Performance investigation of a permanent magnet generator
Introduction
PM generators have the advantages of eliminating the exciter field winding, slip rings and brushes. The ability of the permanent generator to self excite is an attractive feature that makes it a suitable choice for operation at higher power factors and efficiencies. In addition, PM machines do have the overloading capability and full torque capability at zero and at very low speeds. The understanding of the characteristics and accurate modeling of the dynamic performance of these are of fundamental importance to design engineers. Finite-element analysis has been used extensively for the design and performance prediction of various types of permanent magnet machines [1]. Usually it complements analytical techniques which can provide a rapid and reliable means of design optimization by using adaptation procedures. Equally, simulation for power systems is also widely used to understand the behavior of the machine within the system [2]. Combining these two activities is a desirable goal – the dynamic behavior of the machine affects the system and vice versa. Until recently, the electromagnetic analysis has usually been confined to static representations of the machine geometry using, for example, frequency response methods in synchronous machines [3] or slip frequency analysis in induction motors [4]. This inevitably leads to some inaccuracy in the characterization of the machine. A complete simulation with independent dynamic electromagnetic and power system analysis has been achieved [5], but generally this is too expensive computationally for everyday design use. This paper outlines improvements to the characterization of machine that can be obtained by using dynamic non-linear electromagnetic time-stepping analysis including rotation [6]. Generation of adaptive mesh based upon nodal errors is given by [7] in which next adaptive stages are performed after updating the spacing values using previous adaptive solution. Adaptive analysis with rotation has been carried out by [8] but to take into account the rotor movement, the elements of air gap and rotor have to be modified at each rotor step. A novel algorithm of adaptive mesh generation for the non-linear finite-element analysis of electric machines has also been presented in [9]. A two-dimensional adaptive meshing technique for accurate calculation of very low cogging torque of rotating machines has been presented by [10]. But the study of effect of winding parameters on the torque is missing in all these. Simulations of PM wind turbine generators have been tested for stability by [11] but the analysis of winding parameters on the steady state performance of the generator has not been reported.
Present work has been carried out in two parts. Firstly, FE model of the generator is used to study its mechanical dynamics and subsequent starting torque curve by using FEM-software COMSOL Multiphysics. A finite-element mesh of the model is constructed using first order Lagrange quadratic elements. Finally, the Simulink Parameter Identification software is used for optimization of winding resistance and winding inductance.
Section snippets
Generator model
The centre of the rotor is made up of annealed medium carbon steel, which is a material with high relative permeability. The centre is surrounded with several blocks of permanent magnet made of samarium cobalt, creating a strong magnetic field. The stator is also made up of annealed medium carbon steel. The material in the stator and centre part of the rotor has a non-linear relation between magnetic flux density B and magnetic field intensity H, i.e. the B–H curve. The winding is wound around
Parameter identification process
Parameter identification is a process by which the parameters of a mathematical model may be obtained by comparing the output of the model to corresponding experimental results. The parameter set is adjusted until the output of the model predicts the corresponding results to within acceptable accuracy. Fig. 2 below illustrates this process.
A “Process” is the means by which a set of input result in a repeatable set of measured outputs. The model is then subject to the same set of input as the
Results and discussion
For the PM generator model, the initial mesh obtained, shown in Fig. 3 consists of 3658 elements with 7608 degrees of freedom (DOF). The number of boundary elements in this case is 342. The computed magnetic vector potential (MVP) distribution at t = 2 s of rotation, by using this initial mesh is shown in Fig. 4. On an Intel P4, 3.4 GHz system with 504MB RAM, the time taken for this solution was approx. 190 s. It is apparent from the figure that the solution is erroneous at the edges, as the
Conclusion
This paper presents the magneto mechanical performance analysis of PM magnet generator with combination of finite-element analysis of PM generator using COMSOL Multiphysics, and Simulink. The paper addresses the engineer’s need for system simulation, by proposing a novel interfacing method between COMSOL Multiphysics and Simulink, such that components modeled accurately in FEA can be included in a larger power system model to assess behaviour. In this work, firstly by using FE model, the
Acknowledgements
The authors are highly grateful to the Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India, for allowing the use of FEA package of COMSOL Multiphysics 3.2a, for the execution of above work.
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