Online Least-squares One-class SVM for Outlier Detection in the Power GridDate: 2016-10-05 Add to Google Calendar
Time: 4:00pm - 5:00pm
Location: Holmes Hall 389
Speaker: Sharif Uddin, PhD student, University of Hawaiʻi Electrical Engineering
Accurate real-time estimation of the states is of critical importance in the operation of the power grid. The quality of the state estimates is essentially dependent on ensuring the integrity of the measurement data. In our research we study the detection of gross measurement errors and hidden data attacks in the power system as an online outlier detection problem. A sparse online least squares one-class support vector machine classi cation algorithm is developed to detect outliers in a data stream to provide real-time quality information in a decentralized manner, before the data is fed into the computationally expensive state estimator. An approximate linear dependence criteria is used to obtain a sparse solution by sequentially processing each data point only once, keeping with the requirement of data processing over a data stream. The performance of the proposed algorithm in detecting bad data and false data injection attacks is veri ed through simulations on IEEE benchmark test systems.