Power system dynamic state estimation (DSE) remains an active research area. This is
driven by the absence of accurate models, the increasing availability of fast-sampled, time …

The Kalman-filter-based algorithms as the mainstream algorithms of dynamic state
estimation of power systems have been extensively used to provide accurate data for power …

With the ever increasing penetration of renewable energy, smart loads, energy storage, and
new market behavior, today's power grid becomes more dynamic and stochastic, which may …

State and parameter estimation of power transmission networks is important for monitoring
power grid operating conditions and analyzing transient stability. Wind power generation …

For the past few decades, the drastic increase in the installed capacity of wind farms (WFs)
has necessitated a computationally efficient dynamic equivalent model of a WF, which can …

Dengue fever is an acute mosquito-borne viral infection that results in a heavy social burden
in many tropical and subtropical regions. Accurate forecasts of dengue outbreak allow the …

This letter proposes a novel Gaussian mixture model-based ensemble Kalman filter
approach to the accurate calibration of the parameters of machine dynamic models. This …

This paper presents a nonlinear model predictive control (NMPC) strategy for high-voltage
direct current (HVDC) transmission to damp inter-area oscillations in the power grid. An …

This report of TF on dynamic state and parameter estimation aims to 1) clearly review its
motivations and definitions, demonstrate its values for enhanced power system modeling …

Precise modeling of power systems is vital to ensure stability, reliability, and secure
operations. In power industrial settings, model parameters can become skewed over time …