Diagnosis of Outliers and Cyber Attacks in Dynamic PMU-Based Power State Estimation

Chakhchoukh, Yacine; Lei, Hangtian; Johnson, Brian K.

doi:10.1109/tpwrs.2019.2939192

Cited by 49 publications

(29 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In ref. [51] the sudden undesirable disturbances posing a challenge to the network resilience, using synchrophasor based dynamic SE is addressed. It comprises of three main steps, (1) constant monitoring of the network's most indicative feature, (2) prediction of failures based on constant monitoring, and (3) mitigation of network to prevent further damage.…”

Section: Methods Used In Sbr Evaluationmentioning

confidence: 99%

“…Accurate detection and recovery of synchrophasor data from the corrupted measurements prevent further degradation 47,48 . Monitoring its most indicative feature aids mitigation during intrusion and adverse weather 51,52 . The classification of HILF events into natural and cyber‐oriented events help in robust feature extraction 54,56 .…”

Section: Sbr Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Sonal

Ghosh

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary The gradual sprawl of power system toward smart grid and enhanced integration of distributed energy resources at the distribution end is transforming the conventional distribution system and is eventually making it self‐sustained. The distribution system is thus continuously evolving to prepare itself for any unforeseen natural, man‐made, and complex events. These events may impact the distribution system as high‐impact low‐frequency (HILF) scenarios. The high intensity and widespread damage of the power system due to HILF scenarios necessitate resilience. Resilience analysis aims to identify these region‐specific vulnerabilities of the electricity network and implement appropriate methods to quickly restore the system to its predisturbance state. Further to enhance the network visibility and situational awareness, integration of synchrophasor to resilience is significant. Therefore, synchrophasor technology integrated with resilience provides online wide area visibility of the distribution system. Using synchrophasor for resilience assessment propels an agile network for coordinating accurate and timely analysis of the system parameters. Therefore, understanding the concept of resilience, its evolution, detailed overview, synchrophasor‐based resilience (SBR) methods as well as its applications are necessary. This article outlines the key points of the synchrophasor technology based resilience technique, its significance, and lays a foundation for continuing research in this area. It offers detailed insight into the comprehensive review of this evolving concept. The article majorly focuses on all the aspects of SBR, its necessity in performance evaluation, synchrophasor measurement based resilience enhancement methods, and application of different SBR techniques in the distribution system. A detailed comparative analysis of SBR features, sample efficiency, result accuracy, merits, and demerits based on available literature is provided. Finally, future perspectives are discussed for implementing resilience assessment using synchrophasor technology in the distribution system.

show abstract

Section: Methods Used In Sbr Evaluationmentioning

confidence: 99%

Section: Sbr Applicationsmentioning

confidence: 99%

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Sonal

Ghosh

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…This is not the case for the robust estimation that automatically detects and suppresses bad data. For the cyber-attack scenario, robust DSE with high breakdown points or novel machine learning-aided DSE might offer a solution [60]. This is still an open area that requires further investigation.…”

Section: B Monitoringmentioning

confidence: 99%

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

Zhao

Netto

Huang

et al. 2021

IEEE Trans. Power Syst.

146

View full text Add to dashboard Cite

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.

show abstract

“…Reference [35] proposes a machine learning-based approach to detect FDI attacks in the power system. In [36], the authors propose a linear approach to detect cyber-attacks and outliers in PMU-based power system state estimation, and in [37], a supervised learning-based approach is proposed to detect DoS attacks in smart grids. Table 1 shows a comparison of the methods studied in this article and others in the same field.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

et al. 2021

View full text Add to dashboard Cite

The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, which provide benefits in terms of the speed and accuracy of the process. Measurement devices in utilities and buses are vulnerable to communication interruptions between phasor measurement units and operators, who can be easily manipulated by false data. While Kalman filters are incapable of detecting the majority of such cyber-attacks, this article proves that the proposed unsupervised machine learning method is able to detect more than 90 percent of the mentioned attacks. The simulation results on the IEEE 9-bus with 3-machines and IEEE 14-bus with 5-machines systems verify the efficiency of the proposed approach.

show abstract

Diagnosis of Outliers and Cyber Attacks in Dynamic PMU-Based Power State Estimation

Cited by 49 publications

References 39 publications

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

Novel trends in resilience assessment of a distribution system using synchrophasor application: A literature review

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

A Hybrid Framework for Detecting and Eliminating Cyber-Attacks in Power Grids

Contact Info

Product

Resources

About