Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Bouarroudj, Noureddine; Boukhetala, Djamel; Feliu-Batlle, Vicente; Boudjema, Farès; Benlahbib, Boualam; Batoun, B.

doi:10.3390/en12142827

Cited by 24 publications

(17 citation statements)

References 49 publications

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“…Artificial neural networks (ANNs) [9,[18][19][20][21] focused methods were among the approaches followed to solve the problem. Being well-known machine learning algorithms, ANNs ability to model nonlinear functions enabled the accurate estimation of PV panel reference voltage corresponding to the maximum output power.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of PV System Based on Machine Learning

et al. 2020

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This project studies the conditions at which the maximum power point of a photovoltaic (PV) panel is obtained. It shows that the maximum power point is very sensitive to external disturbances such as temperature and irradiation. It introduces a novel method for maximizing the output power of a PV panel when connected to a DC/DC boost converter under variable load conditions. The main contribution of this work is to predict the optimum reference voltage of the PV panel at all-weather conditions using machine learning strategies and to use it as a reference for a Proportional-Integral-Derivative controller that ensures that the DC/DC boost converter provides a stable output voltage and maximum power under different weather conditions and loads. Evaluations of the proposed system, which uses an experimental photovoltaic dataset gathered from Spain, prove that it is robust against internal and external disturbances. They also show that the system performs better when using support vector machines as the machine learning strategy compared to the case when using general regression neural networks.

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Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking of PV System Based on Machine Learning

et al. 2020

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“…Many studies have researched the MPPT control issue of the PV system in recent years [3][4][5][6][7][8][9][10][11][12]. The MPPT methods for the PV system are summarized and compared in [13].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control

Liu

Lee

2020

Energies

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This paper proposes a maximum power point tracking (MPPT) and voltage regulation method based on model predictive control (MPC) for the two-stage grid-tied photovoltaic (PV) system, which can achieve MPPT and output voltage regulation of a PV system simultaneously. The MPPT algorithm based on MPC is implemented in a DC-DC boost converter. The reference voltage at maximum power point is obtained by dual step Incremental Conductance (I&C) algorithm under the rapidly varying illumination intensity, and the MPPT controller only needs to minimize one cost function of PV current, without pulse width modulation (PWM) module. To inject the generated PV power into the grid with high quality, this paper designs voltage regulation controller based on MPC to maintain the output voltage of the PV system at the desired value. The MPC controller outputs the optimal duty signal with the input and state constraints in the inner loop, and the PI controller in the outer loop is designed to improve the dynamic performance. The proposed method based on MPC was demonstrated using the SimPower systems tool in MATLAB/Simulink. Analysis and simulation results for the PV system show possible improvements on the closed-loop performance such as fast response and low overshoot.

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“…Generally, the current is a preferable choice as a regulating parameter because of the relative ease of control than that of voltage [6,7]. Previously, different types of modifications and embodiments of the MPPT system in the form of sequential search and by artificial control techniques are proposed and discussed [8][9][10][11][12][13][14][15][16][17][18][19]. The first group includes perturbation-observation algorithms (P&O), incremental conductance algorithms, sliding mode controllers, and many others [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…The first group includes perturbation-observation algorithms (P&O), incremental conductance algorithms, sliding mode controllers, and many others [9][10][11][12][13][14][15][16]. The second group contains techniques related to fuzzy logic regulators [17,18]. The maximum output power of a PV system is a function of different environmental conditions such as solar radiation, partial shading, and ambient temperature.…”

Section: Introductionmentioning

confidence: 99%

An Approval of MPPT Based on PV Cell’s Simplified Equivalent Circuit During Fast-Shading Conditions

et al. 2019

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The partial shading conditions significantly affect the functionality of solar power plants despite the presence of multiple maximum power point tracking systems. The primary cause of this problem is the presence of local maxima in the power–current and/or power–voltage characteristic curves that restrict the functionality of the conventional maximum power point tracking systems. The present article proposes a modified algorithm based on the simplified equivalent circuit of solar cells to improve the functionality of traditional maximum power point tracking systems. This algorithm provides a method for regularly monitoring the photo-current of each solar module. The upper and lower boundaries of the regulating parameter such as current or voltage are decided very precisely, which is helpful to find the location of the global maximum. During a sequential search, the control system accurately determines the lower and upper boundaries of the global maximum. Simultaneously, the maximum power point tracking system increases the photovoltaic current up to one of these boundaries and applies one of the conventional algorithms. Additionally, the control system regularly monitors the photovoltaic characteristics and changes the limits of regulating parameter concerning any change in global maximum location. This proposed method is fast and precise to locate the global maximum boundaries and to track global maximum even under fast-changing partial shading conditions. The improved performance and overall efficiency are validated by simulation study for variable solar irradiance.

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Maximum Power Point Tracker Based on Fuzzy Adaptive Radial Basis Function Neural Network for PV-System

Cited by 24 publications

References 49 publications

Maximum Power Point Tracking of PV System Based on Machine Learning

Maximum Power Point Tracking of PV System Based on Machine Learning

Maximum Power Point Tracking and Voltage Regulation of Two-Stage Grid-Tied PV System Based on Model Predictive Control

An Approval of MPPT Based on PV Cell’s Simplified Equivalent Circuit During Fast-Shading Conditions

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