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This work presents a study about the control strategies applied on the wind turbine in different operation modes. The produced wind turbine power is optimized in a wide range wind speed thanks to the applied controls in all the operation regions. The strategy of (MPPT) is applied when the wind power is lower than its nominal value. However, in high power relatively the rotation speed and the produced power are limited to their nominal values. The simulation results proved the validity of the mathematical model and the control strategies applied in all operation regions. They show that the proposed controls ensure the continuity of the electric energy production in high wind speed relatively with a good protection of the WECS.

This paper studies the modeling and the controls applied on a real wind turbine (V52) in different operation modes. According to the available wind power and the nominal power, the wind turbine produced power is optimized in a wide range wind speed thanks to the applied controls in all operation regions. When the power is lower than its nominal value, the maximum power point tracking (MPPT) strategies is applied. However, in high rotation speed and high power relatively, the rotation speed and the produced power are limited to their nominal values, all these is ensured by an intelligent neural network controller. The simulation results proved the validity of the model and the control strategies applied. They show that the proposed controls are efficient solutions to protect the Wind Energy Conversion System (WECS) and ensure the electric energy production in high wind speed relatively.

This paper studies the real wind turbine installed in Kaberten (Adrar), in south western Algeria. It is based on Doubly Fed Induction Generator (DFIG) connected to the grid. The main objectives of this research is to create a good model which reflects the real behavior of the studied Wind Energy conversion System (WECS), the optimization of the captured wind power, and the improvement of its performance. In this context, various control strategies were applied. When the power is lower than its nominal value, two Maximum Power Point Tracking (MPPT) strategies were applied and compared. When the power is over or closer to the nominal value, the speed limit and pitch angle controls based on Artificial Neural Network (ANN) approach are applied. The simulation results were compared to the real data of the Supervisory Control and Data Acquisition (SCADA). They proved the validity of the model and the performance improvement.

For a vehicle hybrid electric (VHE) system powered by the batteries and supercapacitors (SC), a real-time optimal power management (OPM) algorithm is proposed based on the look-ahead route information in consideration of the complex traffic environment. At first, a configuration and modeling of the VHE system is addressed. Then, enlighten by the model predictive control (MPC), multiple route information including the traffic and the road terrain over a look-ahead horizon is utilized to predict the future states, and a cost function to minimize total energy loss of the VHE system is given. On above preliminaries, the dynamic programming (DP) algorithm is applied recursively over the look-ahead horizon to update the optimal power distribution between the battery and SC during the total route. Finally, the simulation results confirm that the computational time of OPM has been reduced significantly although a part of energy efficiency is sacrificed. Moreover, the behavior of OPM is similar to that of a skilful driver and thus appropriate be applied to the practical traffic environment.

This paper proposes a sensorless direct power regulation based sliding mode control of a wind energy system. The aim of this paper is to evaluate the performance of the system control and tracking accuracy of rotor position estimator. Sliding mode approach introduces a direct calculation of rotor side and grid side converter voltages by the control law developed. A model-reference adaptive system (MRAS) is used to estimate the rotor position. Simulation results for the system are provided to demonstrate the effectiveness and robustness of the used control.

The paper presents a predictive direct power control of a Doubly Fed Induction Generator (DFIG) via a Direct Matrix Converter (DMC) for use in variable speed Wind Energy Conversion System (WECS). The proposed control method combines the merits of Finite States Model Predictive Control (FSMPC) in term of flexibility to the ones of DFIG control in term of maximum power extraction over a large range of wind speeds. The proposed control algorithm selects the switching state of the Direct Matrix Converter (DMC) that minimizes the error between rotor currents predictions to their computed values for all different voltage vectors. The optimal voltage vector that minimizes a cost function is then applied to the DFIG rotor terminal. Moreover, the proposed predictive control is easily extended to minimize the stator and rotor reactive power with unity power factor operation. Simulation results show that the proposed control method is intuitive since it is simple, multi-objective, avoids inner loops and provides best dynamic performance.

