This paper describes a fitting algorithm suitable for simultaneously approximating the real and imaginary parts of transformer admittance curves. The algorithm follows a unique strategy to determine the best initial guess. It optimizes the parameters one group at a time. This technique allows the fitting routine to find the best solution even when the number of optimization parameters is large. Since the optimization of each group of parameters is well controlled at each stage, the algorithm is suitable for constrained optimization. Another advantage is that the starting point for each stage can be very simple. Examples demonstrate that the algorithm produces good results for a variety of transformer admittance curves. © 2005 IEEE.
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