Direct continuous-time parameter identification of excitation system with the generator online

In this paper, a direct continuous-time approach for parameter identification of excitation system is proposed. The ST1A block diagram of excitation system as the most identical model to our excitation system is presented. To achieve order estimation and parameter identification of the model, both direct and indirect identification methods are applied and compared. Both approaches are based on Prediction Error Method. In the indirect approach, ARX method is employed to initialize identification of system parameters. In order to update parameters, nonlinear least square method is utilized. On the other hand, three different initialization identification algorithms including SRIVC, SVF and GPMF are used in the direct method. To achieve the minimal norm of prediction error, the estimation of the parameters' initial values is updated using nonlinear least square methods. The stability of estimated parameters is studied using identifiability analysis. An experimental study is performed to evaluate the effectiveness of the proposed direct continuous-time approach.