NDLI: Adaptive artificial neural network for robust estimation of parameters of asynchronous state

Content Provider	IEEE Xplore Digital Library
Author	Kostyla, P. Sikorski, T. Waclawek, Z. Leszkiewicz, B.
Copyright Year	2012
Description	Author affiliation: Department of Electrical Engineering, Wroclaw University of Technology, Poland (Kostyla, P.; Sikorski, T.; Waclawek, Z.; Leszkiewicz, B.)
Abstract	In many applications, very fast methods are required for estimating of amplitudes of current signals distorted by noise. In this paper new parallel algorithms are proposed, which can be implemented by analogue adaptive circuits employing selected neural networks principles. Algorithms based on the total least-squares (TLS) and the robust total least-squares (RTLS) criteria are developed and used to detection of asynchronous state of synchronous machines. The problems are formulated as optimization tasks and solved using the steepest descent continuous-time optimization algorithm. The corresponding architectures of analogue neuron-like adaptive processors are also shown. The developed networks are more robust against noise in the measured signal than other known neural network algorithms. The network based on the TLS criterion realizes the optimization process under the assumption that the signal model can also be deteriorated (frequency or sampling interval fluctuation and so forth). The TLS estimation effect is better and more reliable than the corresponding LS structure, when higher sampling frequency and a wider sampling window is applied. An asynchronous state of a synchronous machine may be identified through determining the amplitudes of particular components of stator's current provided that a constant slip value is assumed. Following a synchronism loss, this adopted value is assumed to be achieved and, for sure, exceeded. This work provides a description of artificial neural networks realising mentioned task of asynchronous state detection. Extensive computer simulations confirm the validity and performance of the proposed algorithms.
Starting Page	261
Ending Page	265
File Size	846666
Page Count	5
File Format	PDF
ISBN	9781457718304
e-ISBN	9781457718298
DOI	10.1109/EEEIC.2012.6221585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-18
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Estimation Signal processing algorithms Stators Signal processing Robustness Neural network Synchronous machines Parallel algorithms Optimization Synchronous generator Optimization problem
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in