R. Gobbi

Experimental Investigations on Computer-Based Methods for Determination of Static Electromagnetic Characteristics of Switched Reluctance Motors

Because of the doubly salient structure of the switched reluctance motor (SRM) and its intentional operation in deep magnetic saturation for higher power density, its static electromagnetic characteristics are highly nonlinear functions of rotor position and phase current. This makes the accurate experimental measurement/determination of these characteristics a difficult task. This paper presents a comprehensive discussion and analysis on the different (most practiced) computer-based methods for the determination of these characteristics for a typical SRM. A digital signal processor (DSP)-based completely automated SRM drive system has been used for these studies. For all the offline computations, user-friendly MATLAB/Simulink-based models have been developed. The experimental methods, computational models, measurement results, and appropriate postmortem discussions for the determination of static flux linkage, inductance, and electromagnetic torque characteristics for an 8/6 four-phase SRM are reported.

Rising and Falling Current Methods for Measurement of Flux-Linkage Characteristics of Switched Reluctance Motors: A Comparative Study

Switched reluctance motor has a doubly salient structure, where flux linkage is dependent on both rotor position and phase current. Hence, measurement of flux-linkage characteristics is difficult compared to other motors. One popular method is the DC-excitation method, where flux-linkage can be computed by first measuring phase voltage and current when the current is rising/falling while a fixed DC/zero voltage is applied to a phase winding with rotor being locked at a specific position, and, later, by numeric integration of induced phase voltage determined from Faradays Law. This paper reports a comparative evaluation of results from rising and falling current methods both under static and dynamic conditions. All the calculations are based on developed MATLAB/SIMULINK computational model. The measurement procedures are explained in detail.

Fuzzy Iterative Technique for Torque Ripple Minimization in Switched Reluctance Motors

Abstract The switched reluctance motor has many benefits owing to its low cost, simple and rugged construction, and comparatively high torque-to-mass ratio. The main disadvantage of the motor is that it produces high torque ripples. Torque control is a difficult task since the switched reluctance motor is intentionally operated in deep magnetic saturation to increase the output power density. A new method, called the fuzzy iterative technique, is introduced in this work to modulate the phase current profile to minimize the torque ripples. The experimental results indicate that the torque ripple is reduced to lie within 5% of the desired steady torque even in dynamic operating conditions.

Voltage injection switching inductor (VISI) method for fast transient response in switch mode power supplies

A fast transient response to a step load change is very essential in a switch mode power supply. The transient response can be improved by means of feedback control method. There are two feedback control methods widely used for SMPS, they are voltage mode control (VMC) and peak current mode control (PCMC). A simulation study on these feedback control methods has been done using Matlab/Simulink. It is found that the PCMC helps the SMPS for a faster transient response compared to the VMC. However, there are a few disadvantages of the PCMC which make it not possible for further transient response improvement. Hence, in this paper a novel feedback control technique is presented and it is called voltage injection switching inductor (VISI). This feedback control method provides a better transient response compared to PCMC. Detailed explanations of the novel method and simulation results are presented in this paper. The simulation results show improvement in terms of fast transient response.

Practical current control techniques for torque ripple minimization in SR motors

The switched reluctance motor (SRM) has many benefits owing to its low cost, simple and rugged construction, and comparatively high torque-to-mass ratio. Because of the saturation effect and the variation of magnetic reluctance with respect to rotor position, all the relevant characteristics of the machine are highly nonlinear functions of both rotor position and phase current. The ultimate outcome of all these nonlinearities is that the generated torque contains significant ripples. The nonlinearities in SRM have been extensively studied and many control strategies to reduce the generated torque ripples have been proposed in the literatures. However, the problem of reduction of generated torque ripple still remains an active area of research especially for low speed applications. A new method called fuzzy iterative approach (FIA) is introduced in this work to modulate the phase current profile. FIA uses a multiplying factor in the conventional current calculation method, and a fuzzy rule base determines this multiplying factor by a heuristic judgment iteratively to reduce the torque ripple to zero. The modulated phase current for zero torque ripples is stored in a look-up table, which is utilized for practical implementation. Experimental verification under closed-loop speed control with the stored reference current data has been done. Various current control techniques to track the reference current closely to minimize the torque ripple are investigated and explained in this paper. The experimental results indicate that the torque ripple is reduced to lie within 5% of the desired steady torque.

