Abstract
State estimation application in the distribution system is being revisited with the availability of additional sensor data enabled by the distribution automation and massive sensors installation in the electric power grid. Some of these sensor data have measurement error, data quality problems, and different reporting rate. The traditional weighted least square estimation (WLSE) method is used to find best fit between the system model and the measured data. WLSE will typically give low performance and poor accuracy when some measurements have excessively large error compared to the rest of the measurements. This work proposes a maximum normal measurement rate (MNMR) based state estimation with simultaneous bad data detection method and considering three-phase unbalanced systems. The simulation results for an IEEE test system and an industrial feeder demonstrate the MNMR-based state estimation provides better performance even in the presence of multiple bad measurement data.
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