White Papers

Application of Data Mining to Optimize Settings for Generator Tripping and Load Shedding System (RPTC) in Emergency Control at Hydro-Quebec

Category: Data Management

Tags: software, data

Overview This paper describes the on-going work done by Hydro-Quebec to optimize the settings of automatic devices installed in its main power plants to maintain secure operation under extreme contingencies. The automatic generator tripping and load shedding system (RPTC) described in this paper is installed at the Churchill Falls hydroelectric power plant (5500 MW) in Labrador. Data mining techniques like Decision Trees (DT) and Regression Trees (RT) have been used. Real time snapshots of the Hydro-Quebec power system collected over a 5 year period have been used to generate large amounts of results by transient stability simulations. The processing of these data has been done using software developed by the University of Liege.

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Publisher
University of Liege
File Format
PDF
Date Published
Sep 19, 2008
Format
White Papers
Topics
Knowledge and Data Management, Data Mining - Analysis

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