The measurement of mechanical parameters of a switched reluctance motor drive system

The switched reluctance motor has been receiving attention recently after the availability of power electronics devices, for its robustness, simple construction and a high torque/mass ratio. The electromagnetic characteristic measurement for the motor has been widely researched and reported despite nonlinear behavior of the motor. The electromagnetic characteristic data are being used as look-up tables for modeling and simulation purposes. However, for dynamic simulation, mechanical parameters such as friction coefficients and moment of inertia are equally important. There has not been much work done to measure the parameters as mostly they are provided by the manufacturer of mechanical parts (the motor etc) which are not accurate for the entire mechanical system, when they are coupled. A few measurement methods have been used widely to measure the mechanical parameters but the important friction constants such as Coulomb friction constant were neglected. This paper introduces a new technique which was derived from the classical viscous plus Coulomb friction model for all the mechanical parameter measurements including the Coulomb friction. The results obtained were verified using experimental data.

Using PSpice for Teaching Output Current Control in D.C.-A.C. Inverters to Undergraduate Students

In the present day technology of power semiconductor drives, d.c.-a.c. switch-mode voltage source inverters are widely used in a.c. motor servo drives. In these applications, the motor phase currents need to be controlled for high performance. Thus, the prime control issue is to obtain the switching signals for the inverter switches in order to control/regulate the inverter output current. There are various standard ways to derive the switching signals for the inverter. Undergraduate students of electrical engineering should know these basics and get hands-on experience of the inverter current control techniques while taking a course on Power Electronics and Drives. This paper discusses an efficient way of teaching current control in d.c.-a.c. voltage source inverters by computer simulation studies using the student version of PSpice (Release 9.1). Three standard methods have been demonstrated, i.e., triangular carrier method, hysteresis band method, and periodic sampling method. This paper can be used by students for self-study as well as by instructors for simulation-based laboratory experiments.

Mathematical Modeling of Flux-Linkage Characteristics of Switched Reluctance Motors Using Polynomial Neural Networks

Switched reluctance motor (SRM), built using revolutionary concept, has highly nonlinear flux-linkage characteristics depending heavily on phase current and rotor position. A good mathematical model for these characteristics would help to understand the workings of the motor; thus providing path for better control algorithms and motor designs. It is very much proven by many researchers in this area, that a straightforward simple mathematical model has never satisfied the complete overall characteristics. Moreover, there is no distinct guideline about what sort of mathematical model would be suitable. To overcome this problem, a self-organizing polynomial neural network is proposed in this paper. In this scheme, without any prior knowledge of the mathematical model, the model is evolved iteratively and progressively. The simulation test results verify the effectiveness of this approach.

An investigation for closed loop control of single phase induction motors using industrial components

This paper deals with the monitoring, control and protection of a single phase induction motor. The power to the motor is controlled using a triac. In this work, the potentiometer which determines firing angle to the triac is controlled using a stepper motor. The system provides a simple yet a practical technique for a closed loop system. LabVIEW was used for system monitoring and industrial components such as programmable logic controller, encoders etc were used to verify the system experimentally.

Polynomial neural network based modeling of Switched Reluctance Motors

Switched reluctance motor (SRM) has double salient structure which makes its magnetic characteristics; i.e. flux linkage and torque to be a nonlinear function of stator current and rotor position. For this reason, modeling and control of the SRM is by no means a trivial task. It was proven by many researchers in this area, that a simple mathematical model has never able to represent the complete overall magnetic characteristics. Moreover, there is no distinct guideline about what sort of mathematical model would be suitable. To overcome this modeling problem, a self-organizing polynomial neural network is projected in this paper. With this scheme incorporated, the model is let to evolve iteratively and progressively without any prior knowledge of the plant. Subsequently, MATLAB/SIMULINK is used to model the SRM drive system. Finally, experimental results for both static and dynamic conditions are presented.