Showing 6 results for Tohidi
R. Pour Ebrahim, S. Tohidi, A. Younesi,
Volume 14, Issue 1 (March 2018)
Abstract
In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic control method is applied in the estimator structure instead of PI controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy controller shows more robustness against the variations of machine parameters.
A. Younesi, S. Tohidi, M. R. Feyzi,
Volume 14, Issue 3 (September 2018)
Abstract
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be implemented in real time. In order to solve this problem, this paper presents an improved strategy in the field of nonlinear MPC (NMPC) of the permanent magnet synchronous motor (PMSM). The proposed method applies a sequence of reduction weighting coefficients in the cost function, over the prediction horizon. By using the proposed strategy, NMPC give a more accurate response with less number of prediction horizon. This means the computational time is reduced. It also suggests using an incremental algorithm to reduce the computational time. Performance of the proposed Nonlinear MPC (NMPC) scheme is compared with the previous NMPC methods via simulations performed by MATLAB/Simulink software, in permanent magnet synchronous motor drive system. The results show that the use of proposed structure not only lowers prediction horizon and hence computational time, but also it improves speed tracking performance and reduces electromagnetic torque ripple. In addition, using the incremental algorithm also reduces the computational time which makes it suitable for real-time applications.
S. Dolatabadi, S. Tohidi, S. Ghasemzadeh,
Volume 14, Issue 4 (December 2018)
Abstract
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.
B. Mamipour Matanag, N. Rostami, S. Tohidi,
Volume 17, Issue 2 (June 2021)
Abstract
This paper proposes a new method for direct control of active power and stator flux of permanent magnet synchronous generator (PMSG) used in the wind power generation system. Active power and stator flux are controlled by the proposed discrete time algorithm. Despite the commonly used vector control methods, there is no need for inner current control loops. To decrease the errors between reference and measured values of active power and stator flux, the space vector modulation (SVM) is used, which results in a constant switching frequency. Compared to vector control, the proposed direct control method has advantages such as higher dynamic response due to elimination of inner current control loops and no need to coordinate system transformation blocks as well as the PI controllers and their adjustment. Moreover, permanent magnet flux vector and several machine parameters such as stator inductances are not required which can improve the robustness of the control system. The proposed method can be used in both types of surface-mounted and interior PMSGs. The effectiveness of the proposed method in comparison to the vector control method with optimized PI coefficients by the particle swarm algorithm is evaluated. Simulation results performed in MATLAB/Simulink software show that higher dynamic response with lower active power and the stator flux ripple are achieved with the proposed method.
K. Zarrinnegar, S. Tohidi, M. R. Mosavi, A. Sadr, D. M. de Andrés,
Volume 19, Issue 1 (March 2023)
Abstract
The Global Positioning System (GPS) is vulnerable to various deliberate and unintentional interferences. Therefore, identifying and coping with various interferences in this system is essential. This paper analyzes a method of reducing the dimensions of Cross Ambiguity Function (CAF) images in improving the identification of spoofing interference at the GPS using Multi-Layer Perceptron Neural Network (MLP NN) and Convolutional Neural Network (CNN). Using the proposed method reduces data complexity, which can reduce the number of learning data requirements. The simulation results indicate that, by applying the proposed image processing algorithm for different dimensions of CAF images, the CNN performs better than MLP NN in terms of training accuracy; the MLP NN is superior to CNN in terms of convergence speed of training. In addition, the results demonstrate that the operation of the proposed method is appropriate in the case of small-delay spoofed signals. Therefore, for the intervals above 0.25 code chip, the proposed method detects spoofing attacks with a correct detection probability close to one.
M. J. Jahantab, S. Tohidi, Mohammad Reza Mosavi, Ahmad Ayatollahi,
Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)
Abstract
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
