Résumé: Wind energy plays a crucial role in the global transition toward sustainable power generation. To meet the growing demand for renewable energy, optimizing wind farm design is essential for both maximizing energy output and minimizing costs. This paper builds upon previous studies by refining and expanding several ideal wind turbine configurations, using them as the foundation for a more comprehensive analysis and enhancement. While prior research has primarily focused on reducing the cost per kilowatt (cost/kW) of power generated, this study takes a broader approach, aiming to optimize overall wind farm efficiency through the strategic adjustment of turbine rotor diameters and hub heights. By implementing a particle swarm optimization (PSO) algorithm, this work identifies the optimal arrangement of these turbine dimensions to achieve higher efficiency while simultaneously reducing overall costs. The proposed methodology offers a flexible, scalable solution that can significantly enhance wind farm performance, making it more adaptable to varying environmental conditions and economic constraints.

Résumé: A position control of a two-degree-of-freedom planar robot modeled in bond graph is proposed. Bond graph is a methodology that allows direct modeling of mechatronic systems, so robots represented in bond graph are interesting case studies to apply the properties in this graphic work environment. In this way, a control called calculated torque to remove the nonlinearities characteristic of robot modeling is applied in bond graph. To know the shape of the controller, the mathematical model of the robot is necessary, so this is obtained through the junction structure then the controller is designed in bond graph. To know the effectiveness of the closed-loop system. Simulation results are shown where it is verified that the position of each link of the planar robot reaches the desired reference input.

Résumé: Due to the deregulation of the electricity market, power systems are showing increased uncertainties related to loads and renewable sources. Such uncertainties will involve the assessment of the impact of uncertain variables on the control and monitoring of the future power systems. Identification and classification of influential uncertain variables are imperative for power system stability assessment in future smart grid because they provide guidance for handling uncertainty for electrical system operators for management and securing the power grid operations with efficient control and monitoring. In this paper, a probabilistic analysis methodology is proposed for the assessment of the impact of critical uncertain variables on voltage and small-signal stability using quantitative sensitivity technique. The proposed methodology is carried out with different level of penetration of renewable sources and the impacts of influential variables are treated and discussed. The proposed methodology is performed using the modified IEEE 14-bus benchmark. The performances of the probability density function graphs for stability indexes are used for assessing their sensitivities.

Résumé: The article deals with the robust fault detection and isolation based on linear fractional transformation hybrid bond graph. The main objective is to improve the robustness of fault detection step in the presence of parametric uncertainties in hybrid models in order to minimize the false alarms. The scientific interest of the proposed method is the use of only one tool – the bond graph not only for dynamic modelling of uncertain hybrid system but also for generation of adaptive thresholds needed in residual evaluation step. For this task, hybrid bond graph uncertain model approach with controlled junctions in linear fractional transformation form (allowing to represent graphically the parametric uncertainties) is first proposed to represent all the modes of the hybrid system. Second, based on causal and structural properties of the hybrid bond graph, analytical redundancy relations (with nominal and uncertain part) valid for all the modes are then derived systematically from the diagnostic hybrid bond graph. An application to a hydraulic system is used to illustrate this method.

Résumé: This paper provides a comprehensive analysis of the impact of wind farm integration on the stability of an electrical power system, with a particular focus on the dynamic effects induced by wind power during severe system disturbances. The primary objective is to evaluate how wind power integration influences system behavior, particularly under fault conditions. The research utilized the IEEE 9-bus test system, which consists of three generators and nine buses, as a reference benchmark for the stability analysis. Simulations and modeling were performed using PSAT (Power System Analysis Toolbox), a robust tool for static, dynamic, and control analysis of power systems, integrated with Matlab. The study specifically investigated the effects of a three-phase short circuit, representing a significant system disturbance. The dynamic analysis was structured into three phases: pre-fault, fault, and post-fault, to capture the complete impact on system behavior. The results provided valuable insights into critical electrical and electromechanical variables, shedding light on the role of wind farms in maintaining the stability of the power grid, with particular emphasis on the transmission network's resilience and response to disturbances. This work offers a deeper understanding of the integration of renewable energy sources in enhancing grid stability during extreme events.