In a wide category of embedded systems, sensors, controllers and actuators are located in distant locations. To ensure the control functions, these components need to be interconnected. Usually, communication networks are chosen to support the required communication exchange. In the context of the automotive industry, the Controller Area Network (CAN) bus is yidely used because of its interesting properties. In theory, a CAN network can ensure real-time communications, when no transmission errors are detected. However, an incrpasing numher of applications are sharing this hus, generating jitter and delays on the exchanged messages, which can degrade the performance of control applications. In this paper, the distributed implementation of an active suspension controller is modelled in Simulink thanks to the use of the tool box TRUETIME. The network traffic is generated using the well-known SAE benchmark. Using this simulation model, the control performance of the active suspension system is studied as a function of the available network bandwidth

Since Permanent Magnet Synchronous Motors (PMSM) came in use in industry, major research (Furuhashi, 1992), (Chen et al., 2000), 436 (Misawa, 1987), (Fakham et al., 2004), (Fakham et al., 2005) was done to eliminate sensors that are needed for proper control. Tllis leads the whole system (motor and control) to be cheaper and more robust to noises and other “electrical pollution”. Tltis paper proposes the observability analysis of PMSM as a high order nOlllillear system. Also it presents a design of a sliding mode observer based 011 back EMF which has a good robustness against the variation of electrical parameters of the motor. The effect of the variations of motor parameters such as stator resistance and stator inductance has been studied too. The simulation and experimental results are shown to demonstrate the effectiveness of the approach.

The rapid development of Mobile Internet and Smart Devices and advent of a new generation of Intelligent Transportation Systems (ITS) increase information about present driving conditions and make its prediction possible. Real time traffic information systems (TIS) like SYTADIN help in route to destination planning and traffic state prediction. Energy-optimal routing for electric vehicles creates novel algorithmic challenges where the computation complexity and the quality of information on traffic state are the main issues. This complexity is induced by the possible negative values of edge energy as well as the variability of route and vehicle variables which render the standard algorithms unsuitable. In this paper the authors present an Energy Optimal Real Time Navigation System (EORTNS), implemented on Samsung Galaxy Tab, capable of calculating the route to destination based on information flow obtained from SYTADIN. As an application example the authors propose a real time energy management for a Hybrid Electrical Vehicle (HEV) composed of batteries and Super-Capacitors (SC). The EORTNS is not only capable of energy optimal route to destination calculation with respect to traffic state but also operates the On-Board power splitting between batteries and Super-Capacitors.

This paper presents a Direct Power Control (DPC) for the Rotor Side Converter (RSC) of Doubly-Fed Induction Generator (DFIG) connected to the wind energy conversion system, as an alternative to the classical vector control (FOC). In this work a simple but effective DPC is proposed to achieve quick response and acceptable steady state performance at reason of using a conventional switching table to select the appropriate voltage vectors to the Rotor Side Converter (RSC). Furthermore for controlling Grid-Side Converter (GSC) system a conventional Direct Power Control (DPC) strategy will be compared with Fuzzy Direct Power Control (F-DPC) for controlling Active and reactive power flow exchanged with grid. Simulation results on a 2 MW DFIG system using MAT-LAB/Simulink are provided to demonstrate the effectiveness and robustness of the proposed control strategy during variations of active and reactive power, rotor speed, and converter dc link voltage.

This paper deals with rotor resistance estimation for induction motors drives purpose. A novel MRAS structure for rotor resistance adaptation is proposed. It is based mainly on the use of a very robust sliding mode flux observer as reference model, which gives robustness to the whole parameter estimation operation. Unlike to classical MRAS structure, based on the use of pure integrators, which have initial value and drift problems. By using the proposed MRAS-Sliding mode observer, system has a better performance and robustness. Effectiveness of this proposed estimation structure is verified by experiment under critical disturbance conditions.

