and coefficients of frequency as well as the watermark strength factor using particle swarm optimization in conjunction with visual information fidelity and bit correct rate criteria. Experimental results obtained in this research show improved robustness against common signal processing and geometric distortions, preserving a high visual quality in color images. Performance comparison with conventional discrete Fourier transform proposal is provided, as well as with the current state-of-the-art of particle swarm optimization applied to image watermarking.

scholarly journals Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ya-zhong Luo ◽  
Li-ni Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Mission Design ◽  
Trajectory Design ◽  
Time Transfer ◽  
Pareto Solution ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

scholarly journals Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3112
Author(s):  
Donghyeon Lee ◽  
Seungwan Son ◽  
Insu Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Power ◽  
Distributed Generation ◽  
Optimal Allocation ◽  
Power Grid ◽  
Particle Swarm ◽  
Renewable Energy Resources ◽  
Swarm Optimization ◽  
Large Capacity ◽  
Electric Power Grid

Widespread interest in environmental issues is growing. Many studies have examined the effect of distributed generation (DG) from renewable energy resources on the electric power grid. For example, various studies efficiently connect growing DG to the current electric power grid. Accordingly, the objective of this study is to present an algorithm that determines DG location and capacity. For this purpose, this study combines particle swarm optimization (PSO) and the Volt/Var control (VVC) of DG while regulating the voltage magnitude within the allowable variation (e.g., ±5%). For practical optimization, the PSO algorithm is enhanced by applying load profile data (e.g., 24-h data). The objective function (OF) in the proposed PSO method considers voltage variations, line losses, and economic aspects of deploying large-capacity DG (e.g., installation costs) to transmission networks. The case studies validate the proposed method (i.e., optimal allocation of DG with the capability of VVC with PSO) by applying the proposed OF to the PSO that finds the optimal DG capacity and location in various scenarios (e.g., the IEEE 14- and 30-bus test feeders). This study then uses VVC to compare the voltage profile, loss, and installation cost improved by DG to a grid without DG.

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