The new MPPT algorithm using ANN-based PV

Dzung, Phan Quoc; Khoa, Le Dinh; Lee, Hong Hee; Phương, Lê Minh; Vu, Nguyen Truong Dan

doi:10.1109/ifost.2010.5668004

Cited by 58 publications

(30 citation statements)

References 11 publications

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“…The artificial intelligence techniques Artificial Neural Network (ANN) and Fuzzy Logic Controller (FLC) are suitable to handle nonlinearity of the PV module characteristic and uncertainty of the environment [6,7]. This work presents performance comparison of MPPT methods using artificial intelligence techniques ANN and FLC based controller.…”

Section: Introductionmentioning

confidence: 98%

“…This work presents performance comparison of MPPT methods using artificial intelligence techniques ANN and FLC based controller. ANN and FLC operates fast in comparison to conventional methods and accurately tracks MPP under slow and fast changing solar irradiation producing less oscillations and less power loss which results in maximum power delivered to the grid [7].…”

Section: Introductionmentioning

confidence: 99%

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AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

Arora¹,

Gaur

2015

2015 International Conference on Advances in Computer Engineering and Applications

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This paper presents the artificial neural network (ANN), fuzzy logic controller (FLC) maximum power point tracking (MPPT) methods in grid connected photovoltaic (PV) systems for optimizing the solar energy efficiency. All the methods are simulated in MATLAB-Simulink, respectively together with SunPower-SPR305 PV module connected to singleended primary inductor converter (SEPIC). Performance assessment covers efficiency, overshoot, settling time response, oscillations and stability.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

Arora¹,

Gaur

2015

2015 International Conference on Advances in Computer Engineering and Applications

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“…Artificial neural network (ANN) method, especially, can handle non-linearity characteristic of the PV module and uncertainty of the weather condition (Ramaprabha and Mathur 2011). Different ANN MPPTs have been developed so far, which are different in some aspects of ANN architecture, control variable, load ranges and complexity of the controller variables (Asiful Islam and Ashfanoor Kabir 2011), (Ramaprabha and Mathur 2011), (Alabedin, El-Saadany, and Salama 2011), (Anitha and Prabha 2011), (Dzung 2010), (Jie and Ziran 2011), (Premrudeepreechacharn and Patanapirom 2003), (Hiyama 2009). In a research conducted by Xu et al (2011), two sensors of irradiation and temperature are used, which straightly deliver the duty cycle of the converter.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

Khanaki

Radzi

Marhaban

2014

International Journal of Green Energy

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This paper presents a two-stage maximum power point tracking (MPPT) controller using Artificial Neural Network (ANN) for photovoltaic (PV) standalone system, under varying weather conditions of solar irradiation and module temperature. At the first-stage, the ANN algorithm locates the maximum power point (MPP) associated to solar irradiation and module temperature. Then, a simple controller at the second-step, by changing the duty cycle of a DC-DC boost converter, tracks the MPP. In this method, in addition to experimental data collection for training the ANN, a circuit is designed in MATLAB-Simulink to acquire data for whole ranges of weather condition. The whole system is simulated in Simulink. Simulation results show small transient response time, and low power oscillation in steady-state. Furthermore, dynamic response verifies that this method is very fast and precise at tracking the MPP under rapidly changing irradiation, and has very low power oscillation under slowly changing irradiation. Experimental results are provided to verify the simulation results as well.

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“…Much research on MPPT has been conducted, and various MPPT algorithms have been proposed. The maximum power point (MPP) may be located by the perturbation and observation (P&O) algorithms [3], the incremental conductance (InC) algorithm [4], and the artificial neural network algorithm [5].…”

Section: Introductionmentioning

confidence: 99%

Synergetic Control of Grid-Connected Photovoltaic Systems

Qian¹,

Li²,

Wu³

et al. 2017

International Journal of Photoenergy

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It is important to improve the dynamic performance and the low-voltage ride-through (LVRT) capability of a grid-connected photovoltaic (PV) system. This paper presents synergetic control for the control of a grid-connected PV system. Modeling of a grid-connected PV system is described, and differential-algebra equations are obtained. Two control strategies are used in normal operation and during LVRT of a PV system. Practical synergetic controllers with two control strategies are synthesized. The mathematical expressions are derived for computing control variables. The design of the synergetic controllers does not require the linearization of the grid-connected PV system. A grid-connected PV system with synergetic controllers is simulated in Simulink surroundings. The control performance is studied in normal operation and during LVRT. Simulation results show that the synergetic controllers are robust and have good dynamic characteristics under different operation states.

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The new MPPT algorithm using ANN-based PV

Cited by 58 publications

References 11 publications

AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

AI based MPPT methods for grid connected PV systems under non linear changing solar irradiation

Artificial neural network based maximum power point tracking controller for photovoltaic standalone system

Synergetic Control of Grid-Connected Photovoltaic Systems

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