The rapid development of mobile Internet and smart devices and advent of new generation of Intelligent Transportation Systems (ITS) increase information about future driving conditions and makes on-board energy management of Hybrid Electrical Vehicle (HEV) realistic. In this paper we propose a real time energy management algorithm RTEMA for a HEV composed of batteries and Super-Capacitors (SC). We suppose that the On-board Dedicated Computer (ODC) has a permanent Internet connection and has the possibility to perform Google-Maps application queries to calculate 3D route to destination. Based on 3D route to destination and average speeds for each road segment the state of charge (SOC) for battery and SC at the end of each receding horizon are predicted. A cost function considering the efficiency of battery, SC, DC/DC converter and electric motor as well as the battery life cycles increase is presented and an optimization problem is formulated and resolved. As ODC for real-time communication and computation we use Samsung Galaxy Pad which is an Android smart device. Performance results in terms of energy optimization, increased battery life and computing time are given which show the practical usefulness of this approach and application. Such a system can be made available to potential users of HEV as well as to the Android community driver members who share information on the state of traffic flow via their Android mobile phones [30].

The performance of networked control systems based on a switched control law is studied. The control law is switched between sampled-data feedback and zero control, the switch instant of which is determined by a switching parameter. The objective of this paper is to analyze 1) under what condition the switched control law can lead to better performance than the standard one and 2) how to select the switching parameter if the condition is satisfied. Numerical examples show the effectiveness of the proposed results.

International audience; Congestion control is a fundamental building block in packet switching networks such as the Internet due to the sharing of communication resources. It has been shown that the plant dynamics is essentially made of an integrator plus time delay and that a proportional controller plus a Smith predictor is a simple and effective controller. It has been also shown that the today running TCP congestion control can be modelled using a Smith predictor plus a proportional controller. Due to the importance of this control structure in the field of data network congestion control, we analyze the robust stability of the closed loop system in the face of delay uncertainties that in data networks are present due to queuing. In particular, by applying a geometric approach, we derive a bound on the proportional controller gain which is necessary and sufficient to guarantee stability given a bound on the delay uncertainty.

A novel structure of backstepping control has been proposed for indirect field oriented control of induction motor drive. Voltage and current signals are usually contaminated with high frequency switching noises due to SVPWM inverter. The proposed structure avoids real time integration and its associated drift problems, which in practice are the source of generation of unwanted noise. These problems are typical in the PI controllers used in conventional indirect vector control for speed and current regulation. By using the proposed backstepping controller, the system has a good load disturbance rejection capability, fast dynamic response and less parameters sensitivity. The effectiveness of this proposed control structure is verified by simulation as well as by experiment under critical operating conditions.

A Fast Dynamic Programming (FDP) algorithm is proposed to optimize the fuel consumption of the Plug-in Hybrid Electronic Vehicles (PHEV) over a prescribed driving cycle. Firstly, an innovative DP mathematical model for PHEV with reduced dimension is created. It composes of a linear state transfer equation and a quadratic cost function with 1×1 dimension. Based on this model, two algorithms expressed as the simple analytic forms are derived. One is the control algorithm for the optimal power splitting ratio (PSR) between the internal combustion engine (ICE) power and demand power. Another is the recursive algorithm to calculate the optimization value of the fuel consumption. Then, the optimal output power of ICE (or electronic motor (EM)) and the optimal speed ratio of the gear position can be calculated rapidly as the consequence of using the algorithms with analytic forms. Finally, the simulation results confirm that the computational efficiency of the FDP control algorithm has been improved with a geometric ratio, while its control performance maintains in an acceptable range in contrast with conventional DP control algorithm.Copyright © 2009 by ASME

International audience; A switched model-based control for networked control systems is studied in this paper. The control law is switched between model-based control and zero control, the switch instant of which is determined by a switching parameter. The objective of the paper is to analyze under what condition the switched control law can lead to better performance than the standard model-based control and how to select the switching parameter. Numerical examples show the effectiveness of the proposed results.