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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Main Authors: Narayan Bhusal, Mukesh Gautam, Mohammed Benidris
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9373306/
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spelling 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&#x0025; of actual measurements) with up to 99&#x0025; 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&#x0025; of actual measurements) with up to 99&#x0025; 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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