NDLI: Neural Identification of Average Model of STATCOM using DNN and MLP

Content Provider	IEEE Xplore Digital Library
Author	Bina, M.T. Rahimzadeh, S.
Copyright Year	2007
Description	Author affiliation: K.N. Toosi Univ. of Technol., Tehran (Bina, M.T.; Rahimzadeh, S.)
Abstract	Modeling of STATCOM is conventionally performed in the time-domain. Amongst them, dq-theory is well-known in which state-space equations are used for the analysis. Power systems, however, use the frequency-domain information in phasor-related studies such as load flow analysis. Because time-domain models of FACTS controllers cannot be directly applied to the power system analysis, an intelligent model can usefully bridge the time-domain information to the corresponding frequency-domain data. This paper proposes two neural network identifiers based on the existing time-domain average model of STATCOM. Extended resultant bridge presents an average-neural model of STATCOM, which can be analytically applied to power systems. To this extent, design and development of two neural network identifiers are performed using the dynamic neural network (DNN) and the multi-layer perceptron (MLP). To verify the developed models, the exact solutions obtained from the average model of STATCOM are compared with the outcomes of the DNN and the MLP identifiers. Moreover performance of the two identifiers is accordingly compared as well.
Starting Page	1665
Ending Page	1669
File Size	2924408
Page Count	5
File Format	PDF
ISBN	9781424406449
DOI	10.1109/PEDS.2007.4487932
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-27
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Automatic voltage control Power system modeling Time domain analysis Power system analysis computing Neural networks Frequency domain analysis Bridges Power system dynamics Multi-layer neural network Equations STATCOM DNN FACTS controllers MLP modeling neural-averaging
Content Type	Text
Resource Type	Article

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