mahdi zolfaghari

— This paper describes a control procedure based on fractional order calculus to design the controllers of the converters of doubly fed induction generator (DFIG) of wind turbine systems. The control scheme implements the optimal fractional order proportional-integral-derivative (FOPID) controllers in the control loops of the converters. The FOPID controllers are optimally tuned using genetic algorithm (GA) to produce accurate and effective control performance. A variable speed wind generation systems connected to the power grid is considered in this study. During transient disturbances occurring in the electrical grid and other distortions, the control action of the controllers of the converters has an important role to sustain the wind turbine system in stable operation. This implies that gain adjustment of these controllers is not a trivial task, due to the nonlinearities and the high complexity of the system. Thus, an appropriate fitness function is derived to express the time domain evaluation of the DFIG with purpose of assuring the DFIG continuous operation even under a fault condition and improve at the same time its transient behavior as compared to the conventional methodology of designing PI controllers using poles placement. The results of the simulation using MATLAB confirm the efficacy of the proposed control scheme.

This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability.

— This paper presents a control strategy to improve the load sharing problem between inverter-based distributed generations (DGs) in microgrid. The proposed strategy uses an optimal proportional-integral (PI) controller, which has been tuned using genetic algorithm (GA). The controller tries to minimize the circulating current among parallel-connected DGs. The simulation results using MATLAB/SIMULINK confirm the effectiveness of proposed control strategy.

Microgrid is the main part of future electrical power systems, called "smart grids". In this context, synchronization of a microgrid with utility or other microgrids will be a crucial and commonplace task during the power system operation. Based on the robust control principles, a new approach for synchronizing microgrids with utility was presented in this paper. Uncertainties of the dynamic model of microgrids were considered as multiplicative perturbations. Based on the uncertain model, a robust controller was designed by means of µ synthesis analysis so that the robust stability and performance of the system could be maximized. The simulation results confirmed the effectiveness of the proposed control strategy.

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator.

In this paper, a sensorless permanent magnet synchronous motor (PMSM) drive was presented based on direct power control (DPC) technique. To estimate the rotor's position and speed of PMSM, a drastic sensorless strategy was developed according to artificial neural network (ANN) to reduce the cost of the drive and enhance the reliability. The proposed sensorless scheme was an innovative model reference adaptive system (MRAS) speed observer for DPC control PMSM drives. The suggested MRAS speed observer employed the current model as an adaptive model. The ANN was then designed and trained online by employing a back propagation network (BPN) algorithm. Performance of the proposed strategy was adopted using simulation analysis. The results showed the fast dynamic response, low ripples in motor's currents, power, and electromagnetic torque, as well as good performance in tracking speed and power references.

In this paper, a new approach to the Direct Torque and Flux Control (DTFC) problem of three-phase induction motor drives is presented. Using DTFC technique, the complexity of Field Oriented Control (FOC) is eliminated. In the proposed DTFC method, the stator flux locus is divided into twelve sectors instead of just six all six active states will be used per sector. Simulation results demonstrate that the proposed controller leads to performance improvements despite its simple structure.

This paper presents the dynamic modeling and simulation of a microturbine generation (MTG) system, the nonrenewable source of energy suitable for isolated as well as grid-connected operation in microgrid networks. A microgrid is defined as an independent low or medium-voltage distribution network comprising various DGs, energy storages, and controllable loads. The MTG system consisting of a permanent magnet synchronous generator (PMSG) coupled with a microturbine (MT) which is suitable for stability studies in microgrid networks is modeled and simulated using MATLAB/SIMULINK.

This paper presents a simple, effective control strategy applied to parallel- connected of single- phase voltage source inverters of distributed generations. This strategy is based on changing the gains of the controller that controls the output voltage of the inverter. With this approach, the output current of the inverter is change when the load dynamics or line impedance is change. The effectiveness of the proposed strategy is confirmed through simulation results.

Controlled induction motor drives without mechanical speed sensors at the motor shaft
have the attractions of low cost and high reliability. For these speed sensorless AC drive system, it is
key to realize speed estimation accurately. This paper describes a Model Reference Adaptive System
(MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of
sensorless vector controlled induction motor drive. The neural network has been then designed and
trained online by employing a back propagation network (BPN) algorithm. The estimator was
designed and simulated in Matlab. Simulation result shows a good performance of speed estimator.
Also Performance analysis of speed estimator with the change in resistances of stator is presented.
Simulation results show this estimator robust to resistances of stator variations.

This paper presents a new controller for speed control problem of the BLDC motors. The nonlinear
model of the motor is approximated by implementation of fuzzy rules. The uncertainties are considered
in the fuzzy system. Using this model and linear matrix inequality (LMI) optimization, a robust
controller for purpose of speed control of the motor has been designed and applied to it. The
effectiveness of the designed controls demonstrated through simulation results.