  The reliability and precision of dynamic database are vital for the optimal
operating and global control of integrated energy systems. One of the effective
ways to obtain the accurate states is state estimations. A novel robust dynamic
state estimation methodology for integrated natural gas and electric power
systems is proposed based on Kalman filter. To take full advantage of
measurement redundancies and predictions for enhancing the estimating accuracy,
the dynamic state estimation model coupling gas and power systems by gas
turbine units is established. The exponential smoothing technique and gas
physical model are integrated in Kalman filter. Additionally, the time-varying
scalar matrix is proposed to conquer bad data in Kalman filter algorithm. The
proposed method is applied to an integrated gas and power systems formed by
GasLib-40 and IEEE 39-bus system with five gas turbine units. The simulating
results show that the method can obtain the accurate dynamic states under three
different measurement error conditions, and the filtering performance are
better than separate estimation methods. Additionally, the proposed method is
robust when the measurements experience bad data.
