Automatique
Maximum power extraction framework using robust fractional-order feedback linearization control and GM-CPSO for PMSG-based WECS
The most important issue in the use of wind energy conversion systems is to ensure maximum power extraction in terms of efficiency. Therefore, maximum power point tracking algorithms are as important as the maximum power point tracking controller. In this study, maximum power extraction frameworks operating the state-of-the-art optimization methods are presented for permanent magnet synchronous generator–based wind energy conversion system. These frameworks consist of a Gauss map–based chaotic particle swarm optimization and a hybrid maximum power point tracking approach that combines feedback linearization technique with fractional-order calculus. The feedback linearization control strategy can fully decouple and linearize the original state variables of the nonlinear system and thus provide an optimal controller crossing wide-range operating conditions. The objective is to maintain the tip speed ratio at its optimal value, which implies the use of a rotational speed loop. The method is based on the feedback linearization technique and the fractional control theory. Gauss map–based chaotic particle swarm optimization, which is a remarkable and recent optimization technique, is utilized to achieve optimum coefficients to efficiently ensure the maximum power point tracking operation in here. A simulation study is carried out on a 3-kW wind energy conversion system to show the effectiveness of the proposed control scheme. Voir les détails
Mots clés : Permanent Magnet Synchronous generator, Maximum Power Point Tracking, Feedback linearization control, fractional-order theory, Gauss map–based chaotic particle swarm optimization
A new robust tilt-PID controller based upon an automatic selection of adjustable fractional weights for permanent magnet synchronous motor drive control
This paper focuses on achieving a good trade-off between performance and robustness for a class of uncertainty models including unstructured multiplicative uncertainties. In robust control, the simultaneous improvement of the two secure margins for nominal performances and robust stability using a standard controller structure represents two contradictory objectives and guaranteeing simultaneously of these goals represents therefore a major challenge for most researchers. In this context, a robust tilt-proportional integral derivative (T-PID) controller synthesized with an automatic selection of adjustable fractional weights (AFWs) is discussed in our work. Their parameters are optimized through solving a weighted-mixed sensitivity problem using an optimization tool which is based on the genetic algorithm. This problem is formulated from performance and robustness requirements where a fitness function is accordingly determined. Furthermore, thus its search space is built according to some guidelines for ensuring an automatic selection of adequate AFWs. The proposed constrained optimization problem is initialized by using arbitrary T-PID speed controller as well as through initial fixed integer weights (FIWs) which were chosen previously by the designer. To highlight the proposed control strategy, the synthesized robust T-PID speed controller is applied on the permanent magnet synchronous motor. Their performance and robustness are compared to those provided by an integer-order PID (IO-PID) and two conventional fractional-order PID (FO-PID) controllers. This comparison reveals superiority of the proposed robust T-PID controller over the remaining controllers in terms of robustness with reduced control energy. Voir les détails
Mots clés : Weighted-mixed sensitivity problem, Tilt-proportional integral derivative controller, Fractional-order FO-PID speed controller, Permanent magnet synchronous motor
New Optimal Control of Permanent Magnet DC Motor for Photovoltaic Wire Feeder Systems
This article aims to improve the permanent magnet DC (PMDC) motor performance for photovoltaic (PV) wire-feeder systems (PVWFSs) of arc welding machines. The considered technique is designed by direct speed control based on optimal Fractional-order Fuzzy PID FO-Fuzzy-PID controller. The purpose is to ensure optimal control of wire feed speed reference to reduce torque ripples and hence, the performance of the WFS is improved. The dynamic reaction of the proposed solar PVWFS relies upon the scaling factors of FO-Fuzzy-PID controller, which are optimized by using teaching-learning algorithm based on Particle Swarm Optimization (PSO) method. The maximum power point tracking (MPPT) is achieved using an intelligent FO-Fuzzy-PID current controller based Perturb and Observe (P&O) MPPT algorithm. The PVWFS system incorporating the proposed method is tested and compared with the conventional PID control scheme under different weather conditions. The simulation of the proposed system by MATLAB\SIMULINK is carried out. The simulation results indicate the effectiveness of the considered control strategy in terms of the reduction in torque oscillations, optimizing electrical power and wire feed speed. Voir les détails
Mots clés : Solar photovoltaic (PV) module, wire feeder systems (WFSs), DC-DC buck converter, MPPT control, FO-Fuzzy PID controller, Particle Swarm Optimization (PSO) algorithm
Auto-control technique using gradient method based on radial basis function neural networks to control of an activated sludge process of wastewater treatment
Dissolved oxygen (DO) concentration is a key variable in the activated sludge wastewater treatment processes. In this paper, an auto control strategy based on Euler method and gradient method with radial basis function (RBF) neural networks (NNs) is proposed to solve the DO concentration control problem in an activated sludge process of wastewater treatment. The control purpose is to maintain the dissolved oxygen concentration in the aerated tank for having the substrate concentration within the standard limits established by legislation of wastewater treatment. For that reason, a new proposed control strategy based on gradient descent method and RBF neural network has been used. Compared with RBF neural network PI control, the obtained results show the effectiveness in terms of both transient and steady performances of proposed control method for dissolved oxygen control in the activated sludge wastewater treatment processes. Voir les détails
Mots clés : activated sludge process, Wastewater treatment, Gradient descent algorithm, RBF neural network, PI control
A Comparison Study: Direct and Indirect ModeControl of Perturb and Observe-MPPT Algorithmsfor Photovoltaic System
