Using State Estimation Residuals to Detect Abnormal SCADA Data
Detection of manipulated supervisory control and data acquisition (SCADA) data is critically important for the safe and secure operation of modern power systems. In this paper, a methodology of detecting manipulated SCADA data based on state estimation residuals is presented. A framework of the proposed methodology is described. Instead of using original SCADA measurements as the bad data sources, the residuals calculated based on the results of the state estimator are used as the input for the outlier detection process. The BACON algorithm is applied to detect outliers in the state estimation residuals. The IEEE 118-bus system is used as a test case to evaluate the effectiveness of the proposed methodology. The accuracy of the BACON method is compared with that of the 3-σ method for the simulated SCADA measurements and residuals.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 994037
- Report Number(s):
- PNNL-SA-69779
- Resource Relation:
- Conference: 2010 IEEE PES Transmission and Distribution Conference and Exposition
- Country of Publication:
- United States
- Language:
- English
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