This study aims at analyzing nuclear power plant performances of the last decade through clustering techniques. Annual trends of performance indicators for light water reactors included in the IAEA PRIS database are used to discover a natural clustering of nuclear plants. The analysis of the obtained clusters allows identifying plants with similar performance trends and recognizing outliers and relationships, helping the identification of determinant factors leading to successful nuclear operation. In detail, a cluster analysis is performed jointly using the following three performance indicators: Unit Capability Factor (UCF), Unplanned Capability Loss Factor (UCLF), and Forced Loss Rate (FLR). Physical and technical parameters (e.g., technology design, supplier, country) and managerial aspects (e.g., kind of operating utility) are included in the analysis to characterize and interpret, through conditional barplots, the identified clusters. Reasons for the division are discussed, considering hypotheses for similarities among the plants included in the clusters

Cluster analysis of nuclear performance trends

GHAZY, RASHA;RICOTTI, MARCO ENRICO;MENAFOGLIO, ALESSANDRA;SECCHI, PIERCESARE;VANTINI, SIMONE
2012

Abstract

This study aims at analyzing nuclear power plant performances of the last decade through clustering techniques. Annual trends of performance indicators for light water reactors included in the IAEA PRIS database are used to discover a natural clustering of nuclear plants. The analysis of the obtained clusters allows identifying plants with similar performance trends and recognizing outliers and relationships, helping the identification of determinant factors leading to successful nuclear operation. In detail, a cluster analysis is performed jointly using the following three performance indicators: Unit Capability Factor (UCF), Unplanned Capability Loss Factor (UCLF), and Forced Loss Rate (FLR). Physical and technical parameters (e.g., technology design, supplier, country) and managerial aspects (e.g., kind of operating utility) are included in the analysis to characterize and interpret, through conditional barplots, the identified clusters. Reasons for the division are discussed, considering hypotheses for similarities among the plants included in the clusters
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978-92-95064-14-0
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/748994
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