An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems
Inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet...
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IEEE
2023-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/10164115/ |
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author | Asad Ali Khan Omar A. Beg Yu-Fang Jin Sara Ahmed |
author_facet | Asad Ali Khan Omar A. Beg Yu-Fang Jin Sara Ahmed |
author_sort | Asad Ali Khan |
collection | DOAJ |
description | Inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort normal operation, therefore, an effective cyber anomaly mitigation technique using an Artificial Neural Network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using a dynamic neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection on the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. An explainable framework is employed to interpret the output generated by the trained neural network-based controller after the neural network has been trained offline and validated online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of the microgrid under various cyber anomalies as demonstrated on a real-time digital simulator. |
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id | doaj.art-821f34ded284414f98c97f9d54c98d4c |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-13T01:21:29Z |
publishDate | 2023-01-01 |
publisher | IEEE |
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spelling | doaj.art-821f34ded284414f98c97f9d54c98d4c2023-07-04T23:00:52ZengIEEEIEEE Access2169-35362023-01-0111653826539410.1109/ACCESS.2023.328988710164115An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based SystemsAsad Ali Khan0https://orcid.org/0000-0002-6303-8693Omar A. Beg1https://orcid.org/0000-0002-5353-7616Yu-Fang Jin2Sara Ahmed3https://orcid.org/0000-0003-0935-5011Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Electrical Engineering, The University of Texas Permian Basin, Odessa, TX, USADepartment of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USADepartment of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USAInverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort normal operation, therefore, an effective cyber anomaly mitigation technique using an Artificial Neural Network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using a dynamic neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection on the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. An explainable framework is employed to interpret the output generated by the trained neural network-based controller after the neural network has been trained offline and validated online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of the microgrid under various cyber anomalies as demonstrated on a real-time digital simulator.https://ieeexplore.ieee.org/document/10164115/Artificial neural networkscyber anomaly mitigationdistributed cooperative controlexplainable neural networksfalse data injection attacksmicrogrids |
spellingShingle | Asad Ali Khan Omar A. Beg Yu-Fang Jin Sara Ahmed An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems IEEE Access Artificial neural networks cyber anomaly mitigation distributed cooperative control explainable neural networks false data injection attacks microgrids |
title | An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems |
title_full | An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems |
title_fullStr | An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems |
title_full_unstemmed | An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems |
title_short | An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-Based Systems |
title_sort | explainable intelligent framework for anomaly mitigation in cyber physical inverter based systems |
topic | Artificial neural networks cyber anomaly mitigation distributed cooperative control explainable neural networks false data injection attacks microgrids |
url | https://ieeexplore.ieee.org/document/10164115/ |
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