Robust power system state estimation with minimum error entropy unscented Kalman filter
The unscented Kalman filter (UKF) provides a powerful tool for power system forecasting-
aided state estimation (FASE). However, when the power systems are affected by the …
aided state estimation (FASE). However, when the power systems are affected by the …
Robust dynamic state estimation for power system based on adaptive cubature Kalman filter with generalized correntropy loss
Y Wang, Z Yang, Y Wang, V Dinavahi… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Due to the unfavorable interference of non-Gaussian noise, abnormal system states, and
rough measurement errors, dynamic state estimation (DSE) plays an important role in the …
rough measurement errors, dynamic state estimation (DSE) plays an important role in the …
Resilient dynamic state estimation for multi-machine power system with partial missing measurements
Y Wang, Y Wang, Y Sun, V Dinavahi… - … on Power Systems, 2023 - ieeexplore.ieee.org
Accurate tracking the dynamics of power system plays a significant role in its reliability,
resilience and security. To achieve the reliable and precise estimation results, many …
resilience and security. To achieve the reliable and precise estimation results, many …
Square root unscented Kalman filter with modified measurement for dynamic state estimation of power systems
Dynamic state estimation of a power system provides essential information about its inherent
dynamic change. Nonlinear Kalman filters (NKFs) have been identified as potential versatile …
dynamic change. Nonlinear Kalman filters (NKFs) have been identified as potential versatile …
Detection of false data injection attacks in smart grid based on joint dynamic and static state estimation
P Hu, W Gao, Y Li, F Hua, L Qiao, G Zhang - IEEE Access, 2023 - ieeexplore.ieee.org
Power system state estimation is an essential component of the modern power system
energy management system (EMS), and accurate state estimation is an indispensable basis …
energy management system (EMS), and accurate state estimation is an indispensable basis …
Power system state forecasting using machine learning techniques
D Mukherjee, S Chakraborty, S Ghosh - Electrical Engineering, 2022 - Springer
Modern power sector requires grid observability under all scenarios for its ideal functioning.
This enforces the operator to incorporate state estimation solutions based on a priori …
This enforces the operator to incorporate state estimation solutions based on a priori …
Implementation of Novel Reduced-Order H∞ Filter for Simultaneous Detection and Mitigation of FDI-Attacks in AGC Systems
Recent developments in power systems to build a smart grid depend critically on
communication networks which provide unprecedented advantages for comprehensive …
communication networks which provide unprecedented advantages for comprehensive …
A fast and robust state estimator based on exponential function for power systems
In realistic power system state estimation, the distribution of measurement noise is usually
assumed to be Gaussian while many researcher have verified that it can be non-Gaussian …
assumed to be Gaussian while many researcher have verified that it can be non-Gaussian …
Application of deep learning for power system state forecasting
D Mukherjee, S Chakraborty, S Ghosh… - … on Electrical Energy …, 2021 - Wiley Online Library
The recent trend in modern power sector is to maintain observability of the grid for its smooth
operation under all circumstances. To ascertain this aforementioned criterion, grid operators …
operation under all circumstances. To ascertain this aforementioned criterion, grid operators …
Dynamic State Estimation of Power Systems by -Norm Nonlinear Kalman Filter
The problem of dynamic state estimation of power systems is relevant to the monitoring of
real-time operation of essential power distribution infrastructure. The nonlinear Kalman filter …
real-time operation of essential power distribution infrastructure. The nonlinear Kalman filter …