Journal ArticleDOI
Review article: A review of particle swarm optimization and its applications in Solar Photovoltaic system
Anula Khare,Saroj Rangnekar +1 more
- Vol. 13, Iss: 5, pp 2997-3006
TLDR
Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.Abstract:
Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization Algorithm Based on Chaotic Sequences and Dynamic Self-Adaptive Strategy
TL;DR: A novel improved particle swarm optimization algorithm was proposed, which improved the premature convergence problem with faster convergence rate and strong ability to jump out of local optimum.
Proceedings ArticleDOI
Optimal Sizing of Battery-Ultracapacitor Hybrid Energy Storage Device in a Standalone Photovoltaic System
TL;DR: In this article, a standalone photovoltaic system with battery-ultracapacitor HESD has been considered for case study, and a suitable problem formulation with the necessary objective function and constraints has been developed for the system.
Book ChapterDOI
Photovoltaic Maximum Power Point Trackers: An Overview
Ali M. Eltamaly,Ali M. Eltamaly +1 more
TL;DR: In this article, the authors introduce an overview of the PV maximum power point tracker (MPPT) techniques and provide a comprehensive comparison of these techniques and their performance in tracking the MPP.
Proceedings ArticleDOI
Improved SPSO-based Parameter Identification of Solar PV Cells I-V Model
TL;DR: Based on the nonlinear equation of PV cells I-V experimental model, which cannot be used directly to analyze the parameters' value with the output current and voltage, the authors presented a quasi-linear differential equation of dV/dI to turn the parameters to differential coefficient of the dynamic relationship.
Journal ArticleDOI
Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition
TL;DR: The formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process by utilizing a special time-varying weighting coefficient.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
Particle swarm optimization: developments, applications and resources
TL;DR: Developments in the particle swarm algorithm since its origin in 1995 are reviewed and brief discussions of constriction factors, inertia weights, and tracking dynamic systems are included.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.