A novel ANFIS controller for maximum power point tracking in photovoltaic systems

Khaehintung, N.; Sirisuk, P.; Kurutach, Werasak

doi:10.1109/peds.2003.1283074

Cited by 27 publications

(24 citation statements)

References 3 publications

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“…In addition, ANFIS can generate the fuzzy rules automatically. Various ANFIS-based MPPT methods have been proposed to achieve MPPT [20][21][22][23][24]. The input variables and the output variables are different from one configuration to another.…”

Section: Introductionmentioning

confidence: 99%

“…The input variables and the output variables are different from one configuration to another. The input variables in [20] are the change in the PV voltage (ΔV PV ) and the change in the PV power (ΔP PV ), while the output variable is the duty cycle. On the other hand, the input variables to the ANFIS in [22] are the open circuit voltage (V oc ) and the short circuit current (I sc ), while the output variable is the voltage at maximum power (V MPP ).…”

Section: Introductionmentioning

confidence: 99%

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Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

Noman

Addoweesh

Alolah

2017

International Journal of Photoenergy

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Photovoltaic (PV) module behavior is not linear in nature with respect to environmental conditions and hence exhibits nonlinear PV curves. There is only a single point in the nonlinear PV curve at which the power is maximum. Therefore, special methods have been proposed to track this maximum power point (MPP). This paper proposed an intelligent method for MPP tracking (MPPT) based on adaptive neuro-fuzzy inference system (ANFIS) controller. The proposed system consists of a PV module connected to a DC-DC isolated Ćuk converter and load. A MATLAB/SIMULINK-based MPPT model is built to test the behavior of the proposed method. The proposed method is tested under different weather scenarios. Simulation results exhibit the successful tracking of the proposed method under all ambient conditions. Comparison of the tracking behavior of the proposed method with the perturb and observe method is also presented in the simulation results. In addition, a 220 W prototype with the help of dSPACE 1104 data acquisition system is built and tested under practical weather conditions on a sunny day as well as on a cloudy day. Experimental results are presented to verify the effectiveness of the proposed method. These results exhibit satisfactory performance under different practical weather conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

Noman

Addoweesh

Alolah

2017

International Journal of Photoenergy

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“…Opencircuit voltage and short-circuit current as input to the ANFIS MPPT controller are reported in [25], but this technique does not provide the true MPP because of the approximation employed. PV output current and voltage as input to the ANFIS are used in [26][27][28], whereas irradiation and temperature are used to train the ANFIS MPPT controller in [29,30]. However, the size of the training data used is relatively small that leads to a relatively high training error as reported in [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…PV output current and voltage as input to the ANFIS are used in [26][27][28], whereas irradiation and temperature are used to train the ANFIS MPPT controller in [29,30]. However, the size of the training data used is relatively small that leads to a relatively high training error as reported in [26,27]. In addition, no experimental verification has been reported.…”

Section: Introductionmentioning

confidence: 99%

An Efficient ANFIS-Based PI Controller for Maximum Power Point Tracking of PV Systems

2015

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In this paper, an efficient adaptive neuro-fuzzy inference system (ANFIS)-based PI controller for maximum power point tracking (MPPT) of photovoltaic (PV) systems is proposed. The proposed ANFIS-based MPPT controller has the capacity to track the optimum point under the rapidly changing irradiation conditions with less fluctuations in steady state. The training data of the proposed controller are extracted from a precise PV model developed. The performance of the proposed controller is compared with the conventional incremental conductance method. Finally, the proposed ANFIS-based MPPT controller has been implemented experimentally using real-time digital simulator (RTDS) to simulate a PV system in real time, while the proposed ANFIS-based controller is implemented on dSPACE 1104 controller. Simulation and experimental results show that the proposed ANFIS-based MPPT controller has fast and accurate dynamic response with less fluctuations in steady state. In addition, its performance is superior as compared to the conventional methods.Keywords Photovoltaic (PV) · Maximum power point tracking (MPPT) · Adaptive network-based fuzzy inference system (ANFIS) · Real-time digital simulator (RTDS) · dSPACE controller

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“…Both methods are well accepted for their simplicity in terms of hardware implementation. In addition, the sophisticated artificial intelligent (AI) methods, such as fuzzy logic control (FLC) [4], adaptive fuzzy logic control (AFLC) [5] and adaptive neural fuzzy inference system (ANFIS) [6] have been applied for the MPPT with better performances in terms of tracking speed and accuracy.…”

Section: Introductionmentioning

confidence: 99%

A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

Khaehintung

Kunakorn

Sirisuk

2010

Int. J. Control Autom. Syst.

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This paper presents a novel fuzzy logic control technique tuned by particle swarm optimization (PSO-FLC) for maximum power point tracking (MPPT) for a photovoltaic (PV) system. The proposed PV system composes of a current-mode boost converter (CMBC) with bifurcation control. An optimal slope compensation technique is used in the CMBC to keep the system adequately remote from the first bifurcation point in spite of nonlinear characteristics and instabilities of this converter. The proposed PSO technique allows easy and more accurate tuning of FLC compared with the trialand-error based tuning. Consequently, the proposed PSO-FLC method provides faster tracking of maximum power point (MPP) under varying light intensities and temperature conditions. The proposed MPPT technique is simple and particularly suitable for PV system equipped with CMBC. Experimental results are shown to confirm superiority of the proposed technique comparing with the conventional PVVC technique and the trial-and-error based tuning FLC.

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A novel ANFIS controller for maximum power point tracking in photovoltaic systems

Cited by 27 publications

References 3 publications

Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

Simulation and Practical Implementation of ANFIS-Based MPPT Method for PV Applications Using Isolated Ćuk Converter

An Efficient ANFIS-Based PI Controller for Maximum Power Point Tracking of PV Systems

A novel fuzzy logic control technique tuned by particle swarm optimization for maximum power point tracking for a photovoltaic system using a current-mode boost converter with bifurcation control

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