Ingénierie
Power control strategy of a photovoltaic system with battery storage system
In this paper, an intelligent approach based on fuzzy logic has been developed to ensure operation at the maximum power point of a PV system under dynamic climatic conditions. The current distortion due to the use of static converters in photovoltaic production systems involves the consumption of reactive energy. For this, separate control of active and reactive powers using a proportional-integral controller is applied. Using batteries for energy storage in the photovoltaic system has become an increasingly promising solution to improve energy quality: current and voltage. For this purpose, the energy management of batteries for regulating the charge level under dynamic climatic conditions has been studied. The research presented in this paper provides an important contribution to the application of fuzzy theory to improve the power and performance of a hybrid system comprising a grid-connected PV, battery, and energy management strategy. Therefore, to highlight the advantage of the FL-MPPT studied in this paper, its performance has been compared and analyzed with conventional P&O and NNT algorithms. Simulation results are carried out in MatLab/Simulink tools. According to the analysis of the results, a better energy quality has been proven. Voir les détails
Mots clés : Battery storage, Energy management, Energy storage, MPPT control, Performance, photovoltaic System
Robust adaptive sliding mode control strategy of uncertain nonlinear systems
This paper presents a robust adaptive sliding mode controller scheme as applied to a class of uncertain nonlinear systems with parametric uncertainties and external disturbances. First, a sliding mode control technique is designed. Then, the proposed robust adaptive control schemes are applied to estimate the parametric uncertainties and the upper bound value of the external disturbances by using adaptive laws, ensure robustness in presence of parametric uncertainties and external disturbances, and reduce chattering problem by introducing an hyperbolic tangent function. Lyapunov stability theory is used to analyze the stability of the closed-loop system. As an exemplar, the schemes have been applied to a quadrotor unmanned aerial vehicle (QUAV) model. Simulation results for the control of the QUAV model are provided to illustrate the performance of the proposed robust adaptive sliding mode control scheme and demonstrate that the proposed method has good tracking performance. The simulation results clearly prove the effectiveness of our approach. Voir les détails
Mots clés : adaptive control, Robust control, Sliding mode control, uncertain nonlinear systems, parametric uncertainties, external disturbances, adaptive laws, Lyapunov stability theory, quadrotor unmanned aerial vehicle
Segmentation of x-ray image for welding defects detection using an improved Chan-Vese model
The welding defects detection in industries is becoming an important area and is attracting the attention of many researchers. Radiography is one of the most widely used techniques for inspecting weld defects. X-ray images are generally characterized by low contrast, poor quality and uneven illumination, so the extraction of weld defects could become a difficult task. Among the techniques most used in this field, it is the active contour and the main problem of this technique is the initial contour selection. To solve this problem and obtain reliable and efficient detection of welding defects, we propose in this work a new approach for welding defects detection from x-ray image based on an improved Chan-Vese model. This improved model is based on three stages. The first stage is the detection the region of interest. In the second stage, we apply the Fuzzy C-Mean (FCM) algorithm to select one of the clusters as the initial contour. In the third stage, we use the Chan-Vese model and the selected initial contour to segment the acquired images and obtain the boundaries of the weld defects. Experiments are carried out on different x-ray welding images of the GDxray database in order to extract the characteristics of the welding defects. The results obtained show the effectiveness of the proposed approach compared to conventional techniques. Voir les détails
Mots clés : Chan-Vese model Fuzzy, C-means clustering, X-ray image, Welding defects
Optimizing MAG Welding Input Variables to Maximize Penetration Depth Using Particle Swarm Optimization Algorithm
Systems based on artificial intelligence, such as particle swarm optimization and geneticalgorithm have received increased attention in many research areas. One of the main objectives inthe gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as acharacteristic of quality and stiffness. This article has examined the application of particle swarmoptimization algorithm to obtain a better DP in a GMAW and compare the results obtained with thetechnique of genetic algorithms. The effect of four main welding variables in GMAW process whichare the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distanceon the DP have been studied. For the implementation of optimization, a source code has beendeveloped in MATLAB 8.3. The results showed that, in order to obtain the upper penetration depth,it is necessary that: the welding voltage, the welding speed and the nozzle-to-plate distance must beat their lowest levels; the wire feed speed at its highest level Voir les détails
Mots clés : Artificial intelligence, Particle Swarm Optimization, Genetic algorithm, GMAW, penetration depth, optimization, Matlab