Fuzzy logic implementation for photovoltaic maximum power tracking

Mahmoud, Abeer E.; Mashaly, Hussein M.; Kandil, S.A.; Khashab, Hisham El; Nashed, Maged N. F.

doi:10.1109/iecon.2000.973240

Cited by 11 publications

(13 citation statements)

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“…In this case, five fuzzy levels are used: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). In [54] and [55], seven fuzzy levels are used, probably for more accuracy. In Fig.…”

Section: E Fuzzy Logic Controlmentioning

confidence: 99%

Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Esram

Chapman

2007

IEEE Trans. On Energy Conversion

4,465

1,742

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Section: E Fuzzy Logic Controlmentioning

confidence: 99%

Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Esram

Chapman

2007

IEEE Trans. On Energy Conversion

4,465

1,742

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“…Most of the literature reports on MPPT algorithms aim at improving the steady‐state performance, the transient response , and the cost of implementation . Closed‐loop algorithms, aside from P&O, include incremental conductance , fuzzy logic , neural networks , load maximization , or ripple correlation current (RCC) . In RCC, the ripple current (or voltage) of the PV array that naturally occurs upon switching of the buck–boost converter is recorded to determine the array conductance .…”

Section: Maximum Power Point Tracking Algorithmsmentioning

confidence: 99%

Hill climbing hysteresis of perovskite‐based solar cells: a maximum power point tracking investigation

Pellet

Giordano

Dar

et al. 2017

Progress in Photovoltaics

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The surge of the power conversion efficiency of metal halide lead perovskite solar cells comes with concerns, such as the long‐term ecotoxicity of lead compounds, their sensitivity toward moisture and oxygen, or the scarcity of some of their components. Most perovskite solar cells still suffer from serious stability problems when measured under real working conditions (maximum power point tracking at 60°C). In the long run, stability will certainly decide on the fate of CH3NH3PbI3 and related lead perovskites for their use in photovoltaic modules. Herein, we show an effective and inexpensive strategy to perform ageing of perovskite solar cells under maximum power point tracking. For the first time, we analyze the issue of power extraction from solar cells exhibiting hysteresis. We show that a standard tracking algorithm such as perturb and observe fails to converge to the maximum power point of the solar cell if it exhibits j(V) hysteresis, and we present an effective strategy to stabilize the algorithm. We show that enforcing oscillations in forward bias can boost the mean power output of some perovskite solar cells by more than 10%, in contrast to a reference crystalline silicon solar cell. Copyright © 2017 John Wiley & Sons, Ltd.

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“…There are several MPP tracking methods in the literature, such as fuzzy logic control, neural network control, pilot cells and digital signal processor based implementation. Nevertheless, Perturb and Observe (P and O) and Incremental Conductance (INC) algorithms are most widely used, especially for low-cost implementations (Femia et al, 2005;Mahmoud et al, 2000;Marouani and Bacha, 2009). As shown in the Fig.…”

Section: State-space Averaging Model For Boost Convertermentioning

confidence: 99%

“…The switching states are defined as the duration of switch being turned on and turned off, namely the duty-cycle. The solution of the duty-cycle representation in state-space technique is obtained by summing up the terms for each switching mode (Mahmoud et al, 2000). For example, let K for during switch to be turned on and (1-K) for the switch turn off.…”

Section: State-space Averaging Model For Boost Convertermentioning

confidence: 99%

Modeling and Control of a Photovoltaic Energy System Using the State-Space Averaging Technique

Jamri¹

2010

American Journal of Applied Sciences

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Problem statement:This study presented the modeling and control of a stand-alone Photovoltaic (PV) system using the state-space averaging technique. Approach: The PV module was modeled based on the parameters obtained from a commercial PV data sheet while state-space method is used to model the power converter. A DC-DC boost converter was chosen to step up the input DC voltage of the PV module while the DC-AC single-phase full-bridge square-wave inverter was chosen to convert the input DC comes from boost converter into AC element. The integrated state-space model was simulated under a constant and a variable change of solar irradiance and temperature. In addition to that, maximum power point tracking method was also included in the model to ensure that optimum use of PV module is made. A circuitry simulation was performed under the similar test conditions in order to validate the state-space model. Results: Results showed that the state-space averaging model yields the similar performance as produced by the circuitry simulation in terms of the voltage, current and power generated. Conclusion/Recommendations: The state-space averaging technique is simple to be implemented in modeling and control of either simple or complex system, which yields the similar performance as the results from circuitry method.

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Fuzzy logic implementation for photovoltaic maximum power tracking

Cited by 11 publications

References 1 publication

Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques

Hill climbing hysteresis of perovskite‐based solar cells: a maximum power point tracking investigation

Modeling and Control of a Photovoltaic Energy System Using the State-Space Averaging Technique

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