Adjustable variable step-based MRAC MPPT for solar PV system in highly fluctuating and cloudy atmospheric conditions

Singh, Deepak Kumar; Akella, Ashok Kumar; Manna, Saibal

doi:10.1007/s00202-023-01922-3

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Grid‐connected systems must perform two critical tasks in order to reach this objective: 1) Guarantee that the solar PV system is functioning at MPP utilizing MPPT technique and 2) successfully interject grid current, i.e., free of harmonic distortion. [ 30,31 ]…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

Singh,

Akella,

Manna

2023

Energy Tech

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The solar photovoltaic (PV) energy generation technology has grown rapidly. Herein, a novel continuous‐time Lyapunov‐based model reference adaptive control (LB‐MRAC) technique is suggested for determining the duty cycle of boost converter to maintain maximum power point (MPP) of the solar PV panel. For ensuring the fast‐tracking speed, the reference voltage of, respectively, MPP is calculated using perturb and observe (PO) and input to the MRAC technique. Using MATLAB/Simulink, the performances of the LB‐MRAC maximum power point tracking are comparatively analyzed with PO, incremental conductance, and adaptive neuro‐fuzzy inference system under diverse testing modes such as stand‐alone, partial shading, grid integrated, and real time. The proposed technique tracks MPP in just 3.7 ms having tracking efficacy between 99.15% and 99.59% under highly fluctuating irradiance and temperature with load uncertainties. In partial shading mode, four patterns (3 × 1 PV string) are devised and determined where that the suggested approach monitors the global MPP in merely 0.04 s with the highest power tracked and the least amount of shading loss. Next, the efficacy is checked in a three‐phase 50 kW grid‐integrated mode under realistic weather. Finally, the performance of the suggested continuous‐time LB‐MRAC technique is experimentally corroborated employing OPAL‐RT(OP4510) in the real‐time mode.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

Singh,

Akella,

Manna

2023

Energy Tech

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Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

Dkhichi

2023

ELECTRENG

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<abstract> A novel approach defined by the artificial neural network (ANN) model trained by the improved Gauss-Newton in conjunction with a simulated annealing technique is used to control a step-up converter. To elucidate the superiority of this innovative method and to show its high precision and speed in achieving the right value of the Maximum Power Point (MPP), a set of three comparative Maximum Power Point Tracker (MPPT) methods (Perturbation and observation, ANN and ANN associated with perturbation and observation) are exanimated judiciously. The behavior of these methods is observed and tested for a fixed temperature and irradiance. As a result, the proposed approach quickly tracks the right MPP = 18.59 W in just 0.04382 s. On the other hand, the outstanding ability of the suggested method is demonstrated by varying the irradiance values (200 W/m2, 300 W/m2, 700 W/m2, 1000 W/m2, 800 W/m2 and 400 W/m2) and by varying the temperature values (15℃, 35℃, 45℃ and 5℃). Therefore, the ANN trained by Gauss-Newton in conjunction with simulated annealing shows a high robustness and achieves the correct value of MPP for each value of irradiance with an efficiency 99.54% and for each value of temperature with an efficiency 99.98%; the three other methods sometimes struggle to achieve the right MPP for certain irradiance values and often remains stuck in its surroundings. </abstract>

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Adjustable variable step-based MRAC MPPT for solar PV system in highly fluctuating and cloudy atmospheric conditions

Cited by 2 publications

References 30 publications

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

Improved MPPT algorithm: Artificial neural network trained by an enhanced Gauss-Newton method

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