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Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
Junbo Zhao, Marcos Netto, Zhenyu Huang, Samson Shenglong Yu, Antonio Gomez-Exposito, Shaobu Wang, Innocent Kamwa, Shahrokh Akhlaghi, Lamine Mili, Vladimir Terzija, A. P. Sakis Meliopoulos, Bikash Pal, Abhinav Kumar Singh, Ali Abur, Tianshu Bi, Alireza Rouhani
TL;DR
Power systems need dynamic state estimation because rapidly changing resources, model uncertainty, and time-critical applications challenge static estimation. The paper compares SSE and DSE requirements and roles across modeling, monitoring, and operation, then identifies applications and research directions for next-generation EMS. It concludes that DSE provides dynamic information for future power systems while requiring further development for practical deployment.
Problem
Rapidly changing power-electronics-interfaced resources, inaccurate device models, and time-critical applications create a need to clarify DSE’s role relative to SSE.
Method
The paper compares SSE and DSE measurement, modeling, observability, software, and application requirements and reviews DSE roles in modeling, monitoring, and operation.
Results
DSE provides dynamic visibility and supports applications including model validation, dynamic security assessment, stability assessment, and monitoring of thermal and converter-related behavior.
Takeaways & Limitations
DSE is positioned as a foundation for time-critical power-system functions and the transition toward next-generation EMS.
Abstract
from arXiv · showhide
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-synchronized 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 the way for developing the next generation of energy management systems.
I. INTRODUCTION
DSE is motivated by faster-changing power systems, improved measurement infrastructure, and the need to support time-critical applications. The paper clarifies DSE’s roles, implementation differences, and transition toward next-generation EMS.
- Motivation: Increasing intermittent, stochastic, and power electronics-interfaced generation makes tracking dynamic state variables critical.These resources change operating points more often and rapidly.
- Motivation: DSE can validate power electronics-interfaced device models and estimate unknown or incorrect parameters.
- Research context: PMUs, merging units, and Kalman-filtering methods have accelerated recent DSE research after earlier infrastructure limitations.Research also includes data-driven estimation, observability analysis, and robustness against bad data and parameter errors.
- Applications: DSE can improve dynamic security assessment initialization and transient frequency-based control applications where static estimation is unreliable.The paper notes that DSE can estimate dynamic states, frequency, and ROCoF for these applications.
- Paper scope: The paper summarizes DSE roles in modeling, monitoring, and operation while comparing measurement, model, software, and application requirements with SSE.It also presents examples and future directions for transitioning from today’s EMS to next-generation EMS.
II. COMPARATIVE OVERVIEW OF SSE AND DSE
SSE is established in today’s EMS, whereas DSE is a newer tool requiring clarification of its implementation and functionality. This comparison supports a transition toward DSE-based EMS for power-electronics-dominated systems.
- Background: SSE is widely used in today’s EMS, while DSE remains a newer tool for industry and system operators.
- Observability: Topological observability gives binary observability results and identifies observable islands or minimal pseudo-measurements needed to restore observability.Numerical observability provides a range of answers based on the condition number of a factored matrix.
- Transition: Clarifying SSE and DSE differences is essential for transitioning from SSE-based EMS to future DSE-based EMS.The transition is framed for power electronics-dominated power systems.
A. Implementation Differences
SSE and DSE differ in measurement, model, observability, execution, output, and application requirements. DSE uses fast synchronized data and dynamic models to track evolving system behavior.
- Measurements: DSE uses fast, synchronized measurements reported at 30 to 240 samples per second, unlike SSE’s mainly SCADA-based measurements updated every 2-5s.DSE measurements may come from PMUs and digital fault recorders.
- Models: SSE represents generators and loads with algebraic equations, whereas DSE represents generators, dynamic loads, DERs, and controllers with DAEs.
- Measurement placement: DSE does not require PMUs at every generator terminal when a local LSE makes the terminal observable.
