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In today's power system where the share of renewables is rapidly increasing, the system now exhibits a more dynamic behavior compared to the past. Therefore, the importance of dynamic simulations at every level of the power system is crucial for the system operators. However, calibration of model parameters and their regular controlling are required to simulate the real-life behavior of the system correctly. This paper aims to improve the dynamic simulations by calibrating the parameters of the Type 4 wind turbine generator model. The employed method uses an ensemble Kalman filter to estimate the model states and calibrate parameters. For the simulation environment, SIEMENS PSS®E software (v35.5) and its PythonTM API are utilized. After the sensitivity and collinearity analyses, during the transient event, the erroneous model parameters are calibrated. [ABSTRACT FROM AUTHOR]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]