فهرست مطالب

Iranian Journal of Electrical and Electronic Engineering
Volume:14 Issue: 4, Dec 2018

  • تاریخ انتشار: 1397/09/12
  • تعداد عناوین: 10
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  • S. Mirzakuchaki*, Z. Paydar Pages 308-313
    In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of sEMG signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R2 criterion. The Total value of R2 for 6 DoF was 92.8±5.2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous myoelectric control.
    Keywords: Surface Electromyogram Signals (sEMG), Generalized Neural Network (GRNN), R2 Criterion
  • M. Evazi , M. Shahsavan , M. Heidari , A. Razminia * Pages 314-323
    This paper addresses a new method for decreasing error in secure chaotic communication which utilizes an adaptive law in demodulator part. The basic tools in this process are the Total Least Square as the fundamental technique in demodulating section and a chaotic signal as the carrier one which impose some complexities on the overall system. This algorithm may be used in digital filter for estimating parameters with lower error. Using this approach an improvement can be achieved in estimating the desired signal in comparison with two famous methods, namely, ordinary Least Mean Square (LMS) and Constrained-Stability LMS (CS-LMS). An illustrative example has been used to verify the presented technique through numerical simulation.
    Keywords: Chaos, Secure Communications, Total Least Square, Adaptive Digital Filter, Logistic Map
  • F. Khamin Hamedani , Gh. Karimi * Pages 324-329
    A novel dual-band bandpass filter (DB-BPF) with controllable parameters in design process and a compact structure is introduced in this paper. The total structure includes open-circuited and short circuited coupled-lines, leading to a compact circuit. The resonance frequencies, insertion loss and quality factor can be independently controlled by adjusting the coupled lines. In order to eliminate the magnetic and electric coupling effects, the virtual grounds are placed in coupled complementary hairpin resonator. To verify the validity of the design approach, a DB-BPF centring, at 3.5 and 5 GHz with respective insertion losses of 0.7 and 0.58dB for WIMAX (IEEE 802.16 band) and WLAN (IEEE 802.11 band) applications has been designed and fabricated, whose the measured results confirm the electromagnetic simulation
    Keywords: Micro-Strip Bandpass Filter, Coupled Complementary Hairpin Resonator (C CHR), Admittance Analysis, Two-Band, WLAN Systems, WiMAX Systems
  • H. Ahmadi , A. Rajaei*, M. Nayeripour , M. Ghani Pages 330-341
    Considering the increasing usage of the clean and renewable energies, wind energy has been saliently improved throughout the world as one of the most desired energies. Besides, most power houses and wind turbines work based on the doubly-fed induction generator (DFIG). Based on the structure and the how-ness of DFIG connection to the grid, two cases may decrease the performance of the DFIG. These two cases are known as a fault and a low-voltage in the grid. In the present paper, a hybrid method is proposed based on the multi-objective algorithm of krill and the fuzzy controller to improve the low-voltage ride through (LVRT) and the fault ride through (FRT). In this method, first by using the optimal quantities algorithm, the PI controllers’ coefficients and two variables which are equal to the demagnetize current have been calculated for different conditions of fault and low voltage. Then, these coefficients were given to the fuzzy controller. This controller diagnosed the grid condition based on the stator voltage and then it applied the proper coefficients to the control system regarding the diagnosed condition. To test the proposed method, a DFIG is implemented by taking the best advantages of the proposed method; additionally, the system performance has been tested in fault and low voltage conditions.
    Keywords: Wind Energy, DFIG, Krill Algorithm, Fuzzy Controller, FRT, LVRT
  • A. Azghandi , S. M. Barakati*, B. Wu Pages 342-352
    A voltage source inverter (VSI) is widely used as an interface for distributed generation (DG) systems. However, high-power applications with increasing voltage levels require an extra power converter to reduce costs and complications. Thus, a current source inverter (CSI) is used. This study presents a precise phasor modeling and control details for a VSI-based system for DG and compares it with a CSI-based system. First, the dynamic characteristics of the system based on amplitude-phase transformation are investigated via small signal analysis in the synchronous reference frame. Moreover, the performance of the grid-connected system is determined by adopting the closed-loop control method based on the obtained dynamic model. The control strategies employ an outer active-power loop cascaded with an inner reactive-power loop, which the inner loop is a single-input single-output system without coupling terms. The sensitivity analysis of the linearized model indicates the dynamic features of the system. The simulation results for the different conditions confirm proposed model and design of the controller.
