High-accuracy electromagnetic design and analysis of electric machines is enhanced by the use of various numerical methods. These methods solve Maxwell’s equations to determine the distribution of the electric and magnetic fields throughout the considered machine structure. Due to the complicated architectures of the machines and the nonlinearity of the utilized magnetic materials, it is a very challenging task to obtain an analytical solution and, in most cases, only a numerical solution is possible.
The finite element method (FEM) is one of the standard numerical methods for electromagnetic field analysis. The considered machine domain is divided into finite elements to which the field equations are applied. FEM solvers are utilized to develop optimization procedures to assist in achieving a design that meets the required specifications without violating the design constraints. The design process of electric machines involves adjusting the machine parameters. This is usually done through experience, intuition, and heuristic approaches using FEM software which gives results for various parameter changes. There is no guarantee that the achieved design is the optimal one.
An alternative approach to the design of electric machines exploits robust gradient-based optimization algorithms that are guaranteed to converge to a locally-optimal model.
The gradient-based approaches utilize the sensitivities of the performance characteristics with respect to the design parameters. These sensitivities are classically calculated using finite difference approximations which require repeated simulations with perturbed parameter values. The cost of evaluating these sensitivities can be significant for a slow finite element simulation or when the number of parameters is large. The adjoint variable method (AVM) offers an alternative approach for efficiently estimating response sensitivities. Using at most one extra not-iterative simulation, the sensitivities of the response to all parameters are estimated.
Here, a MATLAB tool has been developed to automate the design process of switched reluctance motors (SRMs). The tool extracts the mesh data of an initial motor model from a commercial FEM software, JMAG. It then solves for magnetic vector potential throughout the considered SRM domain using FEM taking into consideration the nonlinearity of the magnetic material and the motor dynamic performance. The tool calculates various electromagnetic quantities such as electromagnetic torque, torque ripple, phase flux linkage, x and y components of flux density, air-region stored magnetic energy, phase voltage, and phase dynamic currents.
The tool uses a structural mapping technique to parametrize various design parameters of SRMs. These parameters are back iron thickness, teeth height, pole arc angle, and pole taper angle of both stator and rotor. Moreover, it calculates the sensitivities of various electromagnetic quantities with respect to all these geometric design parameters in addition to the number of turn per phase using the AVM method.
The tool applies interior point optimization algorithm to simultaneously optimize the motor geometry, number of turns per phase, and the drive-circuit control parameters (reference current, and turn-on and turn-off angles) to increase the motor average dynamic torque. It also applies the ON/OFF topology optimization algorithm to optimize the geometries of the stator teeth for proper distribution of the magnetic material to reduce the RMS torque ripple.
A 6/14 SRM has been automatically designed using the developed MATLAB tool to achieve the same performance specifications as 6110E Evergreen surface-mounted PM brushless DC motor which is commercially available for an HVAC system. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24905 |
| Date | January 2019 |
| Creators | Sayed, Ehab |
| Contributors | Bakr, Mohamed, Emadi, Ali, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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