Solving data acquisition and processing problems in power systems using a pattern analysis approach

Abstract

The paper proposes a new methodology for the combined solution of the topological identification observability analysis and bad data processing problems in power systems. The solution is based on a pattern analysis approach. An efficient framework is suggested for solving data acquisition and processing problems, as well as joining pattern analysis and analytical procedures. Two different techniques of pattern analysis are combined to produce a classifier and an estimator with unique characteristics to deal with noisy environments. Unobservable network areas, multiple interacting bad data and bad critical measurements can be efficiently treated. The patterns required for the training process can be acquired from the SCADA system and/or from load-flow simulations. Test results are presented for the IEEE 24-busbar reliability test system.