Advances in electric machine modeling and evolutionary parameter identification

Dionysios C Aliprantis, Purdue University

Abstract

The design of robust power systems entails extensive use of computer simulations, increasing the demand for high-fidelity electric machine models. In the present study, new dynamic models for induction machines, synchronous machines, and brushless excitation systems are set forth. The models are derived within the orthogonal qd-axes theoretical framework. Their formulation is geared towards reflecting the machines' actual operating characteristics, in contrast to conventional models that utilize pre-determined equivalent circuit structures of questionable physical meaning. The proposed induction machine model is developed for power-electronics based applications, where the high-frequency interaction between converter and machine is of particular interest. It represents magnetic saturation in the main and leakage flux paths, and uses an arbitrary linear network to capture the frequency dependence of the rotor circuits. The proposed synchronous machine model is applicable to power system stability studies. It similarly addresses magnetic saturation as well as equivalent circuit issues. The proposed brushless excitation model features an average-value representation of the exciter-machine/rotating-rectifier configuration, and the incorporation of magnetic hysteresis. Novel experimental procedures are devised for characterizing the proposed models which utilize evolutionary optimization techniques as a means for parameter estimation. The models are validated by comparison to experimental results. ^

Degree

Ph.D.

Advisors

Major Professor: Scott D. Sudhoff, Purdue University.

Subject Area

Engineering, Electronics and Electrical

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