Dynamic State Estimation of Integrated Electric and Gas Energy Systems Considering Channel Fading

For the state estimation problem of integrated electric and gas energy systems (IEGS), a class of recursive filtering algorithm considering channel fading and nonlinear measurements is investigated to mitigate the sensitivity of state estimation accuracy to fading measurement data and linearization errors. First, a suitable state prediction equation and a measurement fading model represented by the random parameter matrix are established. Unlike the traditional approximation method that ignores the linearization error, the linearization error is treated as a norm bounded uncertainty matrix for enhancing the estimation precision. Then, the upper boundary of the estimated error covariance is derived and is minimized for every sampling moment to obtain the optimal filtering gain. The required filtering parameters are obtained through solving two recursive form difference equations. Finally, the accuracy of proposed dynamic filtering scheme is verified through simulation examples.