Graduation Year
2014
Document Type
Thesis
Degree
M.S.E.E.
Degree Name
MS in Electrical Engineering (M.S.E.E.)
Department
Electrical Engineering
Degree Granting Department
Electrical Engineering
Major Professor
Lingling Fan, Ph.D.
Co-Major Professor
Zhixin Miao, Ph.D.
Committee Member
Chung Seop Jeong, Ph.D.
Keywords
D-Q Transform, IPFFT, Measurement Algorithm Design, Power Electronics, Power Injection
Abstract
This thesis studies the techniques of small-signal impedance measurement in three-phase power systems. Stability issue has become critically important since power electronics are highly applied in power distribution and conversion systems. Controlled output systems cause the risk of instability. In order to obtain the impedance model, an impedance extraction in D-Q reference frame algorithm is developed. This paper also applied Interpolated Fast Fourier Transform to increase accuracy of impedance model. Based on the voltage injection, Phase-Locked Loop, Park Transform, D-Q reference frame, and IPFFT. Three-phase system has been realigned on D-Q coordinate and impedance model is extracted in this form.
Firstly, impedance extraction algorithm is designed by MATLAB/Simulink, the algorithm includes PLL, D-Q transform, and IPFFT is used to obtain magnitude and phase angle in frequency domain. Impedance matrices in D-Q frame may be solved through the relation between currents and voltages. Impedance model is made through various tests. Secondly, using the algorithm to test RL circuit to verify with real bode plot of the circuit. Then apply the algorithm on sophisticated circuit model. Finally, implement the algorithm on LabView/Multisim for future hardware tests.
This paper clearly describes the objective of the research, the research problem and approaches, and experiment setup. This paper presents work conducted at the Smart Grid Power Systems Laboratory at University of South Florida.
Scholar Commons Citation
Lin, Jen-Pin, "Impedance Extraction by MATLAB/Simulink and LabView/Multisim" (2014). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/5257