NDLI: Hopfield neural network approach to the solution of economic dispatch and unit commitment

Content Provider	IEEE Xplore Digital Library
Author	Valsan, S.P. Swarup, K.S.
Copyright Year	2004
Description	Author affiliation: Sch. of Electr. & Electron. Engg., Shastra Deemed Univ., Tamil Nadu, India (Valsan, S.P.)
Abstract	A new method for the solution of the problems of unit commitment (UC) and economic dispatch (ED) using Hopfield neural network (HNN) is proposed in this paper. The difficulty in combining these problems is that while the first one requires a discrete neuron model, the latter requires a continuous neuron model. The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated. The important contribution of this work is the proposal of a new architecture for the discrete HNN for UC and the output of the UC module is used as input to the continuous HNN for ED. The associated advantage of using HNN for the combined solution of UC and ED is the decoupling of their interdependency i.e:, both the UC and ED are iteratively solved using respective HNN for the particular period. The implementation of the proposed method causes a considerable reduction in the HNN size and hence complexity and computation requirements, compared to earlier attempts. The method was successfully tested for different cases (3,10,11 and 26 generator units), with varying load pattern of different durations (24 and 168 hours).
Sponsorship	Faculty of Eng., Univ. of Melbourne IEEE Madras Section, Chennai
Starting Page	311
Ending Page	316
File Size	531387
Page Count	6
File Format	PDF
ISBN	0780382439
DOI	10.1109/ICISIP.2004.1287673
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Costs Job shop scheduling Neurons Hopfield neural networks Power generation economics Artificial neural networks Power system economics Fuel economy Power systems Power generation
Content Type	Text
Resource Type	Article

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