power control
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2022 ◽  
Vol 8 ◽  
pp. 710-717
Jicheng Fang ◽  
Qingshan Xu ◽  
Yuanxing Xia ◽  
Lele Fang

2022 ◽  
Vol 8 ◽  
pp. 338-344
Achara Pichetjamroen ◽  
Phacharawat Chindamanee ◽  
Nithiphat Teerakawanich ◽  
Natthawuth Somakettarin

2022 ◽  
Vol 169 ◽  
pp. 108931
Jiaoshen Xu ◽  
Hui Tang ◽  
Xin Wang ◽  
Ge Qin ◽  
Xin Jin ◽  

Hayder M. Amer ◽  
Ethar Abduljabbar Hadi ◽  
Lamyaa Ghaleb Shihab ◽  
Hawraa H. Al Mohammed ◽  
Mohammed J. Khami

Technology such as vehicular ad hoc networks can be used to enhance the convenience and safety of passenger and drivers. The vehicular ad hoc networks safety applications suffer from performance degradation due to channel congestion in high-density situations. In order to improve vehicular ad hoc networks reliability, performance, and safety, wireless channel congestion should be examined. Features of vehicular networks such as high transmission frequency, fast topology change, high mobility, high disconnection make the congestion control is a challenging task. In this paper, a new congestion control approach is proposed based on the concept of hybrid power control and contention window to ensure a reliable and safe communications architecture within the internet of vehicles network. The proposed approach performance is investigated using an urban scenario. Simulation results show that the network performance has been enhanced by using the hybrid developed strategy in terms of received messages, delay time, messages loss, data collision and congestion ratio.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 572
Chan Roh ◽  
Kyong-Hwan Kim

This study uses deep learning algorithms to predict the rotational speed of the turbine generator in an oscillating water column-type wave energy converter (OWC-WEC). The effective control and operation of OWC-WECs remain a challenge due to the variation in the input wave energy and the significantly high peak-to-average power ratio. Therefore, the rated power control of OWC-WECs is essential for increasing the operating time and power output. The existing rated power control method is based on the instantaneous rotational speed of the turbine generator. However, due to physical limitations, such as the valve operating time, a more refined rated power control method is required. Therefore, we propose a method that applies a deep learning algorithm. Our method predicts the instantaneous rotational speed of the turbine generator and the rated power control is performed based on the prediction. This enables precise control through the operation of the high-speed safety valve before the energy input exceeds the rated value. The prediction performances for various algorithms, such as a multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and convolutional neural network (CNN), are compared. In addition, the prediction performance of each algorithm as a function of the input datasets is investigated using various error evaluation methods. For the training datasets, the operation data from an OWC-WEC west of Jeju in South Korea is used. The analysis demonstrates that LSTM exhibits the most accurate prediction of the instantaneous rotational speed of a turbine generator and CNN has visible advantages when the data correlation is low.

João Paulo Assunção de Souza ◽  
Leonardo Henrique de Melo Leite ◽  
Lucas de Godoi Teixeira ◽  
Wallace do Couto Boaventura ◽  
Danilo Derick Silva Alves ◽  

2022 ◽  
Jamal AMADID ◽  
Abdelfettah Belhabib ◽  
Mohamed Boulouird ◽  
Moha M’Rabet Hassan ◽  
Abdelouhab Zeroual

Abstract Some more practical channels that model the networks in a real environment is the multi-path communication channels. In order to investigate these communications channels. This work addressed Channel Estimation (CE) in the Uplink (UL) phase for a multi-cell multi-user massive multipleinput multiple-output (M-MIMO) system that studies multi-path communication between each user and its serving Base Station (BS). We suppose that the network operates under Time-Division Duplex (TDD) protocol. We studied and analyzed the multi-path channels and their benefit over CE since it presents a more realistic channel that displays a real propagation circumstance. on the flip side, we evaluated the CE quality using ideal MinimumMean Square Error (MMSE). This latter relies on an impractical property that can be explicated since the MMSE estimator considers foreknowledge on Large-Scale Fading (LSF) coefficients of interfering users. Thus, the suggested estimator is introduced to overcome this issue, where the suggested estimator tackled this problem and presented result asymptotic approaches to the performance of the MMSE estimator. Besides, we considered a more real communication in which the multi-path channels are either realized using Non-Line-of-Sight (NLoS) only or using both Line-of-Sight (LoS) and NLoS path depending on the distance at which the user is located from his serving BS. Otherwise, in numerous scenarios, users at the cell edge are strongly affected by Pilot Contamination (PC). Hence, we introduced a Power Control (PoC) policy so that the users at the cell edge are less affected by the PC problem. In the simulation results segment, the analytic and simulated results are introduced to assert our theoretical study.

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 175
Jinlei Chen ◽  
Sheng Wang ◽  
Carlos E. Ugalde-Loo ◽  
Wenlong Ming ◽  
Oluwole D. Adeuyi ◽  

Although the control of modular multi-level converters (MMCs) in high-voltage direct-current (HVDC) networks has become a mature subject these days, the potential for adverse interactions between different converter controls remains an under-researched challenge attracting the attention from both academia and industry. Even for point-to-point HVDC links (i.e., simple HVDC systems), converter control interactions may result in the shifting of system operating voltages, increased power losses, and unintended power imbalances at converter stations. To bridge this research gap, the risk of multiple cross-over of control characteristics of MMCs is assessed in this paper through mathematical analysis, computational simulation, and experimental validation. Specifically, the following point-to-point HVDC link configurations are examined: (1) one MMC station equipped with a current versus voltage droop control and the other station equipped with a constant power control; and (2) one MMC station equipped with a power versus voltage droop control and the other station equipped with a constant current control. Design guidelines for droop coefficients are provided to prevent adverse control interactions. A 60-kW MMC test-rig is used to experimentally verify the impact of multiple crossing of control characteristics of the DC system configurations, with results verified through software simulation in MATLAB/Simulink using an open access toolbox. Results show that in operating conditions of 650 V and 50 A (DC voltage and DC current), drifts of 7.7% in the DC voltage and of 10% in the DC current occur due to adverse control interactions under the current versus voltage droop and power control scheme. Similarly, drifts of 7.7% both in the DC voltage and power occur under the power versus voltage droop and current control scheme.

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