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Translated title of the contribution: Sensor validation based on the artificial neural networks in power plant monitoring

Burak Barutçu*, Erdinç Türkcan, Serhat Şeker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, the data acquisition system of the Netherlands EPZ Borssele Nuclear Power Plant (NPP) is considered and studied for signal validation and verification purposes. The collected data is separated into two groups as AC and DC-signals. Here AC-signals are pre-processed before being sampled for noise analysis studies, while DC-signals are sampled and recorded as received. With the aim of sensor validation, sensor response times were determined using autoregressive model on pre-processed sensor signals (AC-signals), Artificial Neural Networks (ANNs) are applied to AC-signals and Auto-Associative-ANN and Multi Input Single Output (MISO) ANN structures are implemented to solve the sensor validation problem. Furthermore MISO and Multi Input Multi Output (MIMO) ANNs are used for the component based validation study in the plant-wide monitoring.

Translated title of the contributionSensor validation based on the artificial neural networks in power plant monitoring
Original languageTurkish
Title of host publicationProceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Pages237-240
Number of pages4
DOIs
Publication statusPublished - 2005
EventIEEE 13th Signal Processing and Communications Applications Conference, SIU 2005 - Kayseri, Turkey
Duration: 16 May 200518 May 2005

Publication series

NameProceedings of the IEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Volume2005

Conference

ConferenceIEEE 13th Signal Processing and Communications Applications Conference, SIU 2005
Country/TerritoryTurkey
CityKayseri
Period16/05/0518/05/05

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