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Heffron-Phillips model of a synchronous machine is commonly used in small signal stability analysis and for off-line design of power system stabilisers. The data used to determine the parameters of this model are either hard to measure or require the machine to be taken off-line to take the measurements which, in general, is inconvenient. identifying these parameters from online data measurements is important since it does not require any a priori knowledge of the machine data. the problem of closed-loop identification of the Heffron-Phillips model parameters is of practical importance since the data used for identification can be gathered when the machine is normally connected to the power system. the use of open-loop identification techniques using data gathered during closed-loop operation of synchronous generators leads to bias errors in the estimated parameters. motivated by the fact that the synchronous machine model is multivariable and is well defined in a state space structure, a closed-loop subspace parameter identification technique is proposed. consistency of the proposed approach is illustrated using Monte Carlo analysis. comparison of the proposed method with open-loop identification technique shows the superiority of this approach.