Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Abstract

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, timesynchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving This work was partially supported by US Department of Energy under Advanced Grid Modernization Program.

Dynamic State Estimation for Power System Control and Protection

“…Also, the Jacobian matrix is needed for some nonlinear observer designs. However, magnetic saturation in synchronous machines is frequently encountered in power systems [2], making infeasible the computation of Jacobians and subsequent observer design. In contrast, there exist derivativefree nonlinear Kalman filter-based DSEs, such as unscented Kalman filter, ensemble Kalman filter, particle filter, etc.…”

“…Solutions to these challenges can be sought in dynamic state estimation (DSE) applications [1]. Specifically, DSE provides real-time operating states of the system at fast rates [2], which in turn can be utilized to fulfill the requirements for modern power systems protection and control.…”

Application of deep learning for power system state forecasting

“…It can lead to better resilience and robustness towards any unprecedented attacks on the grid. 42 Dynamic state estimation algorithms have also been performed using an extended and unscented Kalman filter showcasing an accurate estimation strategy. 43 Let, e (e ℝ b ) be the total number of available measurements which is accessible for the current observable system and number of current operating states be c (c ℝ a ) at time t. 44 e = g c ð Þ + d ð1Þ g(.)…”

Hybrid Physics-Based Adaptive Kalman Filter State Estimation Framework

“…With the penetration of renewable energy and new control equipment, the static perspective does not provide the modern energy management systems' requirements. Thus, it becomes imperative to capture systems' dynamics for advanced automated applications, in such a way to provide fast control actions, increase the reliability of the system, diagnosis of cyber-attacks, and optimize resources across the power grid [2][3][4].…”

“…Dynamic state estimation in power systems can be understood as to estimate the grid states, as voltages magnitude and phases, considering the time relation between them. There are many presented physics-model-based dynamic state estimation (DSE) solutions, being the Kalman Filter formulation and it is likewise a family of formulations most used [3,5,6]. The first formulations of DSE in power systems were proposed shortly after the consolidation of static state estimation in transmission system operation centers [7][8][9][10][11][12].…”