DC microgrids are gaining more and more popularity and are becoming a more viable alternative to AC microgrids (MGs) due to their advantages in terms of simpler power converter stages, flexible control algorithms and the absence of synchronization and reactive power. However, DC-MGs are prone to instability issues associated with the presence of nonlinear loads such as constant power loads (CPL) known by their incremental negative impedance (INI), which may lead to voltage collapse of the main DC Bus. In this paper, H-based controller of a source side buck converter is designed to avoid the instability issues caused by the load-side converter acting as a CPL. Besides, the proposed controller allows a perfect rejection of all perturbations that may arise from parameter variations, input voltage and CPL current fluctuations. The design process of H∞-based controller is based on the Golver Doyle Optimization Algorithm (GDOA), which requires an augmented system extracted from the small-signal model of the DC/DC converter including the mathematical model of parameter variations and overall external perturbations. The H based controller involves the use of weight functions in order to get the desired performances. The proposed controller is easy to implement and lead to reducing the implementation cost and avoid the use of current measurement that may have some disadvantages. The derived controller is validated by simulation performed in Psim software and experimental setup.
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http://dx.doi.org/10.1016/j.isatra.2020.05.031 | DOI Listing |
Mar Pollut Bull
March 2025
Sanya Research Institute of Hunan University of Science and Technology, Sanya 572000, China. Electronic address:
The increasing demand for sustainable offshore energy solutions necessitates efficient power conversion technologies that minimize environmental impact while ensuring reliable energy delivery. The DC-DC buck converter plays a crucial role in marine renewable energy systems, optimizing power conversion for offshore wind, wave, and floating solar applications. However, selecting the most efficient and sustainable converter requires balancing efficiency, reliability, cost, thermal performance, and size under harsh marine conditions.
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March 2025
School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address:
Two non-inverting buck-boost converters, one for each solar panel of the X and Y axes of CubeSat have been used to condition the power with a common output capacitor on DC bus. The power generated from each axis of the solar panel is added to the DC-bus along with the power generated by the less illuminated solar panels. For the proposed model, Synergetic control, Sliding Mode Control, and Super Twisting Sliding Mode Control algorithms have been implemented for the MPPT, and their results are compared with each other.
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March 2025
Department of Electrical Engineering, College of Engineering, King Khalid University, KSA, P.O. Box 394, 61421, Abha, Saudi Arabia.
To address the challenges of global warming and the greenhouse effect, extensive research has been dedicated to microgrids (MGs) powered by renewable energy sources (RESs). This paper presents an innovative control mechanism, the synergetic simplified super-twisting algorithm (SSSTA), designed specifically for a DC-MG incorporating a battery energy storage system (BESS), a solar photovoltaic (PV) unit, and DC loads. The PV system connects to a shared DC bus via a unidirectional DC-DC boost converter, optimized for maximum power point tracking from the PV generator.
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February 2025
Department of Computing and Artificial Intelligence, Suzhou City University, Suzhou, 215104, China.
In this paper, a Total Loss Minimization (TLM) modulation strategy is proposed to enhance the conversion performance of the series-resonant dual-active-bridge DC-DC converters. Unlike conventional modulations, this optimal approach targets the reduction of reactive power and circulating current, which are recognized as major contributors to conduction loss. Additionally, it addresses the switching loss, which typically results from hard-switching operations.
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January 2025
School of ECE, Adama Science and Technology University, Adama, Ethiopia.
This paper details the hardware implementation of a Universal Converter controlled by an Artificial Neural Network (ANN), utilizing key components such as six Insulated Gate Bipolar Transistors (IGBTs), two inductors, and two capacitors for energy storage and voltage smoothing. A Digital Signal Processor (DSP) serves as the core controller, processing real-time input and feedback signals, including voltage and current measurements, to dynamically manage five operational modes: rectifier buck, inverter boost, DC-DC buck, DC-DC boost, and AC voltage control. The pre-trained ANN algorithm generates pulse-width modulation (PWM) signals to control the switching of the IGBTs, optimizing timing and duty cycles for efficient operation.
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