1 citations · 3 across the 15 of their papers we have counts for
6 papers · 2 filters
Distributed Cubature Kalman Filter based on MEEF with Adaptive Cauchy Kernel for State Estimation
Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu
Nowadays, with the development of multi-sensor networks, the distributed cubature Kalman filter is one of the well-known existing schemes for state estimation, for which the influe…
State-of-charge estimation of lithium-ion batteries using a tree seed and genetic algorithm-optimized generalized mixture minimum error entropy-based square root cubature Kalman filter
Haiquan Zhao, Xiong Yin, Jinhui Hu
The cubature Kalman filter based on minimum error entropy (MEE-CKF) offers accurate and robust performance in state of charge (SOC) estimation. However, due to the inflexibility of…
Dynamic State Estimation of Power System Utilizing Cauchy Kernel-Based Maximum Mixture Correntropy UKF over Beluga Whale-Bat Optimization
Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu
Non-Gaussian noise, outliers, sudden load changes, and bad measurement data are key factors that diminish the accuracy of dynamic state estimation in power systems. Additionally, u…
CKFNet: Neural Network Aided Cubature Kalman filtering
Jinhui Hu, Haiquan Zhao, Yi Peng
The cubature Kalman filter (CKF), while theoretically rigorous for nonlinear estimation, often suffers performance degradation due to model-environment mismatches in practice. To a…
Adaptive Robust Unscented Kalman Filter for Dynamic State Estimation of Power System
Duc Viet Nguyen, Haiquan Zhao, Jinhui Hu +1
Non-Gaussian noise and the uncertainty of noise distribution are the common factors that reduce accuracy in dynamic state estimation of power systems (PS). In addition, the optimal…
Diffusion Augmented Complex Maximum Total Correntropy Algorithm for Power System Frequency Estimation
Haiquan Zhao, Yi Peng, Jinsong Chen +1
Currently, adaptive filtering algorithms have been widely applied in frequency estimation for power systems. However, research on diffusion tasks remains insufficient. Existing dif…