Shigeru Kanemoto

Development of an on-line reactor stability monitoring system in a boiling water reactor

Abstract Reactor stability is an important topic in BWR plant design and operation. The objective of the present study is to establish a method for estimating the reactor stability in an operating BWR by measuring process noise signals, such as neutron flux or core flow rate. In the present study, several methods, based on autoregressive modeling, are presented and their reliability is quantitatively evaluated, using both simulated and real plant noise data. On the basis of these examinations, an on-line stability monitoring system was developed. Through field tests on the system over a year in an operating BWR, the validity of the system has been confirmed. Also, valuable data about reactor stability has been obtained.

Noise source and reactor stability estimation in a boiling water reactor using a multivariate autoregressive model

A method for evaluating reactor stability in boiling water reactors has been developed. The method is based on multivariate autoregressive (M-AR) modeling of steady-state neutron and process noise signals. In this method, two kinds of power spectral densities (PSDs) for the measured neutron signal and the corresponding noise source signal are separately identified by the M-AR modeling. The closed- and open-loop stability parameters are evaluated from these PSDs. The method is applied to actual plant noise data that were measured together with artificial perturbation test data. Stability parameters identified from noise data are compared to those from perturbation test data, and it is shown that both results are in good agreement. In addition to these stability estimations, driving noise sources for the neutron signal are evaluated by the M-AR modeling. Contributions from void, