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      A Hybrid State Estimator Based on SCADA and PMU Measurements for Medium Voltage Distribution System

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      Applied Sciences

      MDPI AG

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          Abstract

          With the increasing importance of renewable energy and flexible loads, the operation of the distribution system is becoming more stochastic and complex, and it is necessary to monitor the power system in real-time. Considering the gradual applications of intelligent electronic devices in the distribution systems, a hybrid state estimator based on supervisory control and data acquisition (SCADA) and phasor measurement unit (PMU) measurements is proposed in this paper, which consists of the improved robust estimation and linear state estimation. At the time of SCADA data acquisition, the improved robust estimation combining the SCADA measurements with PMU measurements is performed. To eliminate the effect of bad data, the internal student residual method is introduced, and the robust thresholds are adjusted adaptively. Then the linear state estimation is performed at the time of PMU data acquisition based on the results of the previous estimation time and the PMU measurements, which can quickly correct the robust estimation results and track the changes of the distribution system. Finally, the effectiveness and performance of the proposed method are verified in a modified IEEE 33-bus distribution system and a real distribution system in China.

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          Most cited references 34

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          A Review on Distribution System State Estimation

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            Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids

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              Efficient Branch-Current-Based Distribution System State Estimation Including Synchronized Measurements

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                Author and article information

                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                September 2018
                September 01 2018
                : 8
                : 9
                : 1527
                Article
                10.3390/app8091527
                © 2018
                Product
                Self URI (article page): http://www.mdpi.com/2076-3417/8/9/1527

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