Research Of Practical State Estimation Method Of Distribution Networks

State estimation processes a given set of measurements to give thebest estimate of the state of the system. Implicitly assumed in theformulation is that the errors are small. Occasionally, large error or baddata does occur due to meter failure or other reasons. It is very importantto detect and identify the presences of any such bad data, and remove allthe bad data so that they would not corrupt the result of state estimation.As a matter of fact, no perfect solution has been implied in distributionsystem so far in practice.With compatible measurement and appropriate measurement systemconfiguration, a reliable real-time database can be achieved by anordinary state estimation algorithm. However, the errors in measurementscan not be ignored. Lack of incompatible redundant measurementscreates inadequate performance of traditional SE algorithm. The standarderror of measurement system in power systems, denoted by σ, is about0.5%-2%of normal measurements. So when the error is greater than±3σ of the measured value, the measurement can be known as the bad data. In practice, measurements with error larger than±(6-7) σ are taken asbad data. In many cases, a reliable database for SE can be reached onlywith the processing of bad data detection and identification, and datarepairing.A practical SE (state estimation) method based on multiple datasource is proposed. The approach borrows the framework from ID3Decision Theory. The bad data identification combines the historical dataand remote data from IDP (Integrated Data Platform) as judgment bases.The least-square state estimation takes into account the reliability of themeasurements. An efficient quality valuation algorithm greatly reducingthe risk of the erroneous judgment is developed. Research shows that thismethod is superior to traditional state estimation methods in that it tackleswith bad data more efficiently. The judgment bases are complementary.The measurement transformation method is time saving. The distributionnetwork of DBC of Shanghai Pudong District proves the method isreliable and efficient.