Fixed-frequency phase-shift modulated PV-MPPT for LLC resonant converters

Abdel‐Rahim, Omar; Alamir, Nehmedo; Orabi, Mohamed; Ismeil, Mohamed A.

doi:10.1007/s43236-019-00001-w

Cited by 16 publications

(3 citation statements)

References 27 publications

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“…4. The PV cell module, Boost voltage-boosting module, MPPT module, and pulse width modulation (PWM) module are the components of the model [16][17]. To adjust the duty cycle of the Boost converter circuit, the model's control circuitry modulates the MPPT output signal with a triangle carrier wave.…”

Section: Model Establishmentmentioning

confidence: 99%

Research on maximum power point tracking of PV systems based on perturbation and observation method

Zhan,

Zhou,

Zhu

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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Section: Model Establishmentmentioning

confidence: 99%

Research on maximum power point tracking of PV systems based on perturbation and observation method

Zhan,

Zhou,

Zhu

et al. 2023

International Conference on Electronic Materials and Information Engineering (EMIE 2023)

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“…Model Predictive Control based maximum power point tracking(MPC-MPPT) is a sophisticated MPPT technique that has gained popularity in recent years due to its ability to achieve high tracking accuracy. This technique is particularly effective in controlling the operating point of photovoltaic (PV) panels to track the Maximum Power Point (MPP) [24]. The basic principle behind MPC-MPPT is to predict the future behavior of the PV panel and then use this information to control the operating point of the panel.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Photovoltaic Conversion Efficiency With Model Predictive Control-Based Sensor-Reduced Maximum Power Point Tracking in Modified SEPIC Converters

Abdel-Rahim,

Alghaythi,

Alshammari

et al. 2023

IEEE Access

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The objective of this paper is to propose a new technique for maximum power point tracking (MPPT) in photovoltaic (PV) systems that utilizes fewer sensors, thereby reducing the hardware cost. The technique aims to achieve efficient MPPT under various environmental conditions by employing a modified SEPIC converter and a model predictive control (MPC)-based MPPT algorithm. To achieve the objective, the proposed technique utilizes only one voltage sensor and one current sensor, significantly reducing the hardware requirements compared to traditional MPPT techniques. The modified SEPIC converter is employed to regulate the voltage and current levels in the PV system. The MPC-based MPPT algorithm is implemented to dynamically adjust the operation of the converter and track the maximum power point. The algorithm incorporates a model predictive control approach, which utilizes a predictive model of the PV system to anticipate and optimize the power output. The algorithm predicts the behavior of the PV system based on the available sensor measurements, allowing for accurate MPPT. The algorithm operates in realtime, providing instantaneous adjustments to maximize power extraction. The study demonstrates that the proposed technique effectively tracks the maximum power point of the PV system using only one voltage sensor and one current sensor, thus reducing the overall hardware cost. The MPC-based MPPT algorithm, in combination with the modified SEPIC converter, achieves efficient power extraction under various operating conditions. The simulation and experimental results indicate that the proposed technique outperforms traditional MPPT techniques in terms of cost-effectiveness and power extraction efficiency.INDEX TERMS model predictive control (MPC)-based MPPT algorithm, modified SEPIC converter, MATLAB simulation and hardware, voltage sensor and current sensor.

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“…Many MPPT methods have been developed and utilized [21][22][23][24][25] during the last two decades. Several studies evaluated the performance of such methods [26][27][28][29][30] based on both uniform solar irradiance and partially shaded conditions.…”

Section: Introductionmentioning

confidence: 99%

A Fast and Accurate Maximum Power Point Tracking Approach Based on Neural Network Assisted Fractional Open-Circuit Voltage

Alzahrani

2020

Electronics

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This paper presents an enhanced maximum power point tracking approach to extract power from photovoltaic panels. The proposed method uses an artificial neural network technique to improve the fractional open-circuit voltage method by learning the correlation between the open-circuit voltage, temperature, and irradiance. The proposed method considers temperature variation and can eliminate the steady-state oscillation that comes with conventional algorithms, which improves the overall efficiency of the photovoltaic system. A comparison with the traditional and most widely used algorithms is discussed and shows the difference in performance. The presented algorithm is implemented with a Ćuk converter and tested under various weather and irradiance conditions. The results validate the competitiveness of the algorithm against other algorithms.

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Fixed-frequency phase-shift modulated PV-MPPT for LLC resonant converters

Cited by 16 publications

References 27 publications

Research on maximum power point tracking of PV systems based on perturbation and observation method

Research on maximum power point tracking of PV systems based on perturbation and observation method

Enhancing Photovoltaic Conversion Efficiency With Model Predictive Control-Based Sensor-Reduced Maximum Power Point Tracking in Modified SEPIC Converters

A Fast and Accurate Maximum Power Point Tracking Approach Based on Neural Network Assisted Fractional Open-Circuit Voltage

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