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Improving photovoltaic water pumping system performance with ANN-based direct torque control using real-time simulation
Abstract Photovoltaic Water Pumping Systems (PVWPS) have become increasingly important as a renewable energy solution in rural areas, providing energy independence, cost savings, and environmental friendliness. This system has two main controllers. The first controller is employed to maximize power extraction from the PV array by controlling the duty ratio of the DC-DC boost converter. The second controller is responsible for regulating the operation of the induction motor through the switching pulses of the Voltage Source Inverter (VSI). These two controllers play an essential role in the system, which increases efficiency and performance. Therefore, the innovative aspect of this work consists of introducing Artificial Neural Networks (ANNs) based on each PVWPS controller. On the one hand, ANN-based MPPT is implemented to ensure optimal performance of the PV array under varying irradiation levels. On the other hand, to overcome the defects and problems caused by Direct Torque Control (DTC), such as flux and torque ripples, high switching frequency, and challenges at low speeds, an ANN-based DTC is proposed in which each of the hysteresis comparators, switching table, and speed controller in the DTC are replaced by ANN controllers. The PVWPS based on the proposed controls is thoroughly modeled and simulated using MATLAB/Simulink software and validated using dSPACE DS1104 Board. The results demonstrate significant improvements, including a 75.51% reduction in flux ripples, a 77.5% reduction in torque ripples, a 44.79% improvement in response time, and an increase in the water quantity. Furthermore, the Real-Time simulation and visualization obtained are consistent with the simulation outcomes.
Beta-hydroxy-beta-methylbutyrate (HMB) improves daily activity and whole-body protein metabolism in Duchenne muscular dystrophy dogs: a pilot study
Life-cycle assessment of oil recovery using dimethyl ether produced from green hydrogen and captured CO2
Quantum secure patient login credential system using blockchain for electronic health record sharing framework
A theoretical prediction for generating isolated attosecond pulse in water window utilizing instantaneous frequency change of two-color driving laser pulse
Elucidating ligand interactions and small-molecule activation in the pyrrolnitrin biosynthetic enzyme PrnB
VO2 based polarization-independent dual-wavelength plasmonic switches using U and C shaped nanostructures
PTEN loss in glioma cell lines leads to increased extracellular vesicle biogenesis and PD-L1 cargo in a PI3K-dependent manner
Temporal variations of metals and trace elements in tuna spines from the canary islands from 1990s to 2000s
Mutations in histones dysregulate copper homeostasis leading to defect in Sec61-dependent protein translocation mechanism in Saccharomyces cerevisiae
Vaccines for cancer prevention: exploring opportunities and navigating challenges
High-Precision computational solutions for nonlinear evolution models in graphene sheets
Abstract This study investigates the analytical solutions of a nonlinear evolution model governing the dynamics of graphene sheets, a material renowned for its exceptional electronic properties and versatile applications in nanotechnology. Three advanced analytical approaches-the Khater II (Khat II) method, the Khater III (Khat III) method, and the Generalized Rational (GRat) approach-are employed to derive exact solutions for this model with high precision. The accuracy and reliability of these solutions are validated by comparing them to numerical results obtained via He’s Variational Iteration (HVI) method, which serves as a benchmark for numerical verification. The analysis reveals a remarkable agreement between the analytical and numerical solutions, highlighting the robustness and effectiveness of the proposed methodologies. Furthermore, this study provides new insights into the nonlinear dynamics and physical properties of graphene sheets, while also identifying connections to other prominent nonlinear evolution equations. The innovative use of these analytical techniques offers practical frameworks for addressing complex nonlinear models in mathematical physics, thus advancing solution methodologies for such equations. This research contributes significantly to applied mathematics, material science, and nanotechnology by delivering accurate solutions and enhancing our understanding of graphene’s nonlinear behavior. Finally, the findings have far-reaching implications, offering potential applications in designing advanced materials with tailored properties to support technological advancements, thereby pushing the boundaries of nanotechnology and materials engineering.