Formal Online Resiliency Monitoring in Microgrids

This work adopts an online resiliency monitoring framework employing metric temporal logic (MTL) under cyber-physical anomalies, namely false-data injection attacks, denial-of-service attacks, and physical faults. Such anomalies adversely affect the frequency synchronization, load sharing, and voltage regulation in microgrids. MTL formalism is adopted to monitor the outputs of inverters/converters against operational bounds, detect and quantify cyber-physical anomalies, monitor the microgrid’s resiliency during runtime, and compare mitigation strategies. Since the proposed framework does not require system knowledge, it can be deployed on a complex microgrid. This is verified using an IEEE 34-bus feeder system and a DC microgrid cluster in a controller/hardware-in-the-loop environment.

[1]  Frank L. Lewis,et al.  Distributed Control Systems for Small-Scale Power Networks: Using Multiagent Cooperative Control Theory , 2014, IEEE Control Systems.

[2]  Lingkun Fu,et al.  DoS Attack Energy Management Against Remote State Estimation , 2018, IEEE Transactions on Control of Network Systems.

[3]  Juan C. Vasquez,et al.  A Multi-Functional Fully Distributed Control Framework for AC Microgrids , 2018, IEEE Transactions on Smart Grid.

[4]  A. Sekar,et al.  Comparative Study of the IEEE 34 Node Test Feeder under Practical Simplifications , 2007, 2007 39th North American Power Symposium.

[5]  Frank L. Lewis,et al.  A Multiobjective Distributed Control Framework for Islanded AC Microgrids , 2014, IEEE Transactions on Industrial Informatics.

[6]  Frank L. Lewis,et al.  Distributed Cooperative Control of DC Microgrids , 2015, IEEE Transactions on Power Electronics.

[7]  Ron Koymans,et al.  Specifying real-time properties with metric temporal logic , 1990, Real-Time Systems.

[8]  Shenxing Shi,et al.  Identifying Single-Phase-to-Ground Fault Feeder in Neutral Noneffectively Grounded Distribution System Using Wavelet Transform , 2008, IEEE Transactions on Power Delivery.

[9]  Jin Wei,et al.  Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism , 2017, IEEE Transactions on Smart Grid.

[10]  Marc Geilen,et al.  On the Construction of Monitors for Temporal Logic Properties , 2001, RV@CAV.

[11]  Danièle Beauquier On probabilistic timed automata , 2003, Theor. Comput. Sci..

[12]  Grigore Rosu,et al.  Monitoring Algorithms for Metric Temporal Logic Specifications , 2004, RV@ETAPS.

[13]  Yi Qian,et al.  Defense Mechanisms against Data Injection Attacks in Smart Grid Networks , 2017, IEEE Communications Magazine.

[14]  Frank L. Lewis,et al.  Synchrony in Networked Microgrids Under Attacks , 2018, IEEE Transactions on Smart Grid.

[15]  Calin Belta,et al.  Temporal Logics for Learning and Detection of Anomalous Behavior , 2017, IEEE Transactions on Automatic Control.

[16]  Duan-Yu Chen,et al.  Deep-Learning-Based Earth Fault Detection Using Continuous Wavelet Transform and Convolutional Neural Network in Resonant Grounding Distribution Systems , 2018, IEEE Sensors Journal.

[17]  A. Agung Julius,et al.  Robust Temporal Logic Inference for Provably Correct Fault Detection and Privacy Preservation of Switched Systems , 2019, IEEE Systems Journal.

[18]  Ali Davoudi,et al.  Signal Temporal Logic-Based Attack Detection in DC Microgrids , 2019, IEEE Transactions on Smart Grid.

[19]  Josep M. Guerrero,et al.  Virtual-Impedance-Based Fault Current Limiters for Inverter Dominated AC Microgrids , 2018, IEEE Transactions on Smart Grid.

[20]  Jian Fu,et al.  A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids , 2017, IEEE Access.

[21]  Valeriy Vyatkin,et al.  A Survey of Static Formal Methods for Building Dependable Industrial Automation Systems , 2019, IEEE Transactions on Industrial Informatics.

[22]  Zhengyou He,et al.  Travelling wave time-frequency characteristic-based fault location method for transmission lines , 2012 .

[23]  Georgios E. Fainekos,et al.  On-Line Monitoring for Temporal Logic Robustness , 2014, RV.

[24]  B. K. Panigrahi,et al.  Joint-Transformation-Based Detection of False Data Injection Attacks in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[25]  Grigore Rosu,et al.  Monitoring Java Programs with Java PathExplorer , 2001, RV@CAV.

[26]  Zhao Yang Dong,et al.  A Review of False Data Injection Attacks Against Modern Power Systems , 2017, IEEE Transactions on Smart Grid.

[27]  Hedvig Kjellström,et al.  A Formal Approach to Anomaly Detection , 2016, ICPRAM.