A Hybrid Isolated Bidirectional DC/DC Solid-State Transformer for DC Distribution Network

Wang, Yu; Yang, Ling; Wu, Qiong; Chen, Sizhe; Yu, Zhibin; Guan, Yuanpeng; Zhang, Yun

doi:10.1109/access.2021.3130650

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…The stability of synchronous generators depends on a number of factors, such as the configuration of the automatic voltage regulators [2]. Phase regression is performed to prevent torque from entering the generator field dynamics rotor angle and velocity variation [3]. Positive synchronous torque and negative torque often occur, causing a small internal positive torque and instability.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

Pradeep Kumar Tiwari

2024

jes

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Microgrids and HRES (Hybrid Renewable Energy Systems) are two related concepts that have gained considerable attention in recent years, particularly in the context of decentralized and sustainable energy systems. The integration of renewable energy sources in micro-grids is becoming increasingly important for meeting the electricity needs of rural areas. HRES contains a solar-air-biomass-bio-fuel-fuel cell and battery. Existing works involved sensitive analysis& utilized annual wind speed fluctuations, biofuel prices, energy costs, and net initial contribution costs. However, the instability of renewable materials such as sun and wind complicates the energy production process. In this paper, an optimal HRES configuration consisting of solar, wind, battery, and ultra-capacitor battery system is proposed. To optimize the HRES, a neural network-based control scheme is designed to balance the renewable energy output with peak load requirements, utilizing battery power storage. The proposed optimization strategy ensures the efficient operation of the HRES by minimizing energy losses, reducing carbon emissions, and improving the overall system reliability. The effectiveness of the proposed HRES and control scheme is demonstrated through simulation results. The proposed system contributes to the development of sustainable rural electrification solutions.

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Section: Introductionmentioning

confidence: 99%

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

Pradeep Kumar Tiwari

2024

jes

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A bidirectional isolated DC‐DC converter with low voltage stresses for energy storage systems integration

Khanbabaie,

Azari,

Nouri

et al. 2024

IET Power Electronics

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Herein, a bidirectional isolated DC‐DC converter with low voltage stress is introduced to utilise in energy storage frameworks. Two sets of coupled inductors (CI) and a transformer are utilized on the low‐voltage side to increase voltage gain. Through the series connection of the secondary windings of the CIs with the primary windings of the transformer, the concluded voltage gain is improved by the multiplication of the turns ratios of CI and transformer. As a result, the voltage gains as well as the voltage stresses in both directions can be flexibly improved through three degrees of freedom, which are the turns ratios of the CIs, transformer and duty cycle. Clamp capacitors accomplish active clamping, and the energy saved within the leakage inductance is recycled. Additionally, some switches are performed with soft‐switching and partially diminished switching losses. The steady‐state analysis and extensive performance comparison of the proposed converter prove that the proposed converter outperforms the similar previous converters in the state‐of‐the‐art. An 800 W 48 V/380 V prototype is developed to prove the performance of the proposed converter. The peak efficiency amounts of the proposed system in the high step‐down and high step‐up modes are 95.8% and 96.2%, respectively.

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Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Yusuf,

Hanzla,

Rahman

et al. 2024

IEEE Access

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Systems must be capable of detecting and tracking autonomous vehicles for intelligent management and control of transportation. Even though several methods are used to create intelligent systems for traffic monitoring, this article explains how to detect and track vehicles using pixel labeling and particle filter algorithms. We suggested a novel technique that segments the image using image segmentation to retrieve the foreground objects. We have divided our proposed model into the following steps: at first, geo-referencing is used to find the exact location; secondly, the images are denoised by using preprocessing; image segmentation is used to separate the background from the foreground; multiple objection detection is performed using the random forest to classify different objects; vehicles are detected through a method called template matching; after this, the vehicles are counted using histogram of oriented gradients (HOG); and after counting, the tracking of vehicles is obtained using particle filter; lastly, the trajectories are predicted by comparing the rectangular centroid of each car against the frame number and using it as a time stamp reference, the last match that the tracking algorithm obtained for each vehicle was recorded and used to estimate the trajectories. Our model outperforms current traffic monitoring approaches in terms of detection and tracking accuracy by 0.87 and 0.92, respectively using the Aerial Car dataset and 0.84 and 0.88, respectively, using the AU-AIR datasets. Vehicle recognition, traffic density detection, traffic flow analysis, and pedestrian route generation are all possible uses for the proposed system. It can transform traffic management and improve overall road safety due to its powerful algorithms and cutting-edge technologies. The system's adaptability makes it a significant asset in modern transportation, from optimizing signal timing to improving pedestrian navigation.INDEX TERMS geo-referencing, random forest, particle filter, vehicle detection, tracking, and trajectories.

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A Hybrid Isolated Bidirectional DC/DC Solid-State Transformer for DC Distribution Network

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 9 publications

References 34 publications

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

Neural Network Based Control Scheme for Micro-Grid Integrated HRES

A bidirectional isolated DC‐DC converter with low voltage stresses for energy storage systems integration

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

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