The Perturb and Observe P&O algorithm hasbeen widely used in most real-world applications due to itssimplicity of implementation in the control loops. Its main idea isto adjust the operating point of photovoltaic PV panels to ensurea good tracking behavior of a desired Maximum Power PointMPP. The P&O algorithm is one of the most used MPPTalgorithms to extract the electrical energy of PV panels underdifferent weather conditions. This can be done via the directcontrol mode of the DC-DC boost converter which commonlylinked by an external resistive load. However, the given electricalpower of the P&O-MPPT algorithm becomes fluctuating in thesmall time range, especially when the current MPP is graduallyapproaching the desired one. It provides unfortunately a steadystatepower oscillation problem and a loss of electrical energy at asudden change of climatic conditions. The indirect control modeof the DC-DC boost converter via P&O-MPPT algorithm isadopted as an alternative key to avoid the above mentioneddrawbacks where electrical performances are well enhanced interm of transient and steady-states of the given output powerresponse, the MPP tracking accuracy, the given electrical energyratio and so on. This goal can be reached through the followingsteps. The desired reference voltage perturbation is firstlycomputed by the standard P&O algorithm using the MPPmeasurements recorded through the actual PV panel at thestandard test condition STC (i.e., nominal absolute temperatureand nominal solar irradiance). It then compared by the actualvoltage perturbation generated by the closed loop P&O-MPPTscheme, providing thus the discrepancy voltage perturbation.Finally, a Proportional-Integral-Derivative PID controller givenin the P&O-MPPT inner loop scheme is used to mitigate as muchas possible the previous voltage error perturbation. This yields adesired duty cycle perturbation of the DC-DC boost converterwhich allowing reaching a good trade-off between both transientstatespeed and steady-state stationary of the output powerresponse. Simulation results confirm the effectiveness of theindirect control mode of the P&O –MPPT algorithm over thedirect control mode of same algorithm for several suddenchanges in weather conditions and wide variations of the resistiveload. Voir les détails
Mots clés : PV system, Boost converter, P&O algorithm, Direct and indirect control modes
Fuzzy controller design using particle swarm optimization for photovoltaic maximum power point tracking
Recently, researchers have strongly promoted the use of solar energy as a viable source of energy due to its advantages and which it can be integrated into local and regional power supplies. The P-V curve of photovoltaic system exhibits multiple peaks under various conditions of functioning and changes in meteorological conditions which reduce the effectiveness of conventional maximum power point tracking (MPPT) methods and the Particle Swarm Optimization (PSO) algorithm is considered to be highly efficient for the solution of complicated problems. In this paper, the application of this approach based MPPT algorithm for Photovoltaic power generation system operating under variable conditions is proposed to optimize and to design an intelligent controller comparing to conventional one. Voir les détails
Mots clés : PV systems, Boost, PWM, MPPT, FLC, PSO
Optimal control based RST controller for maximum power point tracking of wind energy conversion system
This paper presents an LQ optimal control based RST controller for maximum power tracking in a wind energy conversion system (WECS) connected to the electrical grid through a back-to-back converter. Input-output discrete WECS model has been used to implement the input-output optimal control approach. The performance criterion has two terms: the first one is involved for maximum power tracking and the other one for the mechanical fatigue loading (control input) minimization. The obtained simulation results with the considered control and a variable wind profile show an adequate dynamic of the considered conversion system. Voir les détails
Mots clés : wind energy conversion system, Induction generator (IG), LQ optimal control, RST controller
Energy storage based on maximum power pointtracking in photovoltaic systems: A comparisonbetween GAs and PSO approaches
In this paper, a comparison between GAs and PSO Approaches is considered to select andgenerate an optimal duty cycle which varies with photovoltaic parameter in order toextract the maximum Power from Photovoltaic System using real values of temperatureand insolation. The energy storage has very important role in renewable energy. To illustratethe energy storage, we have used a battery type lead-acid simulated in Matlab/Simulink. The obtained simulations results show the effectiveness and the robustness ofthe proposed approaches. Voir les détails
Mots clés : Photovoltaic systems, Chopper, MPPT, PSO, GAs, Energy storage
On-Off control based particle swarm optimization for maximum power point tracking of wind turbine equipped by DFIG connected to the grid with energy storage
In this paper, particle swarm optimization (PSO) is proposed to generate an On-Off Controller. On-Off Control scheme based maximum power point tracking is proposed to control the rotor side converter of wind turbine equipped with doubly fed induction generator connected to the grid with battery storage. The Grid Side Converter (GSC) is controlled in such a way to guarantee a smooth DC voltage and ensure sinusoidal current in the grid side. Simulation results show that the wind turbine can operate at its optimum power point for variable speed and power quality can be improved. Voir les détails
Mots clés : DFIG, Energy storage, Maximum Power Point Tracking, On-Off Control, Particle Swarm Optimization (PSO)
Adaptive backstepping control using combined direct and indirect σ-modification adaptation
In this paper, by using the dynamic surface control technique, an adaptive backstepping controller using combined direct and indirect σ-modification adaptation is proposed for a class of parametric strict-feedback systems. In this approach, a σ-modification parameter adaptation law that combines direct and indirect update laws is proposed. At first, the x-swapping identifier with a gradient-type update law is presented for a class of parametric strict-feedback nonlinear systems. Next, the main steps of the controller design for a class of nonlinear systems in parametric strict-feedback form are described. The closed-loop error dynamics is shown to be globally stable by using the Lyapunov stability approach. Finally, simulation results for a single-link flexible-joint robot manipulator are given to illustrate the tracking performance of the proposed adaptive control scheme. Voir les détails
Mots clés : Backstepping control, direct and indirect adaptive control, adaptive dynamic surface control, Lyapunov stability, flexible joint manipulators