부산대학교 도서관

Numerous physical systems such as transformers, sensors, and biomaterials are analysed using their equivalent ladder network model. Impedance parameters of a ladder network consist of combinations of resistances, inductances, and capacitances. Accurately determining any device impedance parameters directly from the measured time-domain input-output data is challenging. The use of non-linear optimization algorithms does not always work due to the problem being ill-conditioned. Also, the obtained parameters may not be physically realizable. In this work, the problem is approached from a black-box system identification point of view, followed by a physical parameter extraction step. The necessary current and voltage equations obtained from first principles are utilized to build a physical model in the state-space domain. With the use of a subspace system identification algorithm, the black-box parameter matrices are determined from input-output data, which do not correspond to the physical parameter matrices. However, both sets of parameters can be related through a similarity transformation matrix, leading to the extraction of the physical parameters. The novelty of the approach lies in solving the non-linear parameter estimation problem linearly without initialization. The presence of mutual inductances between various transformer winding sections makes its ladder network the most complex one. Therefore, in this paper, the ladder network model of a transformer winding is considered. As an example of the methodology, a six-section transformer ladder network is simulated and the parameter estimation results are used to validate the proposed algorithm. The same approach can be applied to estimate any other device impedance parameters. [ABSTRACT FROM AUTHOR]