Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack

Anwar, Adnan; Mahmood, Abdun Naser

doi:10.1007/978-981-4585-27-9_17

Cited by 41 publications

(17 citation statements)

References 35 publications

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“…The existence of outlier values may be rigorously impaired the estimation results and needs an adequate caution to be taken. This fact has been studied by many researchers in view of point estimation for the unknown state parameters (Anwar and Mahmood 2014). To this reason end, the performance of the proposed interval estimation had also been carried out in the presence of contaminated measurements.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

The computation of confidence intervals for the state parameters of power systems

et al. 2016

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BackgroundIn the past few decades, a significant volume of work has been carried out on various aspects of the state estimation problem to estimate an optimum state vector of the power system. This problem has been focused on, in previous studies regarding the computational efficiency and numerical robustness in view to find point estimates for system state parameters. This current investigation, constructed confidence intervals for the unknown state parameters of the system. The research indicates that confidence intervals can yield addition useful information about the estimated parameters.MethodsThe feasible interval estimates for the system state parameters have been modelled in this study by considering the random uncertainty in the processing measurements. The statistical assumptions of the measurement errors have been utilized to characterize the probabilistic behavior of the estimated parameters in terms of confidence intervals. The Gauss–Newton algorithm has been adopted for maximizing the likelihood function of the processing measurements and obtaining the confidence intervals.ResultsThe usage of the confidence intervals was demonstrated through Monte Carlo experiments on a real dataset of the 6-bus and IEEE 14-bus power systems for both small and large sample sizes. The confidence intervals were constructed for the test networks for the sample of measurements 18, 28, 44 and 68 based on the redundancy ratio R. The proposed interval estimates outperformed for the sample sizes of 28 in the 6 bus and 68 in the IEEE 14-bus systems, respectively. The poor performance for the constructed interval estimates have been reported even for the large sample sizes in the existence of contaminated measurements.ConclusionsThe results of the study show that the method is effective and practically applicable in the state estimation of a power system. The constructed confidence intervals for the system state parameters adequately perform for the lager sample size. However, the existence of the gross errors in the processing measurements had severe effect on the performance of the proposed interval estimates.

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Section: Discussion Of the Resultsmentioning

confidence: 99%

The computation of confidence intervals for the state parameters of power systems

et al. 2016

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“…Equations (6) and (7) express the current and voltage magnitude at bus n, respectively, in complex form as:…”

Section: Optimal Power Flowmentioning

confidence: 99%

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

2019

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In today’s grid, the technological based cyber-physical systems have continued to be plagued with cyberattacks and intrusions. Any intrusive action on the power system’s Optimal Power Flow (OPF) modules can cause a series of operational instabilities, failures, and financial losses. Real time intrusion detection has become a major challenge for the power community and energy stakeholders. Current conventional methods have continued to exhibit shortfalls in tackling these security issues. In order to address this security issue, this paper proposes a hybrid Support Vector Machine and Multilayer Perceptron Neural Network (SVMNN) algorithm that involves the combination of Support Vector Machine (SVM) and multilayer perceptron neural network (MPLNN) algorithms for predicting and detecting cyber intrusion attacks into power system networks. In this paper, a modified version of the IEEE Garver 6-bus test system and a 24-bus system were used as case studies. The IEEE Garver 6-bus test system was used to describe the attack scenarios, whereas load flow analysis was conducted on real time data of a modified Nigerian 24-bus system to generate the bus voltage dataset that considered several cyberattack events for the hybrid algorithm. Sising various performance metricion and load/generator injections, en included in the manuscriptmulation results showed the relevant influences of cyberattacks on power systems in terms of voltage, power, and current flows. To demonstrate the performance of the proposed hybrid SVMNN algorithm, the results are compared with other models in related studies. The results demonstrated that the hybrid algorithm achieved a detection accuracy of 99.6%, which is better than recently proposed schemes.

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“…Traditional state estimators are capable of identifying and eliminating bad data from state estimations [13] [14]. However, it has been recently found that malicious data attacks can exploit system topology information to construct false data injection schemes that will bypass bad data detectors in conventional state estimation [15][16] [17]. A number of defending strategies have been proposed [18], [19], [20].…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of false data injection attacks in smart grid under colored Gaussian noise

Tang

Yan

Kay

et al. 2016

2016 IEEE Conference on Communications and Network Security (CNS)

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Abstract-In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation. We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of both observable and unobservable false data attacks demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.

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Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack

Cited by 41 publications

References 35 publications

The computation of confidence intervals for the state parameters of power systems

The computation of confidence intervals for the state parameters of power systems

Real Time Security Assessment of the Power System Using a Hybrid Support Vector Machine and Multilayer Perceptron Neural Network Algorithms

Detection of false data injection attacks in smart grid under colored Gaussian noise

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