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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TL;DR: In this paper, the authors proposed a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm.
Abstract: This paper proposes a novel global maximum power point tracking (MPPT) strategy for solar photovoltaic (PV) modules under partial shading conditions using a dynamic particle swarm optimisation (PSO) algorithm. Solar PV modules have non-linear V-P characteristics with local maximum power points (MPPs) under partial shading conditions. In order to continuously harvest maximum power from solar PV modules, it always has to be operated at its global MPP which is determined using the proposed dynamic PSO algorithm. The obtained simulation results are compared with MPPs achieved using the standard PSO, and Perturbation and Observation (P&O) algorithms to confirm the effectiveness of the proposed algorithm under partial shading conditions.
32 citations
TL;DR: With the dual mechatronic MPPT with PN+OPSO control algorithms, the maximum power in a limited roof space of the vehicle is possible and this will increase the efficiency of the PHEV.
Abstract: This paper aims at increasing the efficiency of the plug-in hybrid electric vehicle (PHEV) by using rotatable solar panel. Conventionally, the PHEV with solar panel has a critical problem of putting on the roof of a PHEV. Since the limited space on the roof of the vehicle is not large enough, rotatable structure is considered to track the sunlight by mechanical Petri-net (PN)-based maximum power point tracking (MPPT) control. A stepping motor is used to control the rotating angle of the rotating solar panel. In addition, the electric MPPT with orthogonal particle swarm optimization (OPSO) method is also included. With the dual mechatronic MPPT with PN+OPSO control algorithms, the maximum power in a limited roof space of the vehicle is possible. The solar panel has not to be very large. This will increase the efficiency of the PHEV. It is convinced that the proposed dual mechatronic PN+OPSO MPPT controllers are helpful to the PHEV system.
30 citations
Additional excerpts
...C 1 and C 2 values are selected by ( ) ¸ ¸ ¹ · ¨ ¨ © § − − = C C t C C t t min 1 max 1 max max 1 1 (13) ( ) ¸ ¸ ¹ · ¨ ¨ © § − − = C C k C C k k min 2 max 2 max min 2 2 (14) Jain-Long Kuo et al.[21], designed a Plug-in-hybrid electric vehicle, an electromechanical system....
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TL;DR: In this paper, a maximum power point (MPP) detection algorithm for photovoltaic (PV) systems is introduced, which uses the experimental information obtained from a singlevoltage sensor, measured on a capacitor load, either linked at the output of a solar cell, a PV module, or a PV string.
Abstract: In this paper, a maximum power point (MPP) detection algorithm for photovoltaic (PV) systems is introduced, which uses the experimental information obtained from a single-voltage sensor, measured on a capacitor load, either linked at the output of a solar cell (SC), a PV module, or a PV string. The voltage signal is naturally affected by the noise which has a relevant effect on the process necessary for MPP determination, such as voltage first- and second-order derivatives. The aim of this study is to demonstrate the technical feasibility of a maximum power point tracker (MPPT) based on the present MPP detection algorithm employing a single-voltage sensor acquiring a signal affected by the significant noise. Theoretical evaluation, numerical simulations, and experimental measurements are carried out. Excellent agreement between the theoretical and experimental behavior is observed. Conditions for correct MPP detection are shown and good performances are obtained.
25 citations
"Evaluation of particle swarm optimi..." refers background in this paper
...Converter topologies available are buck, boost, buck-boost, Cuk, Zepic and Zeta [12],[17]....
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20 Jun 2005
TL;DR: In this article, the readings of the load parameters instead of the photovoltaic panel output parameters were used for maximum power point tracking in photovolcanic maximum energy point tracking.
Abstract: Photovoltaic maximum power point tracking can be accomplished by monitoring the readings of the load parameters instead of the photovoltaic panel output parameters. It is demonstrated that load parameters based maximum power point tracking has advantages over conventional method in terms of tracking efficiency and simplicity of the hardware and software involved.
19 citations
19 Jun 2014
TL;DR: In this paper, a modified Particle Swarm Optimization (PSO) algorithm is proposed for maximum power point tracking in solar PV system, which is tested on a DC-DC CUK Converter and simulation results are compared with Incremental Conductance and Hill Climbing methods.
Abstract: This paper proposes modified Particle Swarm Optimization (PSO) for maximum power point tracking in solar PV system. MPPT methods are used for extraction of maximum power from PV panels; since the non-linear characteristics of panel hinders the power output from the panel. Earlier proposed methods like HC, Incremental Conductance suffers from steady state and lower efficiency. This proposed evolutionary computation technique assures nearly zero steady state oscillation and faster convergence while tracking maximum power. The proposed PSO algorithm is tested on a DC-DC CUK Converter and simulation results are compared with Incremental Conductance and Hill Climbing methods.
11 citations
"Evaluation of particle swarm optimi..." refers methods in this paper
...Particle position [13] DC /...
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...M, et al. [13] proposed a modified PSO algorithm....
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