Switched reluctance motor (SRM) is operated at high magnetic saturation to generate large torque. The flux linkage of SRM is a nonlinear function with phase current and rotor position because of the high magnetic saturation. Also, the performance of the speed controller for the SRM driver system can be negatively affected by noise, disturbances, and inertia of load torque. Therefore, the fuzzy speed controller for the SRM driver system was developed in this study. In addition, a dynamic model of SRM was simulated in Matlab/Simulink software. Based on the results obtained in this study, the speed of the SRM was controlled over a wide range of speeds including low and high speeds by the fuzzy speed controller. Furthermore, in simulation, the rotor speed was simulated depending on the reference speed. Moreover, the speed of the SRM was experimentally tested using the DS1103 Ace kit. Finally, simulation results were compared with experimental results and they were found to be consistent with each other.
Estimation of dimension parameters for an electrical machine has great importance before manufacturing. For this reason, analytical design should be performed in an optimum form. While motor analysis is accomplished by package programs, initial size parameters are intutivily provided and then various trials are examined to get optimum results. In this study, we are trying to find dimensional and electrical parameters generating mathematical equations in analytic approaches for In-Wheel Switched Reluctance Motor (IW-SRM), which will be employed by Electric Vehicle (EV). Therefore, optimum motor parameters for required speed and torque have been estimated by solving generated equations for in-wheel SRM with 18/12 poles via MATLAB. Using the parameters, analysis of in-wheel SRM has been carried out 3D Finite Element Method (FEM) by Ansoft Maxwell 15.0 Package Software. Consequently, the accuracy of the estimated parameters has been validated by the results of Maxwell 3D FEM.
The sensorless vector control of a nonsinusoidal flux-distribution permanent magnet synchronous motor (PMSM) has been performed by a trained artificial neural network (ANN) using flux data obtained from the finite element method (FEM). A more sensitive rotor position has been estimated by using the fluxes of each of the three phases of the PMSM. In the proposed approach, magnet flux of the nonsinusoidal PMSM has been calculated by FEM for every single degree. Rotor position and speed values have been estimated by training an ANN with this information. The experimental results obtained by DS1103 development kit have proved the validity of the proposed approach.
Solar energy is a
clean energy and it is increasingly being used all over the world. In this
study, a water pump drive has been designed with a switched reluctance motor
(SRM) to be powered by a photovoltaic energy source (PV). A boost converter has
been used to increase the voltage of the PV source. The mathematical model of
the system has been composed in the Matlab/Simulink and simulation results have
been obtained. Output voltage of the boost converter has been controlled by
using a PID controller at 50 kHz switching frequency. A centrifugal pump has
been used as the water pump. In the control of boost converter, an eight bit
PIC16F877A microcontroller has been used. The electrical energy obtained by PV
panels has been stored in batteries. Battery charge testers have been used in
order to control the battery charge level. The speed and current control of SRM
has been done by using DS1103 Ace kit. The value of the current has been
perceived by using hall sensors and transferred to the digital signal processor
by using an analog to digital converter. The accuracy of simulation results has
been proved by experimental results.
scite is a Brooklyn-based startup that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.