Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning
Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices...
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doaj-b20cc2040c4e471a88e900e853ed9fd62021-06-03T23:09:11ZengIEEEIEEE Access2169-35362021-01-019404024041610.1109/ACCESS.2021.30646899373306Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine LearningNarayan Bhusal0https://orcid.org/0000-0002-2275-2145Mukesh Gautam1https://orcid.org/0000-0003-0571-5825Mohammed Benidris2https://orcid.org/0000-0002-8731-8913Department of Electrical and Biomedical Engineering, University of Nevada at Reno, Reno, NV, USADepartment of Electrical and Biomedical Engineering, University of Nevada at Reno, Reno, NV, USADepartment of Electrical and Biomedical Engineering, University of Nevada at Reno, Reno, NV, USASeveral wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. This paper proposes a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system. The results show that the proposed approach can detect low margin attacks (as low as 1% of actual measurements) with up to 99% accuracy. <italic>All of the developed source codes of the proposed solution are publicly available at Github.</italic> <uri>https://github.com/nbhusal/Data-Attack-on-Voltage-Regulation</uri>.https://ieeexplore.ieee.org/document/9373306/Coordinated control of voltage regulationdata falsification cyber attackmachine learningphotovoltaic |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Narayan Bhusal Mukesh Gautam Mohammed Benidris |
spellingShingle |
Narayan Bhusal Mukesh Gautam Mohammed Benidris Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning IEEE Access Coordinated control of voltage regulation data falsification cyber attack machine learning photovoltaic |
author_facet |
Narayan Bhusal Mukesh Gautam Mohammed Benidris |
author_sort |
Narayan Bhusal |
title |
Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning |
title_short |
Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning |
title_full |
Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning |
title_fullStr |
Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning |
title_full_unstemmed |
Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning |
title_sort |
detection of cyber attacks on voltage regulation in distribution systems using machine learning |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. This paper proposes a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system. The results show that the proposed approach can detect low margin attacks (as low as 1% of actual measurements) with up to 99% accuracy. <italic>All of the developed source codes of the proposed solution are publicly available at Github.</italic> <uri>https://github.com/nbhusal/Data-Attack-on-Voltage-Regulation</uri>. |
topic |
Coordinated control of voltage regulation data falsification cyber attack machine learning photovoltaic |
url |
https://ieeexplore.ieee.org/document/9373306/ |
work_keys_str_mv |
AT narayanbhusal detectionofcyberattacksonvoltageregulationindistributionsystemsusingmachinelearning AT mukeshgautam detectionofcyberattacksonvoltageregulationindistributionsystemsusingmachinelearning AT mohammedbenidris detectionofcyberattacksonvoltageregulationindistributionsystemsusingmachinelearning |
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1721398600191180800 |