Power Systems Engineering Research Center

S-71 Project Summary

Real-time Synchrophasor Data Quality Analysis and Improvement

Summary This project aims at developing strategies for real-time data quality management of streaming PMU data. With the recent impetus towards design and adoption of synchrophasor-based applications in the power industry, there is an urgent need to develop online techniques for detecting, analyzing, and mitigating bad as well as missing data in real-time streams. In this project, we will build a systematic online framework for identifying and handling typical data quality issues such as clock errors, transducer errors and network delays. Based on the synchrophasor data¿s spatio-temporal correlations, the proposed approach is capable of identifying bad data during both normal and fault-on conditions. Real-world synchrophasor data as well as synthetic dynamic grid models will be used to differentiate the root causes of data quality issues and to validate the proposed strategies.
Academic Team Members Project Leader: Le Xie (Texas A&M University, le.xie@tamu.edu)
Team Members: P. R. Kumar (Texas A&M University, prk@tamu.edu)
Mani Venkatasubramanian (Washington State University, mani@eecs.wsu.edu)
Industry Team Members Xiaoming Feng (ABB), Gurudatha Pai (Alstom), Vijay Sukhavasi (Alstom), Prashant Kansal (AEP), Giuseppe Stanciulescu (BC Hydro), Aftab Alam (California ISO), Alan Engelmann (ComEd), Floyd Galvan (Entergy), Jay Ramamurthy (Entergy), Mahendra Patel (EPRI), Evangelos Farantatos (EPRI), Chaitanya A. Baone (GE Global Research), Santosh Veda (GE Global Research), Di Shi (GEIRI North America), Frankie Zhang (ISO New England), Liang Min (LLNL), Yingchen Zhang (NREL), Naim Logic (SRP), Jay Caspary (SPP), Harvey Scribner (SPP)
Project Period July 1, 2016 to August 31, 2018