Abstract
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. All of the developed source codes of the proposed solution are publicly available at Github. https://github.com/nbhusal/Data-Attack-on-Voltage-Regulation.
Highlights
Modernization of power distribution systems is increasing rapidly in recent years to improve their operation and control paradigms
The results show that the proposed approach can detect low margin attacks with up to 99% accuracy
EVALUATION METRICS Evaluation metrics used to evaluate the performance of the proposed machine learning model are the mean absolute error (MAE) and root mean square error (RMSE)
Summary
Modernization of power distribution systems is increasing rapidly in recent years to improve their operation and control paradigms. A literature survey of machine learning approaches for the detection of false data injection attacks on power systems is provided in [7]. The approaches proposed in these studies cannot detect low margin coordinated attacks on multiple measurements that are used to set all voltage regulating devices. This paper proposes a machine learning-based two-stage regression-classification approach to detect, locate, and distinguish data falsification attacks on coordinated control of distribution system voltage regulation. Develop a machine learning-based two-stage (regression and classification) approach to detect, locate, and distinguish data falsification attacks in the coordinated control of distribution system voltage regulation network.
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