Evaluation of particle swarm optimization algorithm in photovoltaic applications
01 Jan 2016-pp 1-6
TL;DR: The evaluation of Particle Swarm Optimization (PSO) algorithm in MPPT based solar power generation systems is discussed and the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems are described.
Abstract: Solar energy is the prime source of consumption for the world. It is the potential candidate for meeting the growing energy demand and solving environmental issues. To derive the maximum Power (MP) from the system, Maximum Power Point Tracking (MPPT) methods are implemented. It is highly essential to derive MP from the available solar energy. Over the years, numerous MPPT methods have been developed and presented in the literature. This paper discusses the evaluation of Particle Swarm Optimization (PSO) algorithm in MPPT based solar power generation systems. It describes the different methodologies adopted to extract the maximum power from the solar array in photovoltaic (PV) power systems.
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TL;DR: The proposed advanced particle swarm optimization algorithm aims to catch the global maximum power point much faster, accurately and to reduce the chatter in the power curve and accelerates the globalmaximum tracking time with gridding the initial search area.
Abstract: Maximum power point trackers are in charge of absorbing the maximum potential power from the photovoltaic panels. Thus, this makes the maximum power point trackers the fundamental parts of the photovoltaic panel systems. The conventional maximum power point tracker algorithms are working well under balanced insolation conditions, however when the partial shade condition occurs, those algorithms are trapped at the local maxima. Hence, under partial shade conditions, the need for a global maximum power point tracking algorithm arises. Particle swarm optimization is a preferential algorithm of maximum power point trackers in literature, especially in partial shade conditions. This paper is focused on improving the existing particle swarm optimization algorithm for maximum power point trackers. The proposed advanced particle swarm optimization algorithm aims to catch the global maximum power point much faster, accurately and to reduce the chatter in the power curve. The proposed method accelerates the global maximum tracking time with gridding the initial search area. The effectiveness of the proposed method is demonstrated with simulation results and these results are compared with a conventional particle swarm optimization method under step changes in irradiance and partial shade conditions of an array of photovoltaic panels.
4 citations
DOI•
01 Jan 2017
TL;DR: The research described in the thesis focuses on the analysis of integrating multi-megawatt photovoltaics systems with battery energy storage into the existing grid and on the theory supporting the electrical operation of components and systems.
Abstract: OF THESIS ANALYSIS AND SIMULATION OF PHOTOVOLTAIC SYSTEMS INCORPORATING BATTERY ENERGY STORAGE Solar energy is an abundant renewable source, which is expected to play an increasing role in the grid’s future infrastructure for distributed generation. The research described in the thesis focuses on the analysis of integrating multi-megawatt photovoltaics systems with battery energy storage into the existing grid and on the theory supporting the electrical operation of components and systems. The PV system is divided into several sections, each having its own DC-DC converter for maximum power point tracking and a two-level grid connected inverter with different control strategies. The functions of the battery are explored by connecting it to the system in order to prevent possible voltage fluctuations and as a buffer storage in order to eliminate the power mismatch between PV array generation and load demand. Computer models of the system are developed and implemented using the PSCAD/EMTDC software.
2 citations
04 Feb 2021
TL;DR: In this paper, a DC-to-DC converter has been used for matching the impedance between PV module or array of modules so as to extract maximum power from PV modules or strings of PV modules.
Abstract: in recent trend, there has been rapidly increase of demand in energy. For conserving energy, innovative solutions are being proposed for reducing it. An eco-friendly system should be created in order to save investment on electricity and then maximize the return cost on investment for investing in solar modules. The photovoltaic industry happens to be more efficient and less expensive systems so that it can be more competitive in nature when it is being compared with other conventional sources. For PV modules, irradiation and panel temperature faces more challenges because these parameters are unstable. Therefore, the electricity generations from PV panel are not stable in nature. So, Maximum Power Point Tracking (MPPT) technique is used for extracting maximum power from PV modules. A DC-to-DC converter has been used for matching the impedance between PV module or array of modules so as to extract maximum power from PV modules or strings of PV modules. In this paper, perturb & observe (P&O), Particle swarm 0ptimization (PSO) and Grey wolf optimization (GWO) methods have been applied. The performance of these algorithms has been verified for MPPT in MATLAB /Simulink environment.
References
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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
"Evaluation of particle swarm optimi..." refers background in this paper
...It is further improved by the insertion of a constant, called inertia weight 'Ȧ', with velocity to improve the performance[20]....
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TL;DR: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed in this paper, and at least 19 distinct methods have been introduced in the literature, with many variations on implementation.
Abstract: The many different techniques for maximum power point tracking of photovoltaic (PV) arrays are discussed. The techniques are taken from the literature dating back to the earliest methods. It is shown that at least 19 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in PV power generation.
5,022 citations
16 Jul 2000
TL;DR: It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.
Abstract: The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension. This approach provides performance on the benchmark functions superior to any other published results known by the authors.
2,922 citations
"Evaluation of particle swarm optimi..." refers background or methods in this paper
...It was developed by Russel C.Eberheart and James Kennedy in the year 1995 and modified by Eberheart, Simpson and Dobbins [18] ....
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..." Another form of velocity equation with Constriction factor 'K'[18] is necessary to converge the PSO....
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TL;DR: In this article, the authors proposed an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm.
Abstract: This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shading of the PV array. Its performance is compared with the conventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck-boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results.
851 citations
TL;DR: In this article, a fuzzy-logic controller for maximum power point tracking of photovoltaic (PV) systems is proposed, which improves the hill-climbing search method by fuzzifying the rules of such techniques and eliminates their drawbacks.
Abstract: A new fuzzy-logic controller for maximum power point tracking of photovoltaic (PV) systems is proposed. PV modeling is discussed. Conventional hill-climbing maximum power-point tracker structures and features are investigated. The new controller improves the hill-climbing search method by fuzzifying the rules of such techniques and eliminates their drawbacks. Fuzzy-logic-based hill climbing offers fast and accurate converging to the maximum operating point during steady-state and varying weather conditions compared to conventional hill climbing. Simulation and experimentation results are provided to demonstrate the validity of the proposed fuzzy-logic-based controller.
578 citations