Improving Bad Data Detection in State Estimation of Power Systems

Hagh, Mehrdad Tarafdar; Mahaei, Seyyed Mehdi; Zare, Kazem

doi:10.11591/ijece.v1i2.133

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…In this case, we have pre-knowledge about the variable relation. We believe it is a valid assumption because existing baddata processing schemes [7,9,26] hold the similar assumption.…”

Section: Attack Origins Inferencementioning

confidence: 92%

“…In the second category, researchers applied state estimation to mitigate bad data in the data acquisition sensor [4,25,8,9,26,24,16,11]. This kind of approach is widely used to detect and identify bad data in power systems, such as power flow analysis [11] and topology errors detection [16].…”

Section: Related Workmentioning

confidence: 99%

“…In the first direction, researchers treat the injected data as an injected signal and detect it using bad-data (e.g., data out of normal ranges) processing schemes. As a common detection strategy, existing studies, such as [7,9], introduce artificial redundancy to mitigate the effect of injected data. These approaches have been proved to be effective for securing the integrity of small numbers of system variables.…”

Section: Introductionmentioning

confidence: 99%

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SRID: State Relation Based Intrusion Detection for False Data Injection Attacks in SCADA

Wang

Zhang

et al. 2014

Computer Security - ESORICS 2014

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Abstract. Advanced false data injection attack in targeted malware intrusion is becoming an emerging severe threat to the Supervisory Control And Data Acquisition (SCADA) system. Several intrusion detection schemes have been proposed previously [21,3]. However, designing an effective real-time detection system for a resource-constraint device is still an open problem for the research community. In this paper, we propose a new relation-graph-based detection scheme to defeat false data injection attacks at the SCADA system, even when injected data may seemly fall within a valid/normal range. To balance effectiveness and efficiency, we design a novel detection model, alternation vectors with state relation graph. Furthermore, we propose a new inference algorithm to infer the injection point(s), i.e., the attack origin, in the system. We evaluate SRID with a real-world power plant simulator. The experiment results show that SRID can detect various false data injection attacks with a low false positive rate at 0.0125%. Meanwhile, SRID can dramatically reduce the search space of attack origins and accurately locate most of attack origins.

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“…In this case, we have pre-knowledge about the variable relation. We believe it is a valid assumption because existing baddata processing schemes [7,9,26] hold the similar assumption.…”

Section: Attack Origins Inferencementioning

confidence: 92%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SRID: State Relation Based Intrusion Detection for False Data Injection Attacks in SCADA

Wang

Zhang

et al. 2014

Computer Security - ESORICS 2014

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“…Several approaches are developed to analyze the reliability of measurement data. The classical SE problem [13][14][15][16][17] for power engineering uses steady-state equations based on the basic laws of electrical engineering -Ohm's and Kirchhoff's laws. The variables of these equations are the values of active and reactive power, current and voltage, as well as the parameters of the PG equivalent circuit.…”

Section: Methods Of Estimating the Operating Parameters Of Power Gridsmentioning

confidence: 99%

Estimation of the dynamics of power grid operating parameters based on standard load curves

Tomashevskyi

Burykin

Kulyk

et al. 2019

EEJET

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“…There are several mathematical methods to detect presence of bad data [11, 12]. Considering its simplicity, in this work this task is performed by the χ 2 ‐test [13, 14]. This method can only detect existence of bad data, and cannot identify which is/are the faulty measurement/s.…”

Section: State Estimationmentioning

confidence: 99%

Hybrid method for power system state estimation

Risso

Rubiales

Lotito

2015

IET Generation, Transmission & Distribution

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State estimation in power systems is classically based on the weighted least squares method. Recently, different extensions of Kalman filters have been proposed. Among them, the 'unscented' Kalman filter (UKF) improves the results of weighted least squares methods, when there are small changes in the system, as it considers the history of the state. The novel algorithm presented in this work combines the best of both approaches. To perform this task a new index is defined to allow the algorithm to choose in real time, and for each iteration, between a static or a dynamic estimator. This combination allows overcoming the anomalies observed when the UKF faces abrupt variations of the system state and also the lack of observability that weighted least squares could present. The proposed methodology was tested with three test cases outperforming the previously mentioned algorithms.

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Improving Bad Data Detection in State Estimation of Power Systems

Cited by 9 publications

References 11 publications

SRID: State Relation Based Intrusion Detection for False Data Injection Attacks in SCADA

SRID: State Relation Based Intrusion Detection for False Data Injection Attacks in SCADA

Estimation of the dynamics of power grid operating parameters based on standard load curves

Hybrid method for power system state estimation

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