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Detection of Broken Bars in Induction Motors, Using Neural Network
M. Moradiyan,M. Ebrahimi,M. Danesh,M. baiat 전력전자학회 2004 ICPE(ISPE)논문집 Vol.- No.-
This paper presents a method based on neural network to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic model of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situation or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transform and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been<br/> tested with a different amount of load torque, and it is capable<br/> of working on-line and of recognizing all normal and ill conditions.
Damping the Oscillation in an HVDC/HVAC System with a Novel PSS Controller
M. Moradiyan,M. Bayati Poudeh,S. Eshtehardiha 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper to damp and control the oscillations in the model system which includes two ties (AC and DC), after any change, a new controller was employed. This controller as a damper can decrease the rotor angle deviations in a short time. This hybrid power system stabilizer use the rotor speed oscillations, capacitor voltage deviations and rotor angle changes as sampling parameters and control the exciter voltage and HVDC’ rectifier control signal. This process is applied with genetic algorithm helps and genetic algorithm is employed to find the best values for gains of the controller in a very short time. The simulation results show the improvement in the dynamic performance of the test AC/DC system.
M. Bayati Poudeh,S. Eshtehardiha,M. Moradiyan 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
STATic synchronous COMpensator (STATCOM) is a device capable of solving the power quality problems at power systems. These problems happen in milliseconds and because of the time limitation; it requires the STATCOM to have continuous reactive power control with fast response. Therefore optimal exploitation of STATCOM by classical controllers has been a controversial issue in recent years. The most common controlling devices in the market are Pole Placement (PP). In this article, the STATCOM is controlled by Pole Placement controller. A new control method that is Model Reference Adaptive Control method (MRAC) based on the combination of Pole Placement control and the Genetic Algorithm (GA) is introduced. Genetic algorithm is employed to find the best values for location of poles controllers" parameters in a very short time. The simulation results show an improvement in current control response.
S. Eshtehardiha,M. Bayati Poudeh,S. A, Emami,M. Moradiyan 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper, two different control methods on DC-DC Converter are compared with together. These converters are used for the stabilization or the control of DC voltage of a battery. In addition to other applications, DC converters feed electric vehicles (trucks, electric vehicles, and subway locomotives), telephone sets and civil inverters. Lately, improvement the performance of the DC-DC converter is one of the goals of the engineers in the industries. In this way, several control methods are used to control Buck converters. In this paper, Linear Quadratic Regulator controller is used to optimize the DC-DC converter performance. Also other controller, by the way, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used with these control methods, to obtain the best coefficients in them. The results are shown the capability of the control methods in the improvement of the above-mentioned converter functioning.
An Adaptive Neuro-Fuzzy Controller for DC-DC Converter
S. A, Emami,M. Bayati Poudeh,S. Eshtehardiha,M. Moradiyan 제어로봇시스템학회 2008 제어로봇시스템학회 국제학술대회 논문집 Vol.2008 No.10
In this paper an Adaptive Fuzzy controller is designed for controlling a DC-DC Buck converter. In order to make the controller adaptive and overcome the variations of the input voltage, an Artificial Neural Network is applied to the Fuzzy controller and tuned its coefficients online with system input ripples and variations. Neural Network is trained by direct searching method through successive simulations to optimize fuzzy coefficients versus increasing input voltages. These coefficients and related input voltages constitute the set of training data for artificial neural network which is applied offline by Lauvenberg-Marcoardet method. The resulting Neuro-Fuzzy controller, shows a very good behavior in controlling the output voltage and the simulation results reflect improvement of the system response.