Switched Reluctance Motor Drives

An asymmetrical bridge meets this goal because the current in the winding always has the same direction. In this method, the first signal is the output of the current controller and the PWM generator. A numerical simulation environment has been developed in Simulink/Matlab® (version R2018a) to demonstrate the fundamental control of a switched reluctance motor, which is available at the link: https://bit.ly/2KfPUsG.

For the 4 Nm load, it is necessary to have more current in the phase transition due to the SRM torque characteristic, so during this period the torque has a low value. Different types of controllers can be used - the fundamental ones are voltage pulse, current and torque controllers. Optimization and evaluation of torque sharing functions for minimizing torque ripples in switched reluctance motor drives.

High-efficiency single-pulse switched-reluctance motor drive for high speed (48k RPM) application: analysis, design and experimental verification. Switched Reluctance Machines (SRMs) have a double salient structure meaning that both the stator and rotor have salient poles.

Introduction

This chapter focuses on SRM characterization procedure, highlighting the non-linear characteristics and the importance of the magnetization curves to achieve precise automatic control of SRM. The calculation algorithm used to process the obtained data is presented with the aim of explaining the production of the lookup tables used in the mathematical model. To study SRMs, it is essential to use models that accurately incorporate the nonlinear properties and quantity behavior of the system to be simulated.

The curves express the nonlinear behavior of the SRM and can be used to relate the input voltage (VDC), electric current (I), rotor position (θ) and magnetic flux (ϕ) to the electromagnetic torque (Te) [6, 7]. Methods for obtaining magnetization curves are currently divided into direct and indirect methods. The number of curves used to create the model is a determining factor in the accuracy of the model.

The number of experimentally obtained curves is limited by the accuracy and time of the test. Finally, a brief statistical analysis is used to assess the accuracy of the established mathematical model.

Mathematical modeling of switched reluctance machines

Nonlinear characteristics of switched reluctance machines
Magnetization curves
Automatic characterization system
Switched reluctance machine mathematical model
Processing the acquired data

On the other hand, direct methods consist of applying voltage to the SRM phases and determining the magnetic flux [8–11]. With the accepted assumptions, a basic single-phase SRM circuit can be derived as shown in Figure 1. The sum of the resistor voltage drop and the rate of change of the flux linkage must equal the applied voltage per motor phase; so Eq. 1) can be derived where V is the voltage applied to the phase and i is the phase current, Fig. The characteristics of magnetic flux versus electric current are known as magnetization curves and can be used to model SRM dynamics.

Now assume that the rotor position is driven to the second intermediate angle θ2 and the current is maintainedI1, as in Figure 2(c). The direct methods, conversely, consist of applying voltage to the SRM winding and determining the magnetic flux. Next, the DSP is commanded to calibrate the encoder position according to the rotor position.

In an SRM, for a constant current applied to the phase winding, when the rotor is in the aligned position (θ= 0°), the resistance of the magnetic circuit is minimum, so the magnetic flux is maximum. This undesirable phenomenon can be accentuated by the variation of the magnetic flux for a given current when the rotor is close to aligned and.

Figure 5 shows the assembled system for automatic characterization, wherein an industrial SRM was subjected to the test

Model implementation

Different interpolation methods lead to slight inhomogeneities in the inductance profile which can cause large differences between the model and the actual system behavior. Given the relationship between the produced torque and the inductance derivative, the inductance profile generated may deviate from the actual profile and cause divergences in the torque lookup table. For example, if linear interpolation is used, the inductance profile will be constructed as the junction of lines with different slopes, specifically in the region bounded by the experimentally obtained points.

This will cause the torque LUT to present subtle differences in the returned torque depending on the position. Therefore, the most suitable technique for modeling the operation of SRMs is smoothing joints, provided that there are no disturbances or unexpected behavior in the inductance and torque. a) magnetization curves estimated on the basis of the obtained data; (b) estimated coenergy. The T I,ð θÞLUT surface obtained by interpolation of the smoothing patch is presented in Figure 13; note the smooth transition between negative and positive torque in the table.

The total electromagnetic torque is obtained as the sum of the torque produced by each phase. The output currents from the IðΦ,θÞLUT are generated at the model terminals using current-controlled sources.

Model validation

Motor operation-speed control

The rotor position is also obtained by integrating the SRM rotational speed so that the angles of each phase can be calculated. In the first system, the SRM works like a motor and speed control is implemented. In the second system implemented, the SRM acts as a generator and regulates the DC link voltage.

The simulations must be discretized to accurately represent the experimental behavior, and the discretized control algorithm is embedded in the DSP. For both the experiment and the simulation, an asymmetric half-bridge (AHB) converter is used to drive the SRM in motor mode. Two simulations are performed to study the behavior of the model at below-base and above-base speeds.

For the first simulation setup, called S1, the reference speed is set to 30 rad=s for simulation under base speeds, and the current and trip angle are controlled. Some minor differences occur due to other system parameters such as power switches and data sampling period.

Figure 16 depicts the experimental setup. There are labels indicating the com- com-ponents in the setup

Generator operation-voltage control

Comparing the waveforms from S1 and E1, it can be seen that the pattern is stable for rates below baseline. The SRG feeds a resistive load and the controller is responsible for maintaining the bus voltage at the reference value Vref. The DC bus voltage, turn-off angle (θoff) and phase A current are shown in Figure 27 at the moment of load change.

