Online least-squares one-class support vector machine for outlier detection in power grid data

This paper addresses the problem of outlier detection in the power grid. A sparse online least squares one-class support vector machine classification algorithm is presented to detect outliers in a data stream. 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. Experiments were conducted on a two dimensional synthetic data set and for bad data in a critical measurement in the IEEE 14 bus system to evaluate the performance of the proposed algorithm.