PowerZone: Artificial intelligence educational modules for power engineering

Keith Holbert; Gerald T. Heydt; George G. Karady; Daniel Tylavsky

PowerZone: Artificial intelligence educational modules for power engineering

Keith Holbert, Gerald T. Heydt, George G. Karady, Daniel Tylavsky

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

PowerZone is an NSF sponsored Combined Research and Curriculum Development project aimed at improving electric power industry competitiveness. Educational modules centered on artificial intelligence (AI) techniques are introduced into senior-level undergraduate and first-year graduate power engineering courses. Four independent modules are developed including: (1) fuzzy logic for decision making and signal processing, (2) visualization and intelligent systems for electrical power quality studies, (3) short term load forecasting using neural networks and fuzzy logic, and (4) fast simulation through sparsity coding visualization. These modules are being disseminated using the project initiated PowerZone website located at http://ceaspub.eas.asu.edu/PowerZone/.

Original language	English (US)
Title of host publication	ASEE Annual Conference Proceedings
Pages	7817-7826
Number of pages	10
State	Published - 2001
Event	2001 ASEE Annual Conference and Exposition: Peppers, Papers, Pueblos and Professors - Albuquerque, NM, United States Duration: Jun 24 2001 → Jun 27 2001

Other

Other	2001 ASEE Annual Conference and Exposition: Peppers, Papers, Pueblos and Professors
Country	United States
City	Albuquerque, NM
Period	6/24/01 → 6/27/01

ASJC Scopus subject areas

Engineering(all)

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title = "PowerZone: Artificial intelligence educational modules for power engineering",

abstract = "PowerZone is an NSF sponsored Combined Research and Curriculum Development project aimed at improving electric power industry competitiveness. Educational modules centered on artificial intelligence (AI) techniques are introduced into senior-level undergraduate and first-year graduate power engineering courses. Four independent modules are developed including: (1) fuzzy logic for decision making and signal processing, (2) visualization and intelligent systems for electrical power quality studies, (3) short term load forecasting using neural networks and fuzzy logic, and (4) fast simulation through sparsity coding visualization. These modules are being disseminated using the project initiated PowerZone website located at http://ceaspub.eas.asu.edu/PowerZone/.",

author = "Keith Holbert and Heydt, {Gerald T.} and Karady, {George G.} and Daniel Tylavsky",

year = "2001",

language = "English (US)",

pages = "7817--7826",

booktitle = "ASEE Annual Conference Proceedings",

note = "2001 ASEE Annual Conference and Exposition: Peppers, Papers, Pueblos and Professors ; Conference date: 24-06-2001 Through 27-06-2001",

}

TY - GEN

T1 - PowerZone

T2 - 2001 ASEE Annual Conference and Exposition: Peppers, Papers, Pueblos and Professors

AU - Holbert, Keith

AU - Heydt, Gerald T.

AU - Karady, George G.

AU - Tylavsky, Daniel

PY - 2001

Y1 - 2001

N2 - PowerZone is an NSF sponsored Combined Research and Curriculum Development project aimed at improving electric power industry competitiveness. Educational modules centered on artificial intelligence (AI) techniques are introduced into senior-level undergraduate and first-year graduate power engineering courses. Four independent modules are developed including: (1) fuzzy logic for decision making and signal processing, (2) visualization and intelligent systems for electrical power quality studies, (3) short term load forecasting using neural networks and fuzzy logic, and (4) fast simulation through sparsity coding visualization. These modules are being disseminated using the project initiated PowerZone website located at http://ceaspub.eas.asu.edu/PowerZone/.

AB - PowerZone is an NSF sponsored Combined Research and Curriculum Development project aimed at improving electric power industry competitiveness. Educational modules centered on artificial intelligence (AI) techniques are introduced into senior-level undergraduate and first-year graduate power engineering courses. Four independent modules are developed including: (1) fuzzy logic for decision making and signal processing, (2) visualization and intelligent systems for electrical power quality studies, (3) short term load forecasting using neural networks and fuzzy logic, and (4) fast simulation through sparsity coding visualization. These modules are being disseminated using the project initiated PowerZone website located at http://ceaspub.eas.asu.edu/PowerZone/.

UR - http://www.scopus.com/inward/record.url?scp=8744253808&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=8744253808&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:8744253808

SP - 7817

EP - 7826

BT - ASEE Annual Conference Proceedings

Y2 - 24 June 2001 through 27 June 2001

ER -

PowerZone: Artificial intelligence educational modules for power engineering

Abstract

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ASJC Scopus subject areas

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