Hybrid State Estimation for Distribution Systems With AMI and SCADA Measurements

Manyun Huang,Zhinong Wei,Haixiang Zang,Guoqiang Sun

doi:10.1109/access.2019.2937096

Manyun Huang, Zhinong Wei + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2937096

Copy DOI

Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 32	License type: CC BY 4.0

Affiliation: Hohai University

Abstract

The number of real-time supervisory control and data acquisition (SCADA) measurements in power distribution systems is scarce. This limits the reliability of state estimation (SE) results for distribution systems. Therefore, some studies seek to enhance the observability and SE accuracy of distribution systems by incorporating advanced metering infrastructure (AMI) data with the SCADA measurements. However, the hourly updated AMI data may be too coarse to capture system changes, especially in the presence of intermittent renewable energy sources. This issue is addressed by proposing a hybrid SE framework integrating a data-driven estimator and a model-based estimator. To be specific, the data-driven estimator combined with a topology identification method is presented to solve the DSSE problem between AMI scans, and the model-based estimator is employed to ensure robust estimation results against gross errors at a lower time scale. The proposed hybrid SE switches from the data-driven estimator to the model-based estimator once the AMI data is updated. Such a solution allows for capturing system changes at different time scales and improving the real-time and reliability of distribution system state estimation. Simulations are conducted on a sample distribution system to illustrate the characteristics of the proposed hybrid SE.

Highlights

Distribution system state estimation (DSSE) is a challenging task due to the scarcity of real-time measurements [1]
We propose a hybrid SE for distribution systems, which estimates the system states with the synergistic utilization of advanced metering infrastructure (AMI)
The main contribution of this work is the incorporation of a deep neural networks (DNN)-based estimator method and a weighted least absolute value (WLAV)-based estimator method to handle the unsynchronized measurements and to capture system changes at different time scales

Summary

Introduction

Distribution system state estimation (DSSE) is a challenging task due to the scarcity of real-time measurements [1]. The main contributions of the present work are as follows: 1) A hybrid SE framework integrating a data-driven estimator and a model-based estimator is proposed to handle the unsynchronized AMI and SCADA measurements and to capture system changes at different time scales. 2) A data-driven estimator combined with a topology identification method is built for distribution systems with a limited number of SCADA measurements to track system states quickly.

Results

Conclusion