Economic Dispatch with Minimization of Power Transmission Losses Using Penalty-Function Nonlinear Programming Neural Network
Summary :
This study describes the feasibility of using the penalty-function nonlinear programming neural network method to find the optimal power generating output which minimizes both the costs of generating power and power transmission losses. This method depends on neural network technology in acquiring exterior penalty function. Employing nonlinear function in equality and inequality constraints, the model is established using a neural network and additional objective functions; these additional objective functions expand cost function by using an appropriate penalty function. In this study, a 26-busbar including six generators was used to test the penalty function nonlinear programming neural network method. A comparison with the sequential unconstrained minimization technique (SUMT) demonstrates the reliability and precision of the optimal solution obtained using the new method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.9 pp.2303-2308
- Publication Date
- 2003/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
- Optimization and Control
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Sy Ruen HUANG, Shou-Shian WU, Chien-Cheng YU, Shiun-Tsai LIU, "Economic Dispatch with Minimization of Power Transmission Losses Using Penalty-Function Nonlinear Programming Neural Network" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 9, pp. 2303-2308, September 2003, doi: .
Abstract: This study describes the feasibility of using the penalty-function nonlinear programming neural network method to find the optimal power generating output which minimizes both the costs of generating power and power transmission losses. This method depends on neural network technology in acquiring exterior penalty function. Employing nonlinear function in equality and inequality constraints, the model is established using a neural network and additional objective functions; these additional objective functions expand cost function by using an appropriate penalty function. In this study, a 26-busbar including six generators was used to test the penalty function nonlinear programming neural network method. A comparison with the sequential unconstrained minimization technique (SUMT) demonstrates the reliability and precision of the optimal solution obtained using the new method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_9_2303/_p
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@ARTICLE{e86-a_9_2303,
author={Sy Ruen HUANG, Shou-Shian WU, Chien-Cheng YU, Shiun-Tsai LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Economic Dispatch with Minimization of Power Transmission Losses Using Penalty-Function Nonlinear Programming Neural Network},
year={2003},
volume={E86-A},
number={9},
pages={2303-2308},
abstract={This study describes the feasibility of using the penalty-function nonlinear programming neural network method to find the optimal power generating output which minimizes both the costs of generating power and power transmission losses. This method depends on neural network technology in acquiring exterior penalty function. Employing nonlinear function in equality and inequality constraints, the model is established using a neural network and additional objective functions; these additional objective functions expand cost function by using an appropriate penalty function. In this study, a 26-busbar including six generators was used to test the penalty function nonlinear programming neural network method. A comparison with the sequential unconstrained minimization technique (SUMT) demonstrates the reliability and precision of the optimal solution obtained using the new method.},
keywords={},
doi={},
ISSN={},
month={September},}
Copy
TY - JOUR
TI - Economic Dispatch with Minimization of Power Transmission Losses Using Penalty-Function Nonlinear Programming Neural Network
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2303
EP - 2308
AU - Sy Ruen HUANG
AU - Shou-Shian WU
AU - Chien-Cheng YU
AU - Shiun-Tsai LIU
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2003
AB - This study describes the feasibility of using the penalty-function nonlinear programming neural network method to find the optimal power generating output which minimizes both the costs of generating power and power transmission losses. This method depends on neural network technology in acquiring exterior penalty function. Employing nonlinear function in equality and inequality constraints, the model is established using a neural network and additional objective functions; these additional objective functions expand cost function by using an appropriate penalty function. In this study, a 26-busbar including six generators was used to test the penalty function nonlinear programming neural network method. A comparison with the sequential unconstrained minimization technique (SUMT) demonstrates the reliability and precision of the optimal solution obtained using the new method.
ER -
English