    Keywords: Current Source Inverter (CSI), Distributed Generation (DG), Photovoltaic (PV), Eigenvalue Analysis, Voltage-Source Inverter (VSI)
  • E. Heydari*, M. Rafiee , M. Pichan Pages 353-361
    Among a multitude of diverse control methods proposed for doubly fed induction generator (DFIG) based-wind energy conversion systems, direct power control (DPC) method has demonstrated superior dynamic performance and robustness in presence of disturbances. However, DPC is not a flawless method and shortcomings like necessity for high sampling frequency, high-speed sensors and less noise-affected sampling circuit need to be mitigated by utilizing fuzzy controllers. Parameter setting in a fuzzy controller plays a vital role, especially under non-ideal grid conditions. In this paper, a fuzzy-genetic algorithm-based direct power control (FGA-DPC) method is proposed for DFIG, while, the parameters of the fuzzy controller are optimized by genetic algorithm. The objective of the optimization is to minimize the stator active and reactive power errors to increase the precision of reference tracking. The objectives of the controller are also optimizing active power absorption based on the zone of operation and adjustment of reactive power according to grid requirements. The proposed method improves the overall precision and speed of transient response as well as significantly reducing power oscillations under non-ideal grid conditions. Finally, to demonstrate the effectiveness of the proposed method, extensive simulations are performed in Matlab/Simulink under different conditions.
    Keywords: Wind Energy Conversion System, DFIG, Direct Power Control, FGA-DPC
  • H. Benbouhenni*, Z. Boudjema , A. Belaidi Pages 362-373
    This paper applied second order sliding mode control (SOSMC) strategy using artificial neural network (ANN) on the rotor side converter of a 1.5 MW doubly fed induction generator (DFIG) integrated in a wind turbine system. In this work, the converter is controlled by a neural space vector modulation (NSVM) technique in order to reduce powers ripples and total harmonic distortion (THD) of stator current. The validity of the proposed control technique applied on the DFIG is verified by Matlab/Simulink. The active power, reactive power, torque and stator current are determined and compared with conventional control method. Simulation results presented in this paper shown that the proposed control scheme reduces the THD value and powers ripples compared to traditional control under various operating conditions.
    Keywords: Doubly Fed Induction Generator, Artificial Neural Network, Space Vector Modulation, Neural Space Vector Modulation, Total Harmonic Distortion, Second Order Sliding Mode Control
  • M. Tahmasebipour*, M. Modarres Pages 374-381
    In this paper, a highly sensitive piezoresistive differential pressure microsensor is proposed. This microsensor is consisted of a silicon microcantilever (Length=145 µm; Width=100 µm; Thickness=0.29 µm) and two piezoresistors were mounted (via proper connections) on the microsensor for measuring the created pressure difference. Applying pressure to the microcantilever induces longitudinal and transverse stresses in the piezoresistors, changing their electric resistance and, consequently, the output voltage in the reading circuit of the microsensor. Longitudinal and transverse stresses, different relative sensor resistances resulting from different pressures, voltage variations along the piezoresistors, and microcantilever deflection resulting from different pressures were investigated. To improve the sensor sensitivity, effect of doping concentration, piezoresistors width, and the width of the structure placed under the piezoresistors were studied. In addition, we studied how increasing the width and length of the beam influenced the sensitivity of the sensor. Based on analysis results, the sensor sensitivity was increased from 0.26 W/Pa to 15.78 W/Pa (~60 times). To evaluate the behavior and performance of the proposed microsensor, the following characteristics were analyzed: maximum microcantilever displacement, von Mises stress distribution along the beam and microsensor resistance variations.
    Keywords: Micro-Electromechanical Systems (MEMS), Force Microsensor, Piezoresistive, Finite Element, Von-Mises Stress
  • S. Dolatabadi , S. Tohidi*, S. Ghasemzadeh Pages 382-391
    In this paper, a new active method based on traveling wave theory for islanding detection is presented. A standard power grid that combines a distributed generation source and local loads is used to test the proposed method. Simulations are carried out in MATLAB/Simulink and EMTP/rv which demonstrate fast response and zero non-detection zone (NDZ) of the method along with low perturbation.
    Keywords: Islanding Detection, Microgrid, Traveling Wave Theory, Distributed Generation
  • A. Dameshghi , M. H. Refan * Pages 392-403
    Wind turbines are very important and strategic instruments in energy markets. Wind power production is unreliable. Wind power is weather dependent and the extreme wind speed changes make difficult to control of grid voltage and reactive power. Based on these reasons, Wind Power Prediction (WPP) is important for real applications. In this paper, a new short-term WPP method based on Support Vector Machine (SVM) is proposed. In contrast to physical approaches based on very complex differential equations, the proposed method is based on data history. Firstly, data preprocessing and normalization is done. Secondly, formulate the prediction as a regression problem. Thirdly, the prediction model is constructed using the Particle Swarm Optimization (PSO) and Least Square Support Vector Machine (LSSVM). In this paper, instead of using the conventional kernels, such as linear kernel, Polynomial and Radial basis function (RBF), the Wavelet (W) transform is used. The PSO-LS-WSVM accuracy has been tested with industrial wind energy data. This method has been compared with other methods and the experimental results based on practical data illustrate that PSO-LS-WSVM proposed method has better responses than other methods. Statistical results indicate that the predicting error of PSO-LS-WSVM is 2.98% for one look-ahead hour.
    Keywords: Wind Turbine, Power Curve, SVM, PSO, Wavelet