This study presents the development and experimental validation of a novel wind tur-bine emulator (WTE) based on a Doubly-Fed Induction Generator (DFIG). The pro-posed architecture employs an induction motor (IM) driven by a variable frequency drive (VFD) to emulate wind turbine dynamics, offering a cost-effective and low-maintenance alternative to traditional DC motor-based systems. The control strategy, fully implemented in C language, integrates Tip Speed Ratio (TSR)-based Maximum Power Point Tracking (MPPT) with flux-oriented vector control of the DFIG. Experimental results confirm the emulator’s ability to accurately replicate real wind turbine behavior under varying wind conditions. The test bench demonstrates fast dynamic response, with rotor currents settling in 11–18 ms, and active/reactive powers stabilizing within 25–30 ms. Overshoots remain below 10%, and steady-state errors are limited to ±1 A for currents and ±100 W / ±50 VAR for powers, ensuring pre-cise power regulation. The speed tracking error is approximately 0.61 rad/s, validating the system’s ability to follow dynamic references with high accuracy. Additionally, ef-fective decoupling between active and reactive loops is achieved, with minimal cross-coupling during step changes.
The integration of Artificial Intelligence (AI) and Ambient Intelligence (AmI) has emerged as a promising approach to creating responsive and contextually aware environments. AmI creates contextually aware environments by seamlessly integrating intelligent technologies, while AI develops algorithms for autonomous learning and decision-making. However, embedding AI within AmI environments faces challenges due to limited resources and energy constraints. While recent research on embedded AI has primarily focused on specific tasks of AmI, our goal is to develop a comprehensive framework encompassing all the necessary components for practical use cases. Through this endeavor, we aim to explore power-aware designs and distributed learning as fundamental approaches to address limited computational resources, energy constraints, and dynamic context variations challenges.
Low-dimensional materials exhibiting strong excitonic effects are promising candidates for optoelectronic and energy-harvesting technologies. In this work, using many-body perturbation theory, we explore the excitonic properties of a buckled silver sulfide (Ag2S) single layer. Starting from density functional theory, we apply quasiparticle corrections via the single-shot G 0 W 0 approach and solve the Bethe-Salpeter equation to accurately capture excitonic effects in the optical absorption spectrum. Our calculations reveal a strongly bound bright exciton with a binding energy exceeding 800 meV, associated with a fundamental bandgap of 3.16 eV, and a majority of dark states in the spectrum. The excitonic wave functions are delocalized in real space, and while dark excitons exhibit similar spatial and momentum-space characteristics, bright excitons show significantly extended and distinct spatial distributions. Our results demonstrate the influence of the orbital projection of the band structure in the excitonic properties, and that buckled Ag2S is a compelling low-dimensional material for light-emitting technologies based on bright-exciton physics, as well as for quantum information storage using long-lived dark exciton states.
Abstract Data-driven modeling of complex systems, such as Li-ion batteries with explicit terms, has shown promising results. However, these methods typically work on a single dataset and are sensitive to noise. Here, we present a physics-informed and temperature-dependent data-driven model of Li-ion batteries by aggregating experimental data collected at various operating conditions using statistical bootstrapping techniques. The modeling process starts by creating a set of bootstrapped samples and including terms based on first principles and temperature-related terms in the model library. Then, multiple models were created using the bootstrapped data and aggregated into an ensemble with improved accuracy and robustness. The final model takes the median of the coefficients of the bootstrapped models and has temperature as a model input. We conducted several experiments to create the dataset for developing and validating the novel temperature-dependent battery model. The experiments included charging a Li-ion cell with a constant-current-constant-voltage algorithm and discharging it with custom and standard driving profiles at various temperatures from −10 °C to 40 °C. The baseline parsimonious model was developed at 25 °C and augmented using temperature-dependent terms to determine the sparse ensemble model, which achieved an error <2.5% on unseen test data.