B. Functionality Differences
SSE and DSE both improve system visibility and validate data and models, but their outputs support different levels of system understanding. SSE provides static algebraic quantities, while DSE tracks dynamic changes and states.
- Shared functions: Both SSE and DSE improve targeted system visibility and validate the data and models used by power-system functions.
- SSE functionality: SSE provides snapshot-based algebraic variables used for power flows, power injections, forecasting, and static security assessment.
- DSE functionality: DSE tracks dynamic changes from fast synchronized measurements, providing dynamic states, anomaly detection, and unknown-input estimates.These outputs support dynamic security assessment, rotor-angle stability assessment, and adaptive protection.
- SSE functionality: PMU-only linear SSE can update at PMU scan rates but remains restricted to algebraic variables.
C. Practical Implementation of DSE
DSE can be implemented by combining existing commercial simulation tools with a nonlinear state-space model and Kalman-filter-based prediction and correction.
- Existing commercial tools can support DSE implementation despite the lack of dedicated commercial DSE software.The approach leverages commercial tools’ event playback capabilities.
- The system is represented after time discretization of differential-algebraic equations using available measurements.The model includes dynamic and algebraic states, inputs, parameters, and process and measurement errors.
- DSE first predicts the state and then corrects it by integrating the prediction with measurements.These two steps correspond to the state-space prediction and measurement-based filtering stages.
- Commercial event playback functions can automatically execute the state-transition and measurement equations for DSE implementation.The remaining estimation work integrates state prediction and measurements within a Kalman filter framework.
III. ROLES OF DSE IN POWER SYSTEMS MODELING, MONITORING AND OPERATION
The paper identifies modeling, enhanced dynamic visibility for monitoring, and operation as three major application areas that can benefit from DSE.
- DSE applications are organized into modeling, monitoring through enhanced dynamic visibility, and operation.
A. Modeling
DSE supports dynamic-model validation and online calibration by comparing model responses with measurements and estimating parameters that may be unknown or drifting.
- Dynamic models can be validated by using measured generator voltage magnitude, phase angle, or frequency as inputs and comparing modeled P and Q with measurements.Commercial event playback functions are leveraged for this validation using PMU data.
- Online calibration addresses parameter drift caused by environmental changes, aging, and coupling effects without taking generators offline for testing.The calibration process includes initial checks, sensitivity analysis, parameter estimation, and validation using other disturbances.
- Unknown parameters, including governor dead-band parameters, can be formulated in the state-space model and estimated with DSE algorithms.
B. Monitoring
DSE enhances monitoring by tracking dynamic states, estimating frequencies, identifying inputs and anomalies, and supporting applications beyond electromechanical dynamics. It also improves data quality and can estimate unmeasured quantities under challenging measurement conditions.
- Dynamic visibility: DSE provides seven monitoring functions, including state-trajectory tracking, oscillation monitoring, frequency and ROCOF estimation, data correction, input identification, anomaly detection, and longer-time-constant applications.Its trajectory outputs include generator and controller states during disturbances; rotor speed and angle support oscillation detection and forced-oscillation source location.
- Data quality: DSE filters measurement noise and can detect and suppress bad data, while robust and machine-learning-aided approaches address non-Gaussian errors and cyber attacks.Traditional residual thresholds depend on system characteristics and may be unreliable when noise statistics are unknown; robust DSE remains an open research area for cyber attacks.
- Equipment monitoring: DSE supports excitation-system visibility, including estimation of unknown inputs and detection of controller failures that can affect stability assessment.Modern brushless excitation systems and limiter behavior make generalized excitation-voltage calculation from PMU measurements difficult.
- Unknown inputs: Correlation-aided robust DSE estimates unknown generator frequency and turbine-governor mechanical torque without requiring generator frequency measurements.The approach is described as more robust to bad data than previous methods.
- Longer-time-constant applications: DSE-based monitoring also estimates external thermal-model parameters for lines and cables, supporting real-time thermal ratings under changing meteorological conditions.This extends DSE beyond electromechanical dynamics to applications with much higher time constants.
