Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

Subiyanto, Subiyanto; Mohamed, Azah; Shareef, Hussain

doi:10.1155/2012/798361

Cited by 25 publications

(7 citation statements)

References 23 publications

(40 reference statements)

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“…The most common direct MPPT methods are the P&O algorithm [4] and the Incremental Conductance method [5]. Other techniques as fuzzy control [6,7] and neural networks are also being used to develop MPPT controllers. This is because of the nonlinear relationships involved in photovoltaic systems.…”

Section: Introductionmentioning

confidence: 99%

MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation

Leyva

Olalla

Zazo

et al. 2012

International Journal of Photoenergy

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The paper analyses extremum-seeking control technique for maximum power point tracking circuits in PV systems. Specifically, the paper describes and analyses the sinusoidal extremum-seeking control considering stability issues by means a Lyapunov function. Based on this technique, a new architecture of MPPT for PV generation is proposed. In order to assess the proposed solution, the paper provides some experimental measurements in a 100 W prototype which corroborate the effectiveness of the approach.

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Section: Introductionmentioning

confidence: 99%

MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation

Leyva

Olalla

Zazo

et al. 2012

International Journal of Photoenergy

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“…However, the accuracy performance is unmatched with ANNs. The accuracy can be improved by incorporating the FLC with other techniques, such as in [31], Hopfield neural network [32], T-S fuzzy [33], or genetic algorithm fuzzy logic control [34]. However, these type of hybrid methods will result in greater complexity algorithms.…”

Section: Algorithm Complexitymentioning

confidence: 99%

Perturbative methods for maximum power point tracking (MPPT) of photovoltaic (PV) systems: a review

Tajuddin

Arif

Ayob

et al. 2015

Int. J. Energy Res.

102

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Summary Over the past few decades, the world demand for energy has risen steadily, forcing the world communities to look for alternative sources. Photovoltaic (PV) is seen as the most promising solution for this demand. However, the PV system is popularly known to suffer from low‐energy harvesting due to the change of environment conditions. An inexpensive and practical solution to extract the energy from the PV is by improving the maximum power point tracking (MPPT) controller technique. An ideal MPPT should be able to track the true maximum power operating point accurately under all circumstances and overcome all nonlinearities in the characteristic I‐V curves. This paper presents an updated review of the techniques based on the perturbative MPPT methods, both using the conventional and soft computing methods. The working principles of the techniques, parameter effects, and their limitations are discussed. The focus of this review is to direct the readers to the new direction of MPPT using the artificial intelligence and evolutionary computation techniques. Besides serving as a comprehensive source of information, the paper also provides a critical review on the relative performance of the selected MPPT methods. This includes the module dependency, tracking performance, and the ability to handle the partial shading conditions. Copyright © 2015 John Wiley & Sons, Ltd.

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“…With this arrangement, active power and the DC voltage is controlled using a DC-DC converter while the reactive power and power factor is controlled using DC-AC converter. The DC-DC converter is usually utilised for integrating of maximum power point tracking (MPPT) algorithm to extract maximum power from PV arrays [8]. For grid connected applications, boost type of DC-DC converter is preferred where it step up the arrays voltage to a higher voltage [9].…”

Section: Figure 2 Photovoltaic Generation System Typementioning

confidence: 99%

Influence of an Advance Control of Photovoltaic Generation System on Fault Ride Through Performance

Wanik¹,

Yih

Ayob³

et al. 2013

Power and Energy Systems

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This paper presents a comparison study on photovoltaic generation system (PVGS) performance between two different types of power conversion topologies in riding through the grid faults via digital simulation. In the first topology, PV panels are connected directly to a DC-AC converter. In the second topology, DC-DC converter is employed between PV panels and the DC-AC converter. The second topology added complexity of control but it overs more advantages. Both topologies are subjected to grid faults; which result in voltage sags at the point of interconnection. Active power, reactive power and total current from both PVGS topologies are analysed and compared. The simulation results show that PVGS with DC-DC converter performs better where it return to steady state operation faster after clearance of faults and also the deviation of output from steady state value is less during the recovering period.

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Hopfield Neural Network Optimized Fuzzy Logic Controller for Maximum Power Point Tracking in a Photovoltaic System

Cited by 25 publications

References 23 publications

MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation

MPPT Based on Sinusoidal Extremum-Seeking Control in PV Generation

Perturbative methods for maximum power point tracking (MPPT) of photovoltaic (PV) systems: a review

Influence of an Advance Control of Photovoltaic Generation System on Fault Ride Through Performance

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