Recursive Unscented Kalman Filtering for Power Distribution Networks Under Hybrid Attacks: Tackling Dynamic Quantization Effects

This paper investigates the state estimation problem for power distribution networks subject to dynamic quantization effects and hybrid cyber-attacks, where measurement signals are transmitted from sensors to a remote filter via open digital communication networks. To enhance bandwidth utilization and ensure reliable data transmission, a dynamic quantization mechanism is introduced, which effectively accommodates the dynamic characteristics of power signals. Furthermore, the system is vulnerable to hybrid cyber-attacks that may occur simultaneously in a random manner, including denial-of-service attacks and false data injection attacks, characterized by Bernoulli distributed random variables. The primary objective of this work is to develop a recursive unscented Kalman filter capable of addressing the combined challenges of measurement nonlinearities, dynamic quantization effects, and hybrid cyber-attack scenarios. By solving Riccati-like difference equations, an upper bound on the filtering error covariance is derived, and subsequently minimized through the design of time-varying filter gains. Extensive simulations on the IEEE 69 distribution test system demonstrate the effectiveness of the proposed filtering algorithm.