C. Operation
DSE contributes to power-system operation by supplying dynamic-model initialization and near-real-time information for dynamic security and stability assessment. These capabilities address rapidly changing conditions and limitations of offline simulation databases.
- Operational roles: SSE remains important for contingency analysis, static voltage stability, and optimal power flow, while DSE adds operational benefits through dynamic security and stability assessment.The paper groups these DSE operational benefits into the two categories discussed in this section.
- Dynamic security assessment: DSE can initialize dynamic security assessment with dynamic states when the system is not at equilibrium or nominal frequency, conditions under which SSE cannot initialize differential equations.High DER penetration causes variables to change more often and rapidly, making timely initialization especially important.
- Dynamic stability assessment: Dynamic stability assessment traditionally compares real-time measurements with offline-simulation databases and initiates remedial control when evolving instability is detected.The database records dynamic-state evolution across simulated events, while online classifiers such as decision trees or random forests perform comparisons.
- Dynamic stability assessment: DSE offers an alternative to offline databases by providing an almost real-time picture of system dynamics and enabling computation of transient stability indices.The paper describes this real-time-information control philosophy as promising but still in its infancy.
- Dynamic stability assessment: DSE-provided rotor angles support rotor-angle stability assessment through the sign of the system’s maximal Lyapunov exponent.A robust UKF-based DSE is used to estimate and track generator rotor angle and speed in an unstable, highly nonlinear case.
D. DSE for Power Electronics-Interfaced Renewable Generation Visibility
Power-electronics-interfaced renewable sources require dynamic visibility because their variability, uncertainty, converter controls, and faster dynamics differ from conventional synchronous generation. Monitoring therefore requires faster sampling and broader bandwidth.
- Renewable-generation visibility: Renewable energy sources are commonly integrated through power-electronics converters, creating a need for dynamic visibility for reliable and cost-effective integration.Compared with synchronous generators, RESs exhibit greater variability and uncertainty.
- Renewable-generation visibility: Power converters and renewable-generation control loops differ from synchronous machines and have much smaller time constants, producing faster dynamics.These characteristics distinguish the monitoring requirements of converter-interfaced resources from those of conventional generators.
- Measurement requirements: Monitoring renewable-generation dynamics requires measurement systems with faster sampling speeds and broader bandwidths.The requirement follows from the faster converter and control-loop dynamics described for RESs.
IV. CONCLUSIONS AND FUTURE WORK
The paper frames DSE as a foundation for modeling, monitoring, and operating future power systems, while identifying research needs spanning data infrastructure, algorithms, applications, and practical deployment.
- Data Infrastructure for DSE: Future DSE research must address real-time measurements, their transfer to appropriate locations, and communication requirements for centralized, distributed, and hierarchical implementations.Open issues include data rates, signal selection, sensor placement, bandwidth, reliability, redundancy, and network structure.
- DSE Core Functions: DSE core functions require continued improvement for large-scale real-time estimation, mixed slow and fast inverter dynamics, data-driven enhancement, and resilient islanding operation.The paper specifically identifies computational scalability, fast inverter dynamics, machine learning, and real-time decomposition during islanding as research questions.
- DSE Core Functions: Although DSE research has significantly improved algorithmic performance, future power systems require further development of core functions to meet evolving operational requirements.The paper links this need to increasing system complexity and changing dynamics.
- Development of DSE Applications: DSE provides detailed dynamic information that supports applications including model calibration, oscillation-source location, look-ahead DSA, instability detection, and DER anomaly detection.These applications extend existing functions and introduce capabilities beyond static state estimation.
- Practicality of DSE Applications: DSE applications still face practical deployment gaps involving coexistence with SSE, control-room infrastructure, operator interfaces, workforce training, and standards.Rapidly updated DSE results may require improved presentation methods, including AI tools that process streaming DSE data for visibility and stability assessment.