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## Bad data detection in the smart grid

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This chapter will discuss bad data detection techniques and their application in oscillation monitoring. Utilization of synchrophasor measurements for wide-area monitoring applications enables system operators to acquire real-time grid information. However, intentional injections of false synchrophasor measurements can potentially lead to inappropriate control actions, jeopardizing the security, and reliability of power transmission networks. An attacker can compromise the integrity of the monitoring algorithms by hijacking a subset of sensor measurements and sending manipulated readings. Such an approach can result to wide-area blackouts in power grids. This chapter considers bad data detection techniques with special focus on oscillation monitoring. To achieve an accurate supervision, a Bayesian inference technique has been discussed for each monitoring node using a distributed architecture.

Chapter Contents:

• 6.1 Introduction
• 6.2 Possible approaches
• 6.2.1 State estimation
• 6.2.2 Weighted least squares
• 6.2.3 Dynamic state estimation
• 6.2.4 Bad data analysis using chi-squared test and normalized residual test
• 6.3 Case study: oscillation monitoring
• 6.3.1 Consequences of an attack
• 6.3.2 Distinction between a fault and a cyber-attack
• 6.3.3 Difference from static monitoring applications
• 6.4 Modeling an attack in oscillation detection schemes
• 6.4.1 State-space representation of a power grid
• 6.4.2 Constraints of a power grid
• 6.4.3 Electromechanical oscillation model formulation
• 6.4.4 Characterization of an attack: an example
• 6.4.5 Significance of distributed architecture towards information of cyber-attack
• 6.4.6 Diagonalization of a system into subsystems
• 6.4.7 Detection bad data using initial observation analysis
• References

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