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MS Final Exam – Galpottage Dilan Senaratne

Power Network Parameter Correction via Sparse Unsupervised Regression

The problem of correcting power network parameters and topology using multi-period SCADA measurements is considered. Starting from the current knowledge of parameter values, we formulate the parameter correction problem as a sparse unsupervised regression problem by exploiting the sparsity of the parameter errors. The advantage of the proposed approach is that it can localize and estimate parameter errors at the same time; there is no need for prior knowledge of error locations. Furthermore, the approach can be adapted to correct sparse errors in both parameters and topology simultaneously. We present an iterative parameter correction algorithm with convergence analysis and demonstrate its efficacy using the IEEE 14-bus test case.

Major Advisor: Jinsub Kim
Committee: Raviv Raich
Committee: Eduardo Cotilla-Sanchez
Committee: Xiao Fu
GCR: Leonard Coop

Tuesday, November 26 at 10:00am to 12:00pm

Kelley Engineering Center, 1007
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic

Research

Organization
Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

calvin.hughes@oregonstate.edu

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