Type of Material: | Thesis |
Title: | Reinforcement learning approaches to power system scheduling |
Researcher: | Jasmin E A |
Guide: | Jagathy Raj, V P |
Department: | School of Engineering |
Publisher: | Cochin University of Science and Technology |
Place: | Cochin |
Year: | 29/12/2008 |
Language: | English |
Subject: | Power system | Reinforcement learning | Unit commitment | Economic dispatch | Automatic generation control |
Dissertation/Thesis Note: | PhD |
Fulltext: | Shodhganga |
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035 | __ | |a(IN-AhILN)th_258986 |
040 | __ | |aCUST_682022|dIN-AhILN |
041 | __ | |aeng |
100 | __ | |aJasmin E A|eResearcher |
110 | __ | |aSchool of Engineering|bCochin University of Science and Technology|dCochin |
245 | __ | |aReinforcement learning approaches to power system scheduling |
260 | __ | |aCochin|bCochin University of Science and Technology|c29/12/2008 |
502 | __ | |bPhD |
518 | __ | |oDate of Notification|d2008-12-29 |
520 | __ | |aOne major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power sch |
653 | __ | |aPower system |
653 | __ | |aReinforcement learning |
653 | __ | |aUnit commitment |
653 | __ | |aEconomic dispatch |
653 | __ | |aAutomatic generation control |
700 | __ | |aJagathy Raj, V P|eGuide |
856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/4870|yShodhganga |
905 | __ | |anotification |
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