Details

Autor(en) / Beteiligte

• Uddin, Muhammad Sharif
• Kuh, Anthony
• Yang Weng
• Ilic, Marija

Titel

Online bad data detection using kernel density estimation

Ist Teil von

• 2015 IEEE Power & Energy Society General Meeting, 2015, p.1-5

Ort / Verlag

IEEE

Erscheinungsjahr

2015

Quelle

IEEE Xplore

Beschreibungen/Notizen

• This paper addresses the problem of bad data detection in the power grid. An online probability density based technique is presented to identify bad measurements within a sensor data stream in a decentralized manner using only the data from the neighboring buses and a one-hop communication system. Analyzing the spatial and temporal dependency between the measurements, the proposed algorithm identifies the bad data. The algorithm was then tested on the IEEE 14-bus test system where it demonstrated superior performance detecting critical and multiple bad data compared to the largest normalized residual test.