Date of Award
Electrical and Computer Engineering
Kar, Narayan (Electrical and Computer Engineering)
CC BY-NC-ND 4.0
Magnetization in the ferromagnetic core significantly affects the performance of electrical machines. In the performance analysis of electrical machines, an accurate representation of the magnetization characteristics in the machine model is important. As a part of this research work, two new mathematical models are proposed to represent the magnetization characteristics of electrical machines based on the measured magnetization characteristics data points. These models can be applied to various kinds and sizes of electrical machines. The calculated results demonstrate the effectiveness of the proposed models. The comparison analyses on the proposed models and three different existing models which have been used in the literature by the researchers validate the fact that these models can be used as proper alternative for the other models. Inasmuch as the omission of magnetization in the machine model has a negative impact on the analysis results, integrating the proposed magnetization models into the synchronous machine mode, can better describe the machine behavior. To aim this goal, as a part of this research, the proposed magnetization models are incorporated to the transient and steady state synchronous machine models. The trigonometric model developed in this work, has been applied to a conventional synchronous machine model and extensive stability performance analysis has been carried out. This further reveals the usefulness of the proposed trigonometric magnetization model and the importance of the inclusion of magnetization in stability analysis. The other magnetization model developed in this research is incorporated into a state space synchronous machine model that is used in steady state performance analysis of the machine.
Hamidifar, Saeedeh, "Regression Function Characterization of Synchronous Machine Magnetization and Its Impact on Machine Stability Analysis" (2012). Electronic Theses and Dissertations. 431.