New reduced model approach for power system state estimation using artificial neural networks and principal component analysis

In this paper a new technique using artificial neural networks and principal component analysis for power system state estimation is presented. This method is applicable to both conventional and renewable energy systems. It does not require network observability analysis and uses fewer measurement variables than conventional techniques. This approach has been successfully implemented on an IEEE 14-bus power system and the results show that this method is very accurate and is ideal for smart grid applications.