A dynamic state estimation method for integrated energy system based on radial basis kernel function - Chen, Tengpeng (2024)
For state estimation (SE) of dynamic electro-thermal gas coupled systems, measurements usually assume that the measurement noise obeys a Gaussian distribution. The extended Kalman filter and the unscented Kalman filter (UKF) are some of the widely used estimation methods in SE. However, the measurement noise does not always follow Gaussian distribution in practice. When the measurement noise is non-Gaussian, the performance of these methods may not be satisfactory. In this paper, we propose an unscented Kalman filtering method based on minimizing radial basis kernel function criterion (MRBFC-UKF), which explores the optimal values of the shape parameters of the kernel function instead of using the widely used Gaussian and exponential kernel functions. Simulations are run dynamically in an integrated energy system which comprises an IEEE 14-bus, a 20-node natural gas network and a 32-node local thermal network. The results show that the proposed MRBFC-UKF method has good robustness and accuracy, and can effectively cope with the presence of unexpected bad data inputs.