Serhat ŞEKER, Melih GEÇKİNLİ, Burak BARUTÇU

Nükleer güç santrallarında sensör güvenirliliğinin reaktör işletimine etkileri

Bu çalışmada Hollanda EPZ Borssele Nükleer Güç Santralı 'nın yeni veri toplama sistemi incelenerek işaret doğrulama ve sağlaması için kullanılabilecek çeşitli metotlar ele alınmış ve bu amaca yönelik yeni yöntemler önerilmiştir. Sensör doğrulama amacıyla AC-işaretler üzerinde Öz-İlişkili Yapay Sinir Ağı (YSA) ve Çok Girişli Tek Çıkışlı YSA 'ları uygulanmıştır. Daha sonra, doğrulanmış kalp-dışı nötron sensörlerinin işaretleri kullanılarak kalp kazanı titreşim analizi yapılmıştır. Ayrıca santral genelinde bileşen tabanlı doğrulama yapmak için DC-işaretler üzerinde Çok Girişli Çok Çıkışlı YSA 'ları kullanılmıştır. DC-işaretlerde çevrim-içi anormal durumların belirlenmesi problemine yeni bir çözüm yolu olarak dalgacık analizi kullanımı önerilmiş ve Kalman filtresi uygulamasına üstünlüğü gösterilmiştir.

The effect of sensor reliability on reactor operation in nuclear power plants

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 auto-regressive model on pre-processed sensor signals (AC-signals), Artificial Neural Networks (ANNs) applied to AC-signals and Aulo-Associalive-ANN and Mulli Input Single Output (MISO) ANN structures are implemented to solve the sensor validation problem. Spectral analysis of the ex-core neutron sensors ' AC-signals reveals the presence of a peak at 9.2 Hz due to global noise caused by the reactivity fluctuations, followed peaks characteristic of the Core Barrel Motion (CBM) in the range of 10.5 to 19.0 Hz, and finally a peak attributed to the action of the primary pumps at 25.0 Hz. Also, CBM analysis is carried out using the ex-core neutron sensors ' information which has already been validated. Furthermore MISO and Mulli Input Mulli Output (MIM0) ANNs are used for the component based validation study in the plant-wide monitoring. As a new tool, wavelet analysis is proposed for on-line anomaly detection problem by DC-signals and it is found out to be superior tool to Kalman Filtering approach.

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1-Barutçu, B., Şeker, S., Ayaz, E. ve Türkcan, E., (2003a). Real Time Reactor Noise Diagnostics for the Borssele (PWR) Nuclear Power Plant, Progress in Nuclear Energy, 43, 1-4, 137-143.
2-Barutçu, B., Türkcan, E., Şeker, S. ve Ayaz, E., (2003b). On-Line Identification of the Nuclear Power Plant Physical Parameters at Power Operation, Third International Conference on Electrical and Electronics Engineering ELECO 2003, Bursa, Turkey, 3-7 December 2003, 2, 158-162.
3-Beale, R. ve Jackson, T., (1990). Neural Computing: An Introduction, Adam Hilger Publishing. Ltd., GB.
4-Haykin, S., (1999). Neural Networks: A Comprehensive Foundation, Prentice Hall Inc., GB.
5-Mallat, S., (1989) A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, 7, 674-693.
6-Mitra, S.K. ve Kaiser, J.F., eds. (1993). Handbook for Digital Signal Processing, John Wiley & Sons, New York.
7-Morgenstern, V.M., Upadhyaya, B.R. ve Glockler, O., (1988a). Signal Anomaly Detection and Characterization, Topical Report, DOE/NE/37959-18.
9-Morgenstern, V.M., Upadhyaya, B.R. ve Benedetti, M., (1988b). Signal Anomaly Detection Using Modified CUSUM Method, IEEE Proceedings 27th Conference on Decision and Control, 2340-2341. Austin, Texas, USA.
10-Peeters, T.T.J.M., Çiftçioğlu, Ö. ve Türkcan, E., (1991). An Innovations-Based Method for DC Signal Failure Detection Application to NPP, Energieonderzoek Centrum Nederland, ECN-RX--91-043, Netherlands Energy Research Foundation ECN, Petten, The Netherlands.
11-Thie, J.A., (1981). Power Reactor Noise, American Nuclear Society, Illinois, USA.
12-Türkcan, E. ve Versteegh, J.G.E., (1998). Neural Network Training by Extended Kalman Filtering for Detection of Signal/System Failures, Maintenance and Reliability Conference MARCON 98, Knoxville, Tennessee, USA, May 1998, 66.01-66.13.