Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems

Valencia, Paula Andrea Ortiz; Ramos‐Paja, Carlos Andrés

doi:10.3390/en81112318

Cited by 38 publications

(23 citation statements)

References 35 publications

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“…DC-DC converters are widely deployed in industrial and domestic application due to their simple design and implementation with low cost; howeverm many DC-DC converter topologies are adopted to be used in Stand-alone or grid-connected PV systems, such as: buck, boost, buck-boost, sepic, cuk, etc. The only drawback of using a buck or buck-boost converter is the discontinuous input current, which causes undesired oscillation that will deteriorate the MPPT efficiency while boost, sepic, and cuk converters with continuous input current only have an input current ripples [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

et al. 2020

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In this article, an efficient and fast two-stage approach for controlling DC-DC boost converter using non linear sliding mode controller for a PV power plant is proposed. The control approach is based on two online methods instead of using the conventional combination of online and offline methods to harvest maximum energy and deliver an output PV voltage with reduced ripples. The proposed two-stage maximum power point tracking (MPPT) control can be integrated into many applications such as hybrid electric vehicles. Simulation results compared with the standard approaches P&O prove the tracking efficiency of the proposed method under fast changing atmospheric conditions of an average 99.87% and a reduced average ripple of 0.06. The two-stage MPPT control was implemented involving the embedded dSPACE DSP in comparison to the classical P&O to prove the efficiency and the validity of the control scheme. The experimental set-up system was carried out on boost converter and programmable DC electronic resistive load to highlights the robustness of the proposed controller against atmospheric changes and parametric variation.

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Section: Introductionmentioning

confidence: 99%

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

et al. 2020

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“…2016, 6, 218 2 of 12 the power line estimator for dynamic operation as used in solar tracking is the motivation of this research. There are other estimators in the literature such as those used in thermo-electric power generation [17], hybrid power generation systems [18,19], improved hybrid with fault studies [20][21][22], solar tracking [23], and wind operations [24,25]. This proposed estimator is slightly different, as the case where it depends on the load (dynamic) is formulated through a current equation that brings the best fit curve characteristics of the estimator.…”

Section: Power Mapping Of Double-stator Permanent Magnet Generatormentioning

confidence: 99%

A New Maximum Power Point Estimator Control Strategy to Maximize Output Power of the Double Stator Permanent Magnet Generator

et al. 2016

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Abstract:A new control estimator to maximize the power generated with a maximum power point estimator is introduced. The power mapping characteristics from the double-stator generator are modeled as a mathematical equation which is used to develop the estimator for maximum power tracking to maximize the generated power. The proposed estimator automatically traces the instantaneous maximum power at various load conditions. However, to stabilize the output voltage, a boost converter is used from the inverter side. The developed double-stator generator is tested with the new estimator for the maximizing power generation capability under laboratory conditions. The experimental results confirm that with the new estimator, the average power generation capability is increased by 12% and the peak value is increase by 22%.

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“…By neglecting the series and parallel resistances of the PV array, i.e., by setting Rs = 0 and Rsh = ∞, the PV current can be approximated by the expression [23]:…”

Section: Pv Cell Modelmentioning

confidence: 99%

“…The prevalent MPPT methods are the perturb-and-observe (P&O) method [11][12][13], incremental conductance (INC) method [14][15][16], fuzzy logic control (FLC) method [17][18][19], artificial neural network (ANN) method [20][21][22], sliding mode control (SMC) method [23][24][25], and the swarm-intelligence algorithm [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Chen

et al. 2016

Energies

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Photovoltaic (PV) systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP). Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL) non-linear controller combined with an artificial neural network (ANN) is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT) and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

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Sliding-Mode Controller for Maximum Power Point Tracking in Grid-Connected Photovoltaic Systems

Cited by 38 publications

References 35 publications

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

A Novel Two Stage Controller for a DC-DC Boost Converter to Harvest Maximum Energy from the PV Power Generation

A New Maximum Power Point Estimator Control Strategy to Maximize Output Power of the Double Stator Permanent Magnet Generator

Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

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