Comparative Study of Different DC/DC Power Converter for Optimal PV System Using MPPT (P&amp;O) Method

Ba, Abdellahi; Ehssein, Chighali; Mahmoud, Mouhamed El Mamy Ould Mouhamed; Hamdoun, Ouafae; Aroudam, Elhassen

doi:10.3103/s0003701x18040047

Cited by 52 publications

(14 citation statements)

References 13 publications

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“…Using the Mandami method of fuzzy minima implication, the inference engine applies the control rules to the fuzzified inputs, generating a single set of fuzzy outputs (µΔD) for each rule. Finally, the numerical value of the output variable, which corresponds to the size of the duty cycle variations (ΔD), is obtained using the CoG defuzzification method, shown in Equation (5).…”

Section: Design Of the Fuzzy Logic Controllersmentioning

confidence: 99%

“…Thus, the use of a maximum power point tracking (MPPT) technique to optimize the energy produced becomes essential [3,4]. The implementation of an MPPT algorithm needs a DC-DC power converter, which acts as an impedance adapter between the PV panel and the load, adapting the apparent impedance of the PV module to make it match the impedance in the MPP [5].…”

Section: Introductionmentioning

confidence: 99%

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Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries

Seguel

Seleme

2021

Energies

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This paper presents the design and implementation of a digital control strategy for a Buck converter, used as a solar charger of valve-regulated lead acid (VRLA) batteries. The control system consists of two fuzzy logic controllers (FLCs), which adjust the appropriate increment of the converter duty cycle based on battery state of charge according to a three-stage charging scheme. One FLC works as a maximum power point tracker (FLC-MPPT), while the other regulates the battery voltage (FLC-VR). This approach of using two different set of membership functions overcomes the limitations of the battery chargers with a single control function, where the voltage supplied to the battery is either not constant due to the operation of the MPPT algorithm (possibly damaging the battery) or is constant due to the operation of the voltage control (hence, MPP cannot be achieved). In this way, the proposed control approach has the advantage of extracting the maximum energy of the PV panel, preventing battery damage caused by variable MPPT voltage, thereby extending the battery’s lifetime. Moreover, it allows overcoming of the drawbacks of the conventional solar chargers, which become slow or inaccurate during abrupt changes in weather conditions. The strategy is developed to be implemented in a low-cost AT91SAM3X8E Arduino Due microcontroller. Simulations by MATLAB/Simulink and experimental results from hardware implementation are provided and discussed, which validate the reliability and robustness of the control strategy.

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Section: Design Of the Fuzzy Logic Controllersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries

Seguel

Seleme

2021

Energies

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“…In accordance with the efficiency-based aforesaid objectives in the introduction, maintaining the characteristic curves at a specific point that is known as the maximum power point (MPP) is required [42]. In this regard, maximum power point tracking (MPPT) concepts are employed at PV array in practice to harvest the maximum power from the PV module or array under various irradiance and temperature conditions [43]. Tracking performance of the MPPT algorithms has great importance, hence it has been the main subject of many studies and different methods are developed in consequence [44], [45], [46] .…”

Section: Pve and Mppt Algorithmmentioning

confidence: 99%

Symmetrical Pole Placement Method-Based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller With Harmonic Compensator for Single Phase Grid-Connected PV Inverters

Yanarateş

Zhou

2021

IEEE Access

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In this paper, a symmetrical pole placement Method-based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller is presented. The proposed PR-P controller resolved the issues that are tracking repeating control input signal with zero steady-state and mitigating of 3rd order harmonic component injected into the grid associated with the use of PI controller for single-phase PV systems. Additionally, the PR-P controller has overcome the drawbacks of frequency detuning in the grid and increase in the magnitude of odd number harmonics in the system that constitute the common concerns in the implementation of conventional PR controller developed as an alternative to PI controller. Moreover, the application of an unprecedented design process based on changing notch filter dynamics with symmetrical pole placement around resonant frequency overcomes the limitations that are essentially complexity and dependency on the precisely modelled system associated with the use of various controllers such as Adaptive, Predictive and Hysteresis in grid connected PV power generation systems. The proposed PR-P controller was validated employing Photovoltaic emulator (PVE) consisting of a DC-DC Buck power converter, a maximum power point tracking (MPPT) algorithm and a full-bridge grid connected inverter designed using MATLAB/Simulink system platform. Details of the proposed controller, Photovoltaic emulator (PVE) simulations, analysis and test results were presented in the paper.

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“…The DC-DC converter design is out of the scope of this paper, but the reader can refer to five papers [76][77][78][79][80] for details.…”

Section: Generalized Electric Vehicle Modelmentioning

confidence: 99%

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

2021

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The integration of photovoltaic systems (PVS) in electric vehicles (EV) increases the vehicle’s autonomy by providing an additional energy source other than the battery. However, current solar cell technology generates around 200 W for a 1.4 m2 panel (to be installed on the roof of the EV) at stable irradiance conditions. This limitation in production and the sudden changes in irradiance produced by shadows of clouds, buildings, and other structures make developing a fast and efficient maximum power point tracking (MPPT) technique in this area necessary. This article proposes an artificial neural network (ANN)-based MPPT, called DS-ANN, that uses manufacturer datasheet parameters as inputs to the network to address this problem. The Bayesian backpropagation-regularization performs the training, ensuring that the MPPT technique operates satisfactorily on different PVS without retraining. We simulated the response of 20 commercial modules against actual irradiance data to validate the proposed method. The results show that our method achieves an average tracking efficiency of 99.66%, improving by 1.21% over an enhanced P&O method.

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Comparative Study of Different DC/DC Power Converter for Optimal PV System Using MPPT (P&O) Method

Cited by 52 publications

References 13 publications

Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries

Robust Digital Control Strategy Based on Fuzzy Logic for a Solar Charger of VRLA Batteries

Symmetrical Pole Placement Method-Based Unity Proportional Gain Resonant and Gain Scheduled Proportional (PR-P) Controller With Harmonic Compensator for Single Phase Grid-Connected PV Inverters

System-Independent Irradiance Sensorless ANN-Based MPPT for Photovoltaic Systems in Electric Vehicles

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