Adaptive Decentralized State Estimation for Multimachine Power Grids Under Measurement Noises With Unknown Statistics

Abstract

This article is concerned with the adaptive dynamic state estimation (DSE) problem for synchronous-generator-based multimachine power grids under measurement noise with unknown statistics. The statistical properties of the measurement noises are efficiently revealed by utilizing limited measurement data contained in a sliding window, and such data is employed to establish the base distribution of the noises, with the aid of the Gaussian mixture model and the kernel density estimation scheme. Subsequently, the component number of the base distribution of the measurement noises is reduced by designing a fuzzy C-means clustering algorithm with the Wasserstein distance criterion. An improved sliding-window-based adaptive cubature Kalman filtering scheme is then proposed, which leverages the already obtained statistical characteristics of the measurement noise and the concept of the Gaussian summation filter. Finally, the validity of the proposed adaptive DSE algorithm under various measurement noise statistics is illustrated by simulation studies conducted on the IEEE 39-bus system featuring three test scenarios.