Load Profiling using Grey Relational Analysis for Power System State Estimation

Power system state estimation (PSSE) is a critical tool for power system operation. Load/generation profiles are essential for performing accurate PSSE, and enable PSSE algorithms to handle errors in real and pseudo measurements. The classic approach of modelling the measurement error in PSSE is applying the mixture reduction algorithm to Gaussian mixture models (GMMs) fitted to existent load/generation profiles. However, this approach has inherent limitations in the mixing process. We propose a novel algorithm based on grey relational analysis (GRA) to derive a smaller load/generation profile from the original extensive profiles based on the forecast results of the next day. Our algorithm addresses the issues of mixture reduction and is applied before mixture reduction to improve PSSE accuracy. A case study is presented to evaluate the performance of the proposed algorithm in improving the accuracy of PSSE.