Application of Kalman Filter to Bad-Data Detection in Power System

Huang, Chien-Hung; Shih, Kuang-Kong; Lee, Chien-Hsing; Wang, Yaw-Juen

doi:10.5772/9591

Cited by 8 publications

(10 citation statements)

References 17 publications

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“…Moreover, a dynamic detection method for BDI is proposed. Huang [19] introduced the Kalman Filter and Artificial Neural Network (ANN) into bad data detection. The state estimation is carried out with Extended Kalman Filter and the trained ANN to detect the bad data from the raw measurements.…”

Section: )mentioning

confidence: 99%

“…Many efforts have been devoted to defend the system from BDI attacks on the physical side [4,5,8] [16][17][18][19], utilizing the information from historical data, the installation of Phasor Measuring Unit (PMU) and the topology of power grid. From another perspective, to ensure the correct functioning of smart grid, it is essential that communications are secured [12,20].…”

Section: Introductionmentioning

confidence: 99%

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A survey on bad data injection attack in smart grid

Wang

Guan

Liu

et al. 2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Bad data injection (BDI) is one of the most threatening attacks in smart grid, as it may cause energy theft of end users, false dispatch on the distribution process, and device breakdown during power generation. In this paper, the BDI attack is defined as a cyber-physical attack which is a combination of two aspects: 1) on the cyber side, modern attack techniques are exploited to intrude and inject bad data into the information system; 2) on the physical side, attackers construct the bad data to bypass the traditional error detection in power systems. Related work on BDI construction and implementation are reviewed. An attack simulation is constructed to illustrate how to launch a BDI attack. The countermeasures against the BDI are also summarized from the views of cyber-orientation, physical-orientation. Finally, our work on cyber-physical fusion detection is presented.Index Terms--smart grid; security; bad data injection; cyberphysical fusion.

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Section: )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A survey on bad data injection attack in smart grid

Wang

Guan

Liu

et al. 2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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“…The measurement noise covariance matrix (16) will influence the weight given to a new measurements during the prediction step (6). The more its value increases, the less the sensor data will be taken into account.…”

Section: ) Predictionmentioning

confidence: 99%

“…It is the correct setting of r which allows bad-data detection. Its determination is performed empirically but the good value is usually close to 10 times the sensor standard deviation [6] r = 10 × σ SOW Figure 4 shows an error condition detected with the filter that has been set up. While the boat was under way without making any particular manoeuvre, the raw speed suddenly drops to 5 knots.…”

Section: ) Fault Conditionmentioning

confidence: 99%

Speedometer fault detection and GNSS fusion using Kalman filters

Kerhascoet

Laurent

Senn

et al. 2016

OCEANS 2016 MTS/IEEE Monterey

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“…BACKGROUND AND RELATED WORK As described in [4], Kalman ltering techniques are extensively used for power system state estimation. Recent work [5] also employed them for the detection of bad data (outliers) in power systems.…”

Section: Introductionmentioning

confidence: 99%

Power system state estimation with dynamic optimal measurement selection

Zhang

Welch

Bishop

2011

2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG)

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Abstract-Power system measurement devices continue to evolve towards higher accuracy and update rate. On the other hand, the computation required for processing the enormous amounts of measurement data associated with large complex power systems makes real-time estimation a major challenge. In this paper we present the Lower Dimensional Measurementspace (LoDiM) state estimation method for large-scale and widearea interconnected power systems. We present the method in the context of the Kalman lter and Extended Kalman lter, however our measurement selection procedure is not lter-specic, i.e. it can also be applied on other state estimation methods such as particle lters and unscented lters. Our method can also take advantage of large-scale parallel computation techniques for further improvement. Moreover, the concept of LoDiM should be applicable to other large-scale, real-time and computationallyintensive state tracking systems beyond the power systems, such as weather forecasting systems, gas-pipeline systems, and other critical infrastructure.

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Application of Kalman Filter to Bad-Data Detection in Power System

Cited by 8 publications

References 17 publications

A survey on bad data injection attack in smart grid

A survey on bad data injection attack in smart grid

Speedometer fault detection and GNSS fusion using Kalman filters

Power system state estimation with dynamic optimal measurement selection

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