Sensor measurements are mission-critical for monitoring and controlling power systems because they provide real-time insight into the grid operating condition; however, confidence in these insights depends greatly on the quality of the sensor data. Uncertainty in sensor measurements is an intrinsic aspect of the measurement process. In this paper, we develop an analytical method to quantify the impact of measurement uncertainties in numerical methods that employ the Koopman operator to identify nonlinear dynamics based on recorded data. In particular, we quantify the confidence interval of each element in the push-forward matrix from which a subset of the Koopman operator's discrete spectrum is estimated. We provide a detailed numerical analysis of the developed method applied to numerical simulations and field data collected from experiments conducted in a megawatt-scale facility at the National Renewable Energy Laboratory.

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