Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

Baraean, Abdullah; Kassas, Mahmoud; Alam, Md Shafiul; Abido, Mohamed A.

doi:10.1007/s13369-023-08179-9

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…[ [30] , [31] , [32] ] to enhance the dynamic behavior of converter systems. Additionally [ 33 ], applied a neural network and an adaptive terminal sliding mode controller (NN-ATSMC) to a DC-DC boost converter, ensuring error convergence and minimal chattering effect. The main drawback of these controllers lies in the fact that, when the disturbance decreases, they often generate excessive control input.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer

Baraean,

Kassas,

Alam

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer

Baraean,

Kassas,

Alam

et al. 2024

Heliyon

Self Cite

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“…Optimized MPPT techniques based on metaheuristic algorithms such as PSO [18] and an Adaptive Neuro Fuzzy Inference System (ANFIS) [19] for PV system control have also been introduced. Simulations and experimental outcomes have demonstrated extremely rapid MPPT for sudden changes in irradiance, with minimized steady-state oscillations.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Maximum Power Point Control for Single-stage Grid Connected PV Systems

Abbassi,

Aouiti,

Bacha

2024

Eng. Technol. Appl. Sci. Res.

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In this paper, a novel approach for implementing the maximum power point that could be generated from a photovoltaic (PV) panel while eliminating the need for current sensors through the application of the Hill Climbing algorithm is proposed. The active power generated by the PV panel is injected into the grid via a three-phase inverter using voltage-oriented voltage control with Spatial Vector Modulation (SVM). The developed strategy ensures minimal ripples for both active and reactive power and produces a sinusoidal alternating current waveform, even under varying lighting conditions. A comprehensive description of the adopted control strategy is provided and validated through numerical simulations conducted in MATLAB/Simulink environment. Furthermore, the performance of the proposed method is assessed by analyzing the simulation results. In an attempt to validate the effectiveness of the proposed approach, an implementation of the inverter control was conducted with the DSpace 1104 board, and the results underscored the feasibility and effectiveness of the employed approach for grid-connected PV systems.

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An Enhanced Incremental Conductance MPPT Approach for PV Power Optimization: A Simulation and Experimental Study

Chellakhi,

El Beid,

Abouelmahjoub

et al. 2024

Arab J Sci Eng

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Hybrid Neural Network and Adaptive Terminal Sliding Mode MPPT Controller for Partially Shaded Standalone PV Systems

Cited by 4 publications

References 40 publications

Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer

Physics-informed NN-based adaptive backstepping terminal sliding mode control of buck converter for PEM electrolyzer

Sensorless Maximum Power Point Control for Single-stage Grid Connected PV Systems

An Enhanced Incremental Conductance MPPT Approach for PV Power Optimization: A Simulation and Experimental Study

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