Cet article est consacré à l’étude des performances de la génératrice asynchrone à cage double étoile (GASDE) en site isolé. Le système de commande est composé d’une GASDE raccordé à un bus continu et une charge en sortie de deux redresseurs à commande MLI. Une étude comparative entre la technique de commande conventionnelle et la commande adaptée basée sur l’introduction de la SVM-PI-flou et un nouvel estimateur de flux (flux virtuel statorique) afin d’améliorer la qualité d’énergie et d’atténuer les harmoniques du courant.

Cet article est consacré à l’étude des performances de la génératrice asynchrone à cage double étoile (GASDE) en site isolé. Le système de commande est composé d’une GASDE raccordé à un bus continu et une charge en sortie de deux redresseurs à commande MLI. Une étude comparative entre la technique de commande conventionnelle et la commande adaptée basée sur l’introduction de la SVM-PI-flou et un nouvel estimateur de flux (flux virtuel statorique) afin d’améliorer la qualité d’énergie et d’atténuer les harmoniques du courant.

Cet article est consacré à l’étude des performances de la génératrice asynchrone à cage double étoile (GASDE) en site isolé. Le système de commande est composé d’une GASDE raccordé à un bus continu et une charge en sortie de deux redresseurs à commande MLI. Une étude comparative entre la technique de commande conventionnelle et la commande adaptée basée sur l’introduction de la SVM-PI-flou et un nouvel estimateur de flux (flux virtuel statorique) afin d’améliorer la qualité d’énergie et d’atténuer les harmoniques du courant.

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. 

Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. 

Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. 

Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. 

Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. 

Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. 

Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

Background: Coronavirus disease has become a worldwide threat affecting almost every country in the world. The spread of the virus is likely to continue unabated. The aim of this study is to compare between Autoregressive Integrated Moving Average (ARIMA) model, Fuzzy time series and Holt-Winters – No seasonal for forecasting the COVID-19 new cases in Algeria. 

Methods: Three different models to predict the number of Covid-19 new cases in Algeria were used. The number of new cases of COVID-19 in Algeria during the period from 24^th February 2020 to 31th July 2021 was modeled according to ARIMA(4,1,2) model, Five based Fuzzy time series models including the Chen model, Heuristic Huareng model, Singh model, Abbasov-Manedova model and NFTS model, and Holt-Winters – No seasonal. 

Results: The predictive values were obtained from the 1^st August 2021 to 31th December 2021. According to a set of criteria (ME, MAE, MSE, RMSE, U), we found that the FTNS model is the most accurate and best generating model for the values of the number of new cases of Covid-19. 

Conclusion: To the best of our knowledge, this is the first comparative study of three models of forecasting of Covid-19 new cases in Algeria. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria. Moreover, this forecast will help the Health authorities to be better prepared to fight the epidemic by engaging their healthcare facilities.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

A comparative study between a set of chosen machine learning tools for direct remaining useful life prediction is presented in this work. The main objective of this study is to select the appropriate prediction tool for health estimation of aircraft engines for future uses. The training algorithms are evaluated using “time-varying” data retrieved from Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) developed by NASA. The training and testing processes of each algorithm are carried out under the same circumstances using the similar initial condition and evaluation sets. The results prove that among the studied training tools, Support vector machine (SVM) achieved the best results.

: In this paper, we have studied a different fault tolerant control (FTC) strategies for a three-phase induction motor (3p-IM). Further we introduce Backstepping controller (BC) and Input-output linearization controller (IOLC). To provide a direct comparison between these FTCs approaches, the performances are evaluated using the control of 3p-IM under failures, variable speed, and variable parameters. A comparison between the two control strategies is proposed to prove the most robust one. The simulation results show the robustness and good performance of the fault tolerant control with Input-output linearization controller compared to one with Backstepping controller. The FTC with IOLC is more stable and robust against failures, load torque perturbation and speed reversion

: In this paper, we have studied a different fault tolerant control (FTC) strategies for a three-phase induction motor (3p-IM). Further we introduce Backstepping controller (BC) and Input-output linearization controller (IOLC). To provide a direct comparison between these FTCs approaches, the performances are evaluated using the control of 3p-IM under failures, variable speed, and variable parameters. A comparison between the two control strategies is proposed to prove the most robust one. The simulation results show the robustness and good performance of the fault tolerant control with Input-output linearization controller compared to one with Backstepping controller. The FTC with IOLC is more stable and robust against failures, load torque perturbation and speed reversion

: In this paper, we have studied a different fault tolerant control (FTC) strategies for a three-phase induction motor (3p-IM). Further we introduce Backstepping controller (BC) and Input-output linearization controller (IOLC). To provide a direct comparison between these FTCs approaches, the performances are evaluated using the control of 3p-IM under failures, variable speed, and variable parameters. A comparison between the two control strategies is proposed to prove the most robust one. The simulation results show the robustness and good performance of the fault tolerant control with Input-output linearization controller compared to one with Backstepping controller. The FTC with IOLC is more stable and robust against failures, load torque perturbation and speed reversion

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

The current paper illustrates the numerical study of the global combustion parameters. It mainly focused on the computational analysis that investigated the non-premixed combustion in the cylindrical burner. Therefore, we selected many fuels to supply the burner like Algerian biogas, CH₄, C3H₈, H2, natural gas, and diesel to compare their aerothermochemical characteristics variables. At first, we applied the numerical methods that confirm the solution convergence like combustion models and grid selection. After that, we resolved the aerothermochemical set equations of combustion using the coupled k-ɛɛ turbulent dynamic model with the probability density function approach. These models are also used to surmount the closer in the set of combustion equations too. Moreover, we integrated the pollutant computation model based on the chemical reactions of NO production. Thus, we evaluated each considered fuel’s NO emission during all combustion fuels cases. Accordingly, the results show that Algerian biogas and hydrogen have special characteristics compared to other cases of fuels. The most prominent characteristics are: the high level of the mixture and burn relative to other fuels, their low pollutants emissions (CO and NO), and the proportional relationship between the OH and NO production. Consequently, biogas and H2 conserve the impact on energy and the environment.

In this paper, we consider a time-fractional reaction-diffusion system with the same nonlinearities of the Newton-Leipnik chaotic system. Through analytical tools and numerical results, we derive sufficient conditions for the asymptotic stability of the proposed model and show the existence of chaos. We also propose a nonlinear synchronization controller for a pair of systems and establish the local and global asymptotic convergence of the trajectories by means of fractional stability theory and the Lyapunov method.