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Title: Application of genetic based fuzzy systems to hydroelectric generation scheduling

Abstract

An application of genetic based fuzzy systems to hydroelectric generation scheduling is presented in this paper. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm was included to further enhance the process of tuning membership functions. By this way, membership mappings for those important parameters can be optimally adjusted. The computation performance is thus improved. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results have demonstrated are feasibility and effectiveness of the proposed approach for the applications.

Authors:
Publication Date:
Research Org.:
National Cheng Kung Univ., Tainan (TW)
OSTI Identifier:
20001184
Alternate Identifier(s):
OSTI ID: 20001184
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)
Additional Journal Information:
Journal Volume: 14; Journal Issue: 3; Other Information: PBD: Sep 1999; Journal ID: ISSN 0018-9383
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING AND POLICY; 13 HYDRO ENERGY; HYDROELECTRIC POWER PLANTS; OPERATION; PROGRAM MANAGEMENT; CONTROL THEORY; ARTIFICIAL INTELLIGENCE; TAIWAN; FUZZY LOGIC

Citation Formats

Huang, S.J. Application of genetic based fuzzy systems to hydroelectric generation scheduling. United States: N. p., 1999. Web.
Huang, S.J. Application of genetic based fuzzy systems to hydroelectric generation scheduling. United States.
Huang, S.J. Wed . "Application of genetic based fuzzy systems to hydroelectric generation scheduling". United States.
@article{osti_20001184,
title = {Application of genetic based fuzzy systems to hydroelectric generation scheduling},
author = {Huang, S.J.},
abstractNote = {An application of genetic based fuzzy systems to hydroelectric generation scheduling is presented in this paper. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm was included to further enhance the process of tuning membership functions. By this way, membership mappings for those important parameters can be optimally adjusted. The computation performance is thus improved. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results have demonstrated are feasibility and effectiveness of the proposed approach for the applications.},
doi = {},
journal = {IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)},
issn = {0018-9383},
number = 3,
volume = 14,
place = {United States},
year = {1999},
month = {9}
}