Transient Diagnosis and Prognosis for Secondary System in Nuclear Power Plants

• Main Authors: Sangjun Park, Jinkyun Park, Gyunyoung Heo
• Format: Article
• Language: English
• Published: Elsevier 2016-10-01
• Series: Nuclear Engineering and Technology
• Subjects: Diagnosis; Nuclear Power Plant; Pattern Matching; Principal Component Analysis; Prognosis; Transient
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573316300419

This paper introduces the development of a transient monitoring system to detect the early stage of a transient, to identify the type of the transient scenario, and to inform an operator with the remaining time to turbine trip when there is no operator's relevant control. This study focused on the transients originating from a secondary system in nuclear power plants (NPPs), because the secondary system was recognized to be a more dominant factor to make unplanned turbine-generator trips which can ultimately result in reactor trips. In order to make the proposed methodology practical forward, all the transient scenarios registered in a simulator of a 1,000 MWe pressurized water reactor were archived in the transient pattern database. The transient patterns show plant behavior until turbine-generator trip when there is no operator's intervention. Meanwhile, the operating data periodically captured from a plant computer is compared with an individual transient pattern in the database and a highly matched section among the transient patterns enables isolation of the type of transient and prediction of the expected remaining time to trip. The transient pattern database consists of hundreds of variables, so it is difficult to speedily compare patterns and to draw a conclusion in a timely manner. The transient pattern database and the operating data are, therefore, converted into a smaller dimension using the principal component analysis (PCA). This paper describes the process of constructing the transient pattern database, dealing with principal components, and optimizing similarity measures.