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Title: Parallel State Estimation Assessment with Practical Data

This paper presents a parallel state estimation (PSE) implementation using a preconditioned gradient algorithm and an orthogonal decomposition-based algorithm. The preliminary tests against a commercial Energy Management System (EMS) State Estimation (SE) tool using real-world data are performed. The results show that while the precondition gradient algorithm can solve the SE problem quicker with the help of parallel computing techniques, it might not be good for real-world data due to the large condition number of gain matrix introduced by the wide range of measurement weights. With the help of PETSc package and considering one iteration of the SE process, the orthogonal decomposition-based PSE algorithm can achieve 5-20 times speedup comparing against the commercial EMS tool. It is very promising that the developed PSE can solve the SE problem for large power systems at the SCADA rate, to improve grid reliability.
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Conference: IEEE Power and Energy Society General Meeting (PES), July 21-25, 2013, Vancouver, BC, Canada, 1-5
Institute of Electrical and Electronics Engineers, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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Country of Publication:
United States
State Estimation; Preconditioned Conjugated Gradient; Orthogonal Decomposition; High Performance Computing