A review of cyber security risks of power systems: from static to dynamic false data attacks

Xu, Yan

doi:10.1186/s41601-020-00164-w

Cited by 47 publications

(19 citation statements)

References 59 publications

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“…Large-scale exploitation and application of renewable energy are significant to our future energy transformation and sustainable development, thanks to their outstanding environment-friendly characteristics (Yan, 2020), which can effectively help in the global energy crisis and ecosystem deterioration (Zhang et al, 2021a). In general, distributed generation (DG) is always deemed as an insightful solution, which can satisfy the demand for both uninterrupted electricity supply and zero pollution (Yang et al, 2019a;Xi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

2021

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The electric energy storage system (EESS) is considered as an efficient and promising tool to alleviate the power imbalance of grid-connected microgrid with distributed generation (DG). This work develops a perturbation observer-based fractional-order control (POFOC) strategy for superconducting magnetic energy storage (SMES) system. Initially, a high-gain state and perturbation observer (HGSPO) is designed for reliable estimation of the combined impact of the nonlinearities, parameter uncertainties, unmodeled dynamics, and external disturbances of SMES. Then the storage function of an SMES system is designed, which takes favorable terms into serious consideration to sufficiently utilize the physical properties of the SMES system. Moreover, a fractional-order control framework is applied for complete compensation for the estimated perturbation and adopted as the attached input to boost its dynamical responses. Furthermore, a newly proposed jellyfish search algorithm (JSA) is utilized to realize optimization and tuning of control gains of the developed strategy, upon which high-quality global optimum can be obtained to ensure prominent controlling performance. Case studies, e.g., active power and reactive power supply and system restoration capability under power grid fault effectively validate the effectiveness and reliability of the POFOC strategy compared with traditional PID control and interconnection and damping assignment passivity-based controller (IDA-PBC). In particular, the overshoot of PID is 115.264% of the rated value, while POFOC has no overshoot.

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Section: Introductionmentioning

confidence: 99%

Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

2021

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“…To detect electricity theft more effectively, in the past decades, researchers from various countries have proposed different detection methods, which can be divided into three types: state estimation-based, game theory-based, and machine learning algorithm-based ones [5]. For the state estimation-based method, the operation data in the distribution network collected by advanced measurement infrastructure (AMI) [6], [7], wireless sensor, and other equipment are used to detect the operation status of the system to determine the abnormality in the power systems [8]- [13]. In [8], a detection method of electricity theft is proposed based on illegal branch impedance identification.…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

et al. 2021

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Electricity theft has been a major concern to the secure operation of power systems and the interests of power companies. Due to the different methods and types of electricity theft behaviors, it is difficult to determine the suspicion levels of consumers in the research of electricity theft detection. An electricity theft detection method based on stacked autoencoder (SAE) and the undersampling and resampling based random forest (UaRe-RF) algorithm is proposed in this work to formulate appropriate strategies for the practical electricity theft detection requirements of the power company. In the proposed method, the supervised SAE is first trained to extract electricity consumption features that are more adaptable to the classification algorithm for electricity theft detection. Then, the UaRe-RF algorithm is used to establish the class-balanced subsets and determine the suspicion level of each electricity theft user. Finally, two cases of different datasets of electricity consumers are studied for demonstrating the effectiveness of the proposed method, and the results show that higher classification accuracy and more targeted detection strategies can be achieved through the proposed method.

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“…The work presented in [7] describes comprehensively how machine learning algorithms such as supervised learning, semi-supervised, unsupervised learning, and deep learning are used to detect FDI attacks. Literature review of impact of FDI attacks on economic dispatch, state estimation, and distributed control of distributed generators is provided in [26]. Literature survey of FDI attacks on power systems along with future research direction are studied in [27].…”

Section: Introductionmentioning

confidence: 99%

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

2021

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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 realtime. 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.INDEX TERMS Coordinated control of voltage regulation, data falsification cyber attack, machine learning, and photovoltaic.

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A review of cyber security risks of power systems: from static to dynamic false data attacks

Cited by 47 publications

References 59 publications

Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

Electricity Theft Detection Based on Stacked Autoencoder and the Undersampling and Resampling Based Random Forest Algorithm

Detection of Cyber Attacks on Voltage Regulation in Distribution Systems Using Machine Learning

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