Robust State Estimation For Distribution Systems And Its Distributed Parallel Computing

Distribution Automation has become an inevitable trend in the development of distribution system. It puts forward higher requirements for distribution system data in integrity, accuracy and timeliness. In the current status of distribution system measurement configuration, there are still many drawbacks of traditional state estimation for distribution system in terms of robustness and adaptability. In addition, with the rapid growth of electricity demand, the node matrix scale of distribution system keeps increasing. Traditional serial algorithms can’t meet the speed needs of real-time analysis and security control of distribution system. For the above two issues, this thesis studies robust state estimation for distribution system and its distributed parallel computing.Current-measurements-accommodating MES robust state estimation has been taken to estimate feeders with many current amplitude measurements. On its basis, power-measurements-accommodating MES robust state estimation has been proposed to realize the state estimation for feeders with many power measurements. The combination of the two algorithms can achieve state estimation for feeders with different measurement configurations. The calculation example shows that improved MES robust state estimation gets good performance in aspects of calculation accuracy and comlexity, and has more advantages of robustness.To achieve real-time state estimation of large-scaled distribution systems, a data-level-based distributed parallel computing platform has been built. ZeroMQ is used to support the communication architecture. CIM model is taken for data processing. With the MES robust state estimation of a feeder being a sub-task, MES robust state estimation of the whole distribution system is achieved on this platform. The calculation example shows that with this platform, calculation for a certain-scaled distribution system takes a significant advantage in speed relative to traditional ones.