Les énergies renouvelables prennent ces dernières années un axe d’investigation pour les chercheurs. Pour cette raison, notre étude est consacrée à l’application des différentes commandes non linéaires à la génératrice asynchrone double alimentée (GADA) intégrée dans un système de conversion de l’énergie éolienne. En premier lieu on présente l’application de la commande vectorielle associée à un système éolien. Pour raison d’amélioration des performances, des commandes avancées de type Mode Glissant (MG) et Backstepping (Back) sont appliquées à la GADA-éolienne afin d’assurer un découplage entre les puissances active et réactive pour des vitesses fixe et variable avec des performances souhaitées. La stratégie MPPT (Maximum Power Point Track) pour extraire le maximum de puissance pendant la conversion est développée. Aussi, la technique SVM (Space Vector Modulation) est appliquée. L’étude comparative des différentes commandes étudiées à travers les résultats des simulations montre une amélioration significative des performances des contrôleurs non linéaires, Backstepping (Back) et Mode Glissant (MG) proposés par rapport au contrôleur vectoriel en termes de réponse dynamique, de rejet des perturbations et des variations paramétriques

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

The combination of neural networks and fuzzy controllers is considered as the most efficient approach for different functions approximation, and indicates their ability to control nonlinear dynamical systems. This paper presents a hybrid control strategy called Neuro-Fuzzy Sliding Mode Control (NFSMC) based on the Brushless Doubly fed Induction Generator (BDFIG). This replaces the sliding surface of the control to exclude chattering phenomenon caused by the discontinuous control action. This technique offers attractive features, such as robustness to parameter variations. Simulations results of 2.5 KW BDFIG have been presented to validate the effectiveness and robustness of the proposed approach in the presence of uncertainties with respect to vector control (VC) and sliding mode control (SMC). We compare the static and dynamic characteristics of the three control techniques under the same operating conditions and in the same simulation configuration. The proposed controller schemes (NFSMC) are effective in reducing the ripple of active and reactive powers, effectively suppress sliding-mode chattering and the effects of parametric uncertainties not affecting system performance.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

Research on risk matrices show that there is considerable diversity in the practice of designing risk matrices. This has led to serious problems of standardization and communication. Indeed, these problems affect at the same time on the development of matrices and in their exploitation in term of risk assessment. To solve these problems, this paper proposes an experience feedback method that aims to capitalize the feedback invariants resulting from the analysis of existing risk matrices. This capitalization allows developing a theoretical framework of the robust risk matrices design. The application of the proposed method for examples of matrices confirms the interest of articulating these risk matrices designs through an argument based on experience feedback. In this sense, the merit of the proposed experience feedback method is that it promotes the sharing of knowledge between the actors involved in a risk assessment.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30 years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have launched a rehabilitation and modernization schedule of these installations. Within the framework of this program, many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM) context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed indicators. Their prioritization adjusted by (TOPSIS) Technique for Order-Preference by Similarity to Ideal Solution method which allows identification of ageing control solutions of Algerian onshore fields.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Purpose The purpose of this paper is to achieve the national strategic agenda’s criteria that aim for accomplishing sustainable buildings by estimating the effects of energy efficiency measures in order to reduce energy consumption and CO2 emission. Design/methodology/approach A design approach has been developed based on simulation software and a modeled building. Therefore, a typical office building is considered for testing five efficiency measures in three climatic conditions in Algeria. This approach is conducted in two phases: first, the analysis of each measure’s effect is independently carried out in terms of cooling energy and heating energy intensities. Then, a combination of optimal measures for each climate zone is measured in terms of three sustainable indicators: final energy consumption, energy cost saving and CO2 emission. Findings The results reveal that a combination of optimal measures has a substantial impact on building energy saving and CO2 emission. This saving can rise to 41 and 31 percent in a hot and cold climate, respectively. Furthermore, it is concluded that obtaining higher building performance, different design alternatives should be adapted to the climate proprieties and the local construction materials must be applied. Originality/value This study is considered as an opportunity for achieving the national strategy, as it may contribute in improving office building performance and demonstrating a suitable tool to assist stakeholders in the decision making of most important parameters in the design stage for new or retrofit buildings.

Purpose The purpose of this paper is to achieve the national strategic agenda’s criteria that aim for accomplishing sustainable buildings by estimating the effects of energy efficiency measures in order to reduce energy consumption and CO2 emission. Design/methodology/approach A design approach has been developed based on simulation software and a modeled building. Therefore, a typical office building is considered for testing five efficiency measures in three climatic conditions in Algeria. This approach is conducted in two phases: first, the analysis of each measure’s effect is independently carried out in terms of cooling energy and heating energy intensities. Then, a combination of optimal measures for each climate zone is measured in terms of three sustainable indicators: final energy consumption, energy cost saving and CO2 emission. Findings The results reveal that a combination of optimal measures has a substantial impact on building energy saving and CO2 emission. This saving can rise to 41 and 31 percent in a hot and cold climate, respectively. Furthermore, it is concluded that obtaining higher building performance, different design alternatives should be adapted to the climate proprieties and the local construction materials must be applied. Originality/value This study is considered as an opportunity for achieving the national strategy, as it may contribute in improving office building performance and demonstrating a suitable tool to assist stakeholders in the decision making of most important parameters in the design stage for new or retrofit buildings.

Aiming to provide a generalized method for assessing the performance of safety instrumented systems with a flexible and accurate consideration of the common cause failures’ contribution. This paper is devoted to the development of a direct way to generate the transition rate matrix associated with the continuous-time Markov model of any typical KooN architecture using any parametric model. Such a choice is considered after a detailed comparison of the ability of several dependability methods (e.g., fault trees, reliability block diagrams, Markov models, Bayesian networks, etc) to provide simple representations and genuine results in this context. To validate the developed method, the unavailability and the unconditional failure intensity of a wide range of configurations are quantified using the Binomial Failure Rate model and compared to those of the complete fault tree implementation.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.

Purpose The purpose of this paper is to show the impact of operational and environmental conditions (risk influencing factors) on the component criticality of safety barriers, safety barrier performance and accidents frequency and therefore on risk levels. Design/methodology/approach The methodology focuses on the integration of criticality importance analysis in barrier and operational risk analysis method, abbreviated as BORA-CIA. First, the impact of risk influencing factors (RIFs) associated with basic events on safety barrier performance and accident frequency is studied, and then, a risk evaluation is performed. Finally, how unacceptable risks can be mitigated regarding risk criteria is analyzed. Findings In the proposed approach (BORA-CIA), the authors show how specific installation conditions influence risk levels and analyze the prioritization of components to improve safety barrier performance in oil and gas process. Practical implications The proposed methodology seems to be a powerful tool in risk decision. Ordering components of safety barriers taking into account RIFs allow maintenance strategies to be undertaken according to the real environment far from average data. Also, maintenance costs would be estimated adequately. Originality/value In this paper, an improved BORA method is developed by incorporating CIA. More precisely, the variability of criticality importance factors of components is used to analyze the prioritization of maintenance actions in an operational environment.