List of Articles Dynamic Model

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  1 - Integrated Modeling of Bidirectional Solid-State Transformers: Rectifier, DC to DC Converter and Inverter Stages
  hamed molla-ahmadian morteza shafiei javid khorasani
  : One of the new and growing equipment in modern power networks is solid state or power electronic transformer. These types of transformers are based on power semiconductor switches and high frequency transformers. Compared to traditional transformers, it has several ca More

  : One of the new and growing equipment in modern power networks is solid state or power electronic transformer. These types of transformers are based on power semiconductor switches and high frequency transformers. Compared to traditional transformers, it has several capabilities such as the ability to operate with input voltage variations in amplitude and frequency, automatic regulation of output voltage and input power factor correction. The investigated transformer has the ability to transfer power in both directions and has three stages, including the rectifier, the middle stage and the inverter stage. This transformer has a large number of semiconductor switches and its modeling, analysis, design and simulation is difficult and complex. In particular, real-time simulation of these transformers with conventional models is not possible. In these cases, the use of averaging theory seems to be the appropriate solution. In this paper, the averaging theory is applied to a solid-state transformer and its modeling is done in a simple and powerful way with the ability to study real-time, transient and steady states performance. The proposed modeling includes differential equations and equivalent circuits and offers an integrated transformer model with the ability to study the interaction between stages as a part of power system. The presented models are used in simulation of smart grids, DC microgrids and connection of distributed generation sources to the grid, as well as analysis and design of solid-state transformer behavior in areas such as renewable energies and electrical transportation. In addition to the proposed modeling, the closed-loop control structure has been implemented for all three stages. Transformer simulation is performed by implementing differential equations in SIMULINK/MATLAB software and verified the proposed model. Manuscript profile
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  2 - Comparative Study of 5G Signal Attenuation Estimation Models
  Md Anoarul Islam Manabendra Maiti Judhajit Sanyal Quazi Md Alfred
  10.52547/jist.32544.11.42.84
  Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent y More

  Wireless networks functioning on 4G and 5G technology offer a plethora of options to users in terms of connectivity and multimedia content. However, such networks are prone to severe signal attenuation and noise in a number of scenarios. Significant research in recent years has consequently focused on establishment of robust and accurate attenuation models to estimate channel noise and subsequent signal loss. The identified challenge therefore is to identify or develop accurate computationally inexpensive models implementable on available hardware for generation of estimates with low error and validate the solutions experimentally. The present work surveys some of the most relevant recent work in this domain, with added emphasis on rain attenuation models and machine learning based approaches, and offers a perspective on the establishment of a suitable dynamic signal attenuation model for high-speed wireless communication in outdoor as well as indoor environments, presenting the performance evaluation of an autoregression-based machine learning model. Multiple versions of the model are compared on the basis of root mean square error (RMSE) for different orders of regression polynomials to find the best-fit solution. The accuracy of the technique proposed in the paper is then compared in terms of RMSE to corresponding moderate and high complexity machine learning techniques implementing adaptive spline regression and artificial neural networks respectively. The proposed method is found to be quite accurate with low complexity, allowing the method to be practically applicable in multiple scenarios. Manuscript profile