A novel state estimation method based on topological contraction for distribution networks

A practical SE (state estimation) method based on multiple data source is proposed. With the help of Decision Theory the bad data identification method which combines the historical data and remote data from IDP (Integrated Data Platform) and 6 data rules are proposed. A logical judgment based on the 6 data rules is conducted, and the data quality evaluation method is established accordingly. The data repairing improves the quality of the input data for state estimation. The weighed least-square state (WLS) estimation takes into account the reliability of the measurements, reducing the influence of bad data. Research shows that this method is superior to traditional state estimation methods in that it tackles with bad data more efficiently, resulting in more compatible state information for advanced applications. Experiments in the distribution network of DBC of Shanghai Pudong District prove the method is reliable and efficient.