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Thoai N. Nguyen

Bio: Thoai N. Nguyen is an academic researcher from Vietnam National University, Ho Chi Minh City. The author has contributed to research in topics: Algorithm design & Maximum power point tracking. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Papers
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Proceedings ArticleDOI
28 May 2013
TL;DR: The results show that the dynamic PSO algorithm is better than the standard PSO and P&O algorithms for determining and tracking MPPs of solar PV panels.
Abstract: This paper proposes a novel application of a dynamic particle swarm optimization (PSO) algorithm for determining a maximum power point (MPP) of a solar photovoltaic (PV) panel. Solar PV cells have a non-linear V-I characteristic with a distinct MPP which depends on environmental factors such as temperature and irradiation. In order to continuously harvest maximum power from the solar PV panel, it always has to be operated at its MPP. The proposed dynamic PSO algorithm is one of the PSO algorithm variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO algorithm as linear time-varying parameters to improve the global search capability of particles in the early stage of the optimization process and direct the global optima at the end stage. The obtained simulation results are compared with MPPs achieved using other algorithms such as the standard PSO, and Perturbation and Observation (P&O) algorithms under various atmospheric conditions. The results show that the dynamic PSO algorithm is better than the standard PSO and P&O algorithms for determining and tracking MPPs of solar PV panels.

14 citations


Cited by
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Journal ArticleDOI
TL;DR: A novel overall distribution MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithms to improve the accuracy of MPPT.
Abstract: Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P – V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

345 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed an adaptive and optimal control strategy for a solar photovoltaic (PV) system, which ensures that the solar PV panel is always perpendicular to sunlight and simultaneously operated at its maximum power point (MPP) for continuously harvesting maximum power.
Abstract: This paper proposes an adaptive and optimal control strategy for a solar photovoltaic (PV) system. The control strategy ensures that the solar PV panel is always perpendicular to sunlight and simultaneously operated at its maximum power point (MPP) for continuously harvesting maximum power. The proposed control strategy is the control combination between the solar tracker (ST) and MPP tracker that can greatly improve the generated electricity from solar PV systems. Regarding the ST system, the paper presents two drive approaches including open- and closed-loop drives. Additionally, the paper also proposes an improved incremental conductance algorithm for enhancing the speed of the MPP tracking of a solar PV panel under various atmospheric conditions as well as guaranteeing that the operating point always moves toward the MPP using this proposed algorithm. The simulation and experimental results obtained validate the effectiveness of the proposal under various atmospheric conditions.

117 citations

Journal ArticleDOI
TL;DR: A modified Ant Colony Optimization (ACO) Algorithm based controller for Maximum Power Point Tracking (MPPT) of a stand-alone photovoltaic (PV) system is presented.

15 citations

01 Jan 2016
TL;DR: In this paper, the authors proposed an enhancement to the conventional perturb and observe (P&O) maximum power point tracking (MPPT) technique in order to overcome the disadvantages of this method such as oscillation and slow tracking under sudden change of atmospheric conditions.
Abstract: Modeling and analysis of photovoltaic (PV) system is substantial for designers of solar power plants to do a yield investigation that precisely predicts the expected output power under changing weather conditions. The model allows the prediction of PV module’s behaviour and characteristics based on the mathematical model equivalent circuit using Matlab/Simulink platform under different temperature and solar radiation readings. The second part of this paper proposes an enhancement to the conventional perturb and observe (P&O) maximum power point tracking (MPPT) technique in order to overcome the disadvantages of this method such as oscillation and slow tracking under sudden change of atmospheric conditions. The proposed method suggested that utilizing a variable perturbation step size depending on power changes instead of constant step size which is used in conventional P&O algorithm in order to ensure that the solar energy is captured and converted as much as possible. The simulation results are compared with that of traditional P&O to demonstrate the effectiveness of the proposed method.

4 citations