We report a pulse width modulation (PWM) buck converter that is able to achieve a power conversion efficiency (PCE) of > 80% in light loads 100 μA) for implantable biomedical systems. In order to achieve a high PCE for the given light loads, the buck converter adaptively reconfigures the size of power PMOS and NMOS transistors and their gate drivers in accordance with load currents, while operating at a fixed frequency of 1 MHz. The buck converter employs the analog-digital hybrid control scheme for coarse/fine adjustment of power transistors. The coarse digital control generates an approximate duty cycle necessary for driving a given load and selects an appropriate width of power transistors to minimize redundant power dissipation. The fine analog control provides the final tuning of the duty cycle to compensate for the error from the coarse digital control. The mode switching between the analog and digital controls is accomplished by a mode arbiter which estimates the average of duty cycles for the given load condition from limit cycle oscillations (LCO) induced by coarse adjustment. The fabricated buck converter achieved a peak efficiency of 86.3% at 1.4 mA and > 80% efficiency for a wide range of load conditions from 45 μA to 4.1 mA, while generating 1 V output from 2.5-3.3 V supply. The converter occupies 0.375 mm(2) in 0.18 μm CMOS processes and requires two external components: 1.2 μF capacitor and 6.8 μH inductor.
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http://dx.doi.org/10.1109/TBCAS.2015.2501304 | DOI Listing |
Sci Rep
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.
This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller (AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory of control (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposed SC algorithm involve the features of finite-time convergence, unaffected by parameter variations, and chattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controller more robust and is utilized to estimate the undefined converter nonlinear dynamics without resorting to the usual linearization and simplifications of the converter model.
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January 2025
Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, India.
Health monitoring and analysis of photovoltaic (PV) systems are critical for optimizing energy efficiency, improving reliability, and extending the operational lifespan of PV power plants. Effective fault detection and monitoring are vital for ensuring the proper functioning and maintenance of these systems. PV power plants operating under fault conditions show significant deviations in current-voltage (I-V) characteristics compared to those under normal conditions.
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November 2024
Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt.
This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (P) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system.
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November 2024
Chemistry Division (Code 6100), US Naval Research Laboratory, Washington, DC 20375, USA.
Nickel-based catalysts are widely studied for water-gas shift (WGS), a key intermediate step in hydrogen production from carbon-based feedstocks. Their viability under practical conditions is limited at high temperatures when Ni aggregates and converts CO to methane, an undesirable side product. Because experimental and computational studies identify undercoordinated Ni step sites as most active toward CH formation, we eliminate Ni step sites by atomically dispersing Ni into networked, nanoparticulate CeO aerogels.
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November 2024
Department of Electrical and Electronic Engineering, Universidad de Los Andes, Bogotá, Colombia.
This paper presents a methodology for integrating Deep Reinforcement Learning (DRL) using a Deep-Q-Network (DQN) agent into real-time experiments to achieve the Global Maximum Power Point (GMPP) of Photovoltaic (PV) systems under various environmental conditions. Conventional methods, such as the Perturb and Observe (P&O) algorithm, often become stuck at the Local Maximum Power Point (LMPP) and fail to reach the GMPP under Partial Shading Conditions (PSC). The main contribution of this work is the experimental validation of the DQN agent's implementation in a synchronous DC-DC Buck converter (step-down converter) un-der both uniform and PSC conditions.
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