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Journal ArticleDOI

Review article: A review of particle swarm optimization and its applications in Solar Photovoltaic system

01 May 2013-Vol. 13, Iss: 5, pp 2997-3006
TL;DR: 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.
Citations
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Journal ArticleDOI
TL;DR: In this article, a state-of-the-art analysis of the available maximum power point tracking (MPPT) techniques and their comprehensive comparative analysis based on 110 standard research articles is presented.
Abstract: Unfilled gap of prolonged energy demand by conventional energy sources and consent of global warming as its vulnerable outcome provides a vent to search compatible option. Augmentation in use of solar energy reveled through last 3 decades portrays its heterogeneous rewards in the prevailing energy scenario. Nevertheless solar PV system arises as viable option in the critical power system era its low efficiency energy conversion attribute necessitates an efficient power conversion system. The nonlinearity of I–V (current–voltage) characteristic and its alteration for an assorted insolation and temperature values may enable the alteration in terminal voltage. This may deviates maximum power point due to which the available maximum power delivery to load can be differed. Literature of this field reiterated that the uniform insolation and partial shading condition demands undeniable need of maximum power point tracking. Nonetheless through investigation in this direction furnishes the availability of a bunch of such techniques; each of them posses its own pros and cones. This ubiquitous trait of available maximum power point tracking (MPPT) techniques unfolds the complexity in its precise selection. To diminish such complexity this paper offers a state of art of various MPPT technique and their comprehensive comparative analysis based on 110 standard research articles. The focus of this paper is to offer a better commencement and to furnish valued information for investigators of this field.

325 citations

Journal ArticleDOI
TL;DR: This paper presents the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach and mentions some metaheuristics belonging to the SI.
Abstract: In this paper, we present the swarm intelligence (SI) concept and mention some metaheuristics belonging to the SI. We present the particle swarm optimization (PSO) algorithm and the ant colony optimization (ACO) method as the representatives of the SI approach. In recent years, researchers are eager to develop and apply a variety of these two methods, despite the development of many other newer methods as Bat or FireFly algorithms. Presenting the PSO and ACO we put their pseudocode, their properties, and intuition lying behind them. Next, we focus on their real-life applications, indicating many papers presented varieties of basic algorithms and the areas of their applications.

168 citations

Journal ArticleDOI
15 Feb 2019-Energy
TL;DR: A methodology both to optimize and to perform a sensitivity analysis of an autonomous hybrid PV-diesel-battery energy system and it was found that the PSO based approach is more cost effective with more PV penetration than HOMER.

167 citations

Journal ArticleDOI
TL;DR: The generic theory is discussed along with illustrative industrial process applications that include a real liquid level control application, wind turbines and a nonlinear servo system and nature-inspired optimal control.

148 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the bio-inspired algorithms used for global maximum power point tracking and the modified and combined forms of these methods found to have better performance than original algorithms.
Abstract: Solar energy is one of the most promising renewable energy resource due to its variety of advantages. The photovoltaic systems have a remarkable development over the past few decades. As the maximum power point of the photovoltaic system varies with the change in environmental conditions, the maximum power point tracking technology is necessary to harvest maximum power from the photovoltaic systems. However, multiple peaks occur in the power-voltage (P-V) curve during partial shading conditions. In such condition, many traditional maximum power point tracking methods like perturbation and observation, and incremental conductance may become invalid due to involvement in the local maximum power point. Many advanced methods based on the artificial intelligence like artificial neural network, and fuzzy logic control can track the global maximum power point. However, they are not feasible in real complex environment because they need massive training and broader experience. Alternatively, bio-inspired maximum power point tracking algorithms deal properly with such situations. In recent years, researchers have widely applied bio-inspired algorithms to track the global maximum power point of photovoltaic system during partial shading situations. This paper presents a comprehensive review of the bio-inspired algorithms used for global maximum power point tracking. Various tracking methods are discussed and compared in terms of their characteristics and corresponding improved methods. It also presents the advantages and disadvantages of each method. The modified and combined forms of these methods found to have better performance than original algorithms. Overall, the performance of swarm intelligence based algorithms is found better than evolutionary algorithms. This review may help the researchers to acquire comprehensive information about the application of bio-inspired algorithms to gain maximum power from the photovoltaic systems, and furthermore, help them to choose an efficient way of global maximum power point tracking in photovoltaic systems during partial shading conditions.

127 citations

References
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Proceedings ArticleDOI
06 Aug 2002
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.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

35,104 citations

Proceedings ArticleDOI
04 Oct 1995
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.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.

14,477 citations

Proceedings ArticleDOI
04 May 1998
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.
Abstract: Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manipulated according to the rule of survival of the fittest through "genetic" operations, such as mutation, crossover and reproduction. A best solution is evolved through the generations. In contrast to evolutionary computation techniques, Eberhart and Kennedy developed a different algorithm through simulating social behavior (R.C. Eberhart et al., 1996; R.C. Eberhart and J. Kennedy, 1996; J. Kennedy and R.C. Eberhart, 1995; J. Kennedy, 1997). As in other algorithms, a population of individuals exists. This algorithm is called particle swarm optimization (PSO) since it resembles a school of flying birds. In a particle swarm optimizer, instead of using genetic operators, these individuals are "evolved" by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its companions' flying experience. We introduce a new parameter, called inertia weight, into the original particle swarm optimizer. Simulations have been done to illustrate the significant and effective impact of this new parameter on the particle swarm optimizer.

9,373 citations

Proceedings ArticleDOI
Eberhart1, Yuhui Shi
27 May 2001
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.
Abstract: This paper focuses on the engineering and computer science aspects of developments, applications, and resources related to particle swarm optimization. Developments in the particle swarm algorithm since its origin in 1995 are reviewed. Included are brief discussions of constriction factors, inertia weights, and tracking dynamic systems. Applications, both those already developed, and promising future application areas, are reviewed. Finally, resources related to particle swarm optimization are listed, including books, Web sites, and software. A particle swarm optimization bibliography is at the end of the paper.

4,041 citations

Book ChapterDOI
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.
Abstract: 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. Analysis of experiments demonstrates the validity of these guidelines.

3,557 citations