For many expectant parents, the first opportunity to "meet" their baby comes at 20-weeks of pregnancy. The ultrasound scan performed at that time gives the parents a sense of the health of the growing fetus. The images produced in this important exam reveal the shape and structure of the head and brain, which are of particular interest because severe brain problems may become visible at this stage in the pregnancy. As the brain develops, maternal-fetal specialists keep a close eye on the cerebellum – the part of the brain that coordinates and regulates muscular activity. A healthy cerebellum can typically rule out fetal complications, such as spina bifida – a neural tube defect in which the spinal cord fails to properly develop.

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.