NDLI: Transaction based power flow solution and transmission loss allocation using neural network

Content Provider	IEEE Xplore Digital Library
Author	De, M. Choudhury, N.B.D. Goswami, S.K.
Copyright Year	2010
Description	Author affiliation: Department of Electrical Engineering, NIT Silchar, Assam, India (Choudhury, N.B.D.) \|\| Electrical Engineering Department of Jadavpur University, Kolkata, India-7000032 (De, M.; Goswami, S.K.)
Abstract	Recovering transmission loss cost accurately and transparently is an important challenge in deregulated power market. Though a generally acceptable method for transmission loss allocation is yet to be found out, proportional allocation technique has some popularity due to its uniqueness in the sense that it can neither be proved nor disproved. This method however is not applicable to transaction based power system structure, due to lack of a power flow tool for transaction based operation of power system. This paper proposes an application of proportional sharing principle to transmission loss allocation for transaction based power system. The unavailability of a power flow method for transaction based power system operation is addressed by framing a neural network. The neural network allocates losses to transactions and also generates the bus voltage magnitudes. Thus, it serves the purpose of both loss allocation and transaction based power flow tool.
Starting Page	1
Ending Page	6
File Size	428422
Page Count	6
File Format	PDF
ISBN	9781424477821
e-ISBN	9781424477814
DOI	10.1109/PEDES.2010.5712493
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-20
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load flow Artificial neural networks Resource management Generators Propagation losses Training neural network Power Transaction Loss allocation transaction based power flow proportional sharing power tracing
Content Type	Text
Resource Type	Article

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