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Keywords = PVM-LMA

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24 pages, 12095 KiB  
Article
Study on Thermophysical Properties of Arc Plasma for Melting Magnesium Oxide Crystals at Atmospheric Pressure
by Zhongyuan Chi, Weijun Zhang and Qiangda Yang
Energies 2022, 15(3), 1036; https://doi.org/10.3390/en15031036 - 29 Jan 2022
Cited by 1 | Viewed by 2478
Abstract
The thermodynamic and transport properties of magnesium oxide crystal arc plasma have been researched under local thermodynamic equilibrium in this paper. The pure CO2 plasma in the arc initiation stage and Mg-CO mixtures plasma in the stable melting stage were selected. The [...] Read more.
The thermodynamic and transport properties of magnesium oxide crystal arc plasma have been researched under local thermodynamic equilibrium in this paper. The pure CO2 plasma in the arc initiation stage and Mg-CO mixtures plasma in the stable melting stage were selected. The parameter-variation method combined with Levenberg–Marquardt algorithm (PVM-LMA) is used to solve the plasma equilibrium compositions model established by mass action law from higher to lower temperature in sequence. Taking Mg50%-CO50% plasma as an example, the plasma number density of 7500 K is calculated according to 8000 K. The results show that the PVM-LMA algorithm has the advantages of fast and high precision. The comparisons to the results of pure CO2 in previous literature are displayed and our work shows better agreement with theirs. The results of Mg-CO mixtures indicate that the chemical properties of Mg atoms are more active and easier to ionize, which can effectively improve the electrical conductivity and thermal conductivity of plasma and reduce its viscosity. Full article
(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)
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17 pages, 3700 KiB  
Article
Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis
by Mohamed Louzazni, Heba Mosalam and Daniel Tudor Cotfas
Electronics 2021, 10(16), 1953; https://doi.org/10.3390/electronics10161953 - 13 Aug 2021
Cited by 25 | Viewed by 3357
Abstract
In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is [...] Read more.
In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results. Full article
(This article belongs to the Special Issue Photovoltaic Energy Systems and Storage)
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