A pattern analysis approach for topology determination, bad data correction and missing measurement estimation in power systems

Abstract

The authors propose a novel methodology for the combined solution of the topological identification, observability analysis and bad data processing problems in power systems. The idea is to provide, in a very fast way, a reliable input database for the state estimator, without interacting with it. The solution is based on a pattern analysis approach. An efficient framework for solving data acquisition and processing problems, joining pattern analysis and analytical procedures, is suggested. Two different techniques of pattern analysis are combined to produce a classifier and an estimator with unique characteristics to deal with noisy environments. The patterns required for the training process can be acquired from the SCADA (supervisory control and data acquisition) system and/or from load-flow simulations. Test results have been obtained for the IEEE 24-bus reliability test system.