Optimization based optimal control of solar PV system
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.
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TL;DR: A novel and fast maximum power point tracking algorithm for a photovoltaic generation system that can successfully track the global MPP in 249 of 252 different PSC patterns and can improve the tracking time by 46.42% and 11.76% compared to conventional perturb and observe (P&O) and variable-step P&O technique.
Abstract: In this study, a novel and fast maximum power point tracking (MPPT) algorithm for a photovoltaic generation system is proposed. The main idea is to remove the random number in the voltage calculation equation of the conventional cuckoo search method. The advantages of the proposed method include 1) fast maximum power point (MPP) tracking speed and high MPP tracking accuracy under both uniform insolation and partially shaded conditions (PSCs), 2) simple MPPT structure, and 3) consistent solution can be achieved with only three particles and only one parameter is required to be tuned. In order to validate the effectiveness and correctness of the proposed method, both simulation and experiments are carried out on a 300 W prototyping circuit. According to the experimental results, the proposed MPPT method can improve the tracking time by 46.42% and 11.76% comparing to conventional perturb and observe (P&O) and variable-step P&O technique, respectively. In addition, the proposed method can successfully track the global MPP in 249 of 252 different PSC patterns. The tracking accuracies under both uniform irradiance and PSC conditions are all higher than 99.8%.
102Â citations
01 Sep 2017
TL;DR: In this article, the authors proposed MPPT Partial Shading using Modified Particle Swarm Optimization (MPSO) method to solve partial shading conditions so the MPPT can reach GMPP without being trapped in LMPP.
Abstract: Solar energy source can be utilized into electric energy using Photovoltaic (PV) panel. Maximum power in PV can be obtained in sunny weather conditions, optimum temperature, and unshaded surface. When PV is blocked by an object, the resulting power will decrease depending on the size of the barrier. Barriers of the sun intensity are shadows of trees, buildings, peoples, and others. These conditions cause PV can't absorb the maximum intensity of sunlight. Unfortunately, an obstructed PV surface condition may affect the resulting characteristic curves. These conditions can produce different characteristic curves. The difference is the position of the maximum power point that should be only one that is GMPP (Global Maximum Power Point) but this is divided into two there are GMPP and LMPP (Local Maximum Power Point). These conditions make a simple MPPT trapped in LMPP or an unreal maximum power point. To solve this problem, in this paper proposed MPPT Partial Shading using Modified Particle Swarm Optimization (MPSO) method. The MPSO method is chosen to solve partial shading conditions so the MPPT can reach GMPP without being trapped in LMPP. The MPSO method is applied to the Sepic converter to generate maximum power. According to simulation result, the MPSO method has a good accuracy greater than 95% and a fast time convergence is about 0.5 until 1 second to obtain a MPP depend on the shading pattern.
31Â citations
Journal Article•
TL;DR: In this article, the development status and trend of Chinese PV technology and industry are reported, and the research situation of BOS component technology for PV system is introduced by means of examples, such as large scale inverters for grid connection and automatic tracking system.
Abstract: The development status and trend of Chinese PV technology and industry are reported in this paper. Also the research situation of BOS component technology for PV system is introduced by means of examples, such as large scale inverters for grid connection and automatic tracking system. The main problems of Chinese PV development in recent stage are summarized; Energy configuration modality, its development potential and the grim situation of energy and environment are analyzed; It is clarified that transition of PV power generation from complementary energy to substitute energy and at last PV power as leading energy are an inevitable trend. Finally according to the realistic situation and synthetic strength of our country the strategic goal, road map and its guarantee measure of Chinese PV power development before 2050 are put forward in the paper.
8Â citations
01 Jan 2016
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.
3Â citations
01 Dec 2017
TL;DR: A new adaptive PSO algorithm to cooperatively search underwater acoustic source for dedicated swarm of autonomous surface vehicles is proposed and results show the reliability and performance improvement of the proposed method compared to several existing search algorithm benchmarks.
Abstract: Source searching task is important in many real world applications. Searching a source with complex spatial pattern especially in a large workspace is a challenging task. The task becomes harder if a single robotic platform is used. In underwater perspective, such examples include underwater acoustic source searching which is useful during flight black box searching, mines detection and localizing underwater vehicle applications. In this paper, a new adaptive PSO algorithm to cooperatively search underwater acoustic source for dedicated swarm of autonomous surface vehicles is proposed. In the proposed PSO based searching algorithm, velocity parameters (i.e. inertia weight and acceleration coefficients) are adaptively updated considering the trajectory stability of the robot. In addition, to expedite the convergence speed, each parameter is updated for each robot and each dimension independently at each iteration. To validate the proposed strategy, a simulation study is performed. Simulation results show the reliability and performance improvement of the proposed method compared to several existing search algorithm benchmarks.
3Â citations