Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Conference · Thu May 07 04:00:00 EDT 2020

DOI:https://doi.org/10.1109/ISGT45199.2020.9087787· OSTI ID:1669506

Zhao, Junbo; Huang, Can; Mili, Lamine; Zhang, Yingchen; Min, Liang

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Electricity, Advanced Grid Modeling Program
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1669506
• Report Number(s): NREL/CP-5D00-77386; MainId:26332; UUID:b32ef4c6-4f29-47b7-b273-5f6a5ee92f0f; MainAdminID:14110
• Country of Publication: United States
• Language: English

Related Subjects

POWER TRANSMISSION AND DISTRIBUTION
distribution system state estimation
machine learning
pseudo measurement
real-time measurement
smart meter