Resilient Dynamic State Estimation and Situational Awareness in Cyber-Physical Power Systems

Modern power systems are evolving into highly digital, distributed, and data-intensive cyber-physical networks. As new sensing, communication, and control infrastructures intertwine with traditional electrical grids, ensuring reliable system awareness becomes increasingly complex. While synchronized phasor measurements and advanced automation have elevated monitoring capabilities, they also expose vulnerabilities to data loss, latency, interference, and malicious manipulation. Existing state estimation techniques, primarily designed for static and idealized network conditions, often fail to retain accuracy and robustness in these adversarial and uncertain contexts. Consequently, the stability and reliability of modern grids depend critically on developing estimation methods that are resilient to degraded data quality, communication disruptions, and cyber-physical threats.This Research Topic aims to advance resilient dynamic state estimation and situational awareness methodologies for next-generation power systems. The primary goal is to foster innovations that maintain observability, accuracy, and trustworthiness of system state information, even under challenging measurement conditions or cyber-induced anomalies. Research questions include: How can estimation algorithms adapt to asynchronous or corrupted data streams? What frameworks ensure real-time awareness despite network uncertainties? How can model-based and data-driven approaches be integrated for resilient situational awareness? By addressing such open challenges, the topic seeks to promote more secure and intelligent grid operation across transmission, distribution, and integrated energy systems.This Research Topic encompasses contributions that address resilient estimation, uncertainty modeling, and awareness enhancement across multiple operational contexts. To gather further insights into resilient and secure information processing for cyber-physical grids, we welcome submissions that include, but are not limited to, the following themes:- Dynamic state estimation for transmission and distribution systems under incomplete, delayed, or corrupted measurements- Resilient estimation under false data injection, denial-of-service incidents, sensor degradation, and communication failures- Multi-rate, asynchronous, and hybrid SCADA–PMU estimation frameworks- Robust filtering, observer design, and uncertainty-aware estimation for nonlinear power system dynamics- Data–model fusion and hybrid analytics for situational awareness in smart grids- Observability enhancement and redundancy design for resilient monitoring architectures- Estimation algorithms for inverter-dominated, low-inertia, and renewable-rich power systems- Distributed and decentralized estimation in active distribution networks, microgrids, and multi-area systems- Estimation under non-Gaussian noise, topology uncertainty, and parameter mismatch- Event-triggered, communication-efficient, and digital twin–based estimation for real-time operational awareness- Benchmarking, validation, and experimental case studies on resilient estimation performance in practical power grid environments Topic editors: Yi Wang, Xinxin Lv, Dongchen Hou, Sen Wang Source: https://www.frontiersin.org/research-topics/80530/resilient-dynamic-state-estimation-and-situational-awareness-in-cyber-physical-power-systems