To operate the SRG in self-excited mode, an initial magnetization is required, which is provided by a 2250μF capacitor at the DC bus. Note that the control acts on the off angle and increases the SRG excitation voltage to supply the energy to be generated thus. To evaluate the accuracy of the obtained model, statistical parameters were calculated, such as root mean square error (RMSE), mean absolute error (MAE), sum of square error (SSE) and R-square (R2).

The relative errors RMSE and MAE for the SRG electric current are defined as the equations where IexpandIsim is the measured and simulated SRG phase currents, is the number of data points, and the relative errors and their average value, respectively:. Note that the model obtained with the instruments developed and using smoothing splines accurately describes the nonlinear magnetic characteristics of the SRM.

Conclusions

With the curves obtained, it is possible to synthesize a nonlinear model consistent with the real behavior of the SRM. For the motor mode, a speed control with variable angles has been developed, while for the generator mode, the voltage control has been made. The results support the similarity between the model and real SRM behavior in both motor and generative modes.

This is only possible because the interpolation method used, such as patch smoothing, returns a smooth curve at the boundaries.

Appendix

Procedure to obtain the magnetization curves from the blocked rotor test data

Data processing to obtain the lookup tables

6% This algorithm receives data from magnetization curve data and calculates I (theta, flux), T (I, Theta), and I (T, Theta) look-up tables (LUTs). 13% These functions transfer magnetization curve data according to the position obtained from the locked rotor test. 23 TMax2 = 20; % Maximum desired torque for the model 24 %% Initialize the vectors that will contain the curves 25 flux_i_thetaCte = zeros(Ncurves,Npoints);.

81% Calculate the phi(theta) functions for each current in the table and determine the flux using polynomial fitting. Calculate I (theta,phi) and fit the I(phi,theta) and I(theta,phi) tables. Calculates the I(phi) curves for all positions and goes through half of the total N to arrive at position 22.5 degrees.

Switched reluctance motor parameters

Basics of switched-reluctance machines

Machine structures
Governing equations
Motor and generator operation modes
Dynamic models
SRM converters
Equivalent circuit parameter estimation and performance test of SRM drive Figure 6 shows the suggested test facilities for establishing an SRM drive

Possible front-end converters

DC/DC front-end converters
AC/DC front-end converters

Some key issues of SRM and SRG
Example SRG system: a grid-connected SRG-based microgrid

System configuration
Some experimental results .1 SRG-based microgrid

Example SRM drive: a battery-/SC-powered EV SRM drive

System configuration
Some experimental results .1 Winding current responses

Conclusions
Torque production in switched reluctance machines
Average torque control

Average torque estimation
Direct average torque control

Instantaneous torque control

Instantaneous torque estimation
Current profiling technique through torque sharing functions
Direct torque control
Direct instantaneous torque control

Results and discussion
Conclusions
General analytic theory for saturable switched reluctance machines In this chapter we set out the proposed nonlinear theory based on vector analysis

Switched reluctance machine energy conversion estimation and flux-linkage map construction
Saturable switched reluctance machine energy conversion ratio estimation Since the SR machine requires a DC bus capacitor if the DC supply voltage is to
Saturable switched reluctance machine average rated current and voltage estimation
Saturable switched reluctance machine converter volt-ampere requirement estimation
Saturable switched reluctance machine generator mode energy conversion estimation

Conclusions
Operation of the LSRA
Modal analysis via the finite element method

Dynamic analysis for multiple degrees of freedom systems
Modal analysis via the finite element method
Results from FEM simulations
Experimental modal excitation

Operational modal responses

Experimental setup and instrumentation
Analysis of stationary and nonstationary signals
Software tools for signal analysis and representation

Experimental characterization and results of local responses

Results from experimental modal excitation
Characterization for operational modal excitation

Spectral analysis of the audio emitted
Conclusion
Electromagnetic conversion principles
Dynamic model
Control

Hysteresis control
Proportional integral (PI) control

Practical case study scenario

Point absorber mathematical model
System simulation/simulation results

Conclusion

And Figure 17(b) plots the measured output voltage vdc, DC link current id, and inductor currents (iL1, iL2) of the interleaved converter. The proposed control scheme of the developed SRM drive shown in Figure 22 (b) consists of the outer speed loop, the inner current loop, and a dynamic commutation tuning (DCT) scheme. However, as in the case of the ATC, measuring the instantaneous torque can be expensive.

Phase current profiles in the phase windings due to the voltage switching strategy of the asymmetric h-bridge converter. The rotor position unaligned flux coupling is fairly linear for the entire range of phase current values, especially when compared to the full. The torque production of the SR machine can be described in terms of the flux-linkage map shown in Fig. 6a.

Profile of the phase current in the phase windings as a function of voltage and rotor position angles. Profile of the phase current in the phase windings as a function of voltage and rotor position angles. Due to the switching process, an induced electromagnetic force (emf) appears at the terminals of the phase coil.

The mathematical model of the switched reluctance generator is the result of the analysis of the associated power converter.