This paper presents a novel way to obtain parameters of synchronous-machine equivalent circuits from standstill frequency response data using a hybrid genetic algorithm. The genetic algorithm is capable of finding a global minimum within a search interval of the fitness function used to match the equivalent circuit and the measured machine transfer functions, notwithstanding the initial guess of the identification process. Therefore, methods such as the Maximum Likelihood Estimation technique, could be substantially enhanced. Results obtained in the identification procedure show that good matching can be obtained with either negative or positive leakage inductance values. These results cast some light on the possible physical meaning that circuit parameters may have. Finite-element modeling is used here to determine the transfer functions of a turbine generator. This approach is consistent with the general aim of obviating the requirement of field testing.

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An efficient two-dimensional finite-element (FE) model is developed for the calculation of synchronous machine transfer functions. The numerical model uses two equivalent sinusoidally distributed stator windings substituting the actual three-phase machine by an equivalent two-phase one, leading to simplified FE simulations. Just two complex solutions per frequency are needed to obtain the three transfer functions that completely describe the two-port nature of the axis network. A new FE-based method is proposed to accurately establish the rotor base quantities, allowing the calculated transfer functions to be in per-unit. Results are validated by comparing the performance of a two-axis equivalent circuit, derived from the FE transfer functions, with a reference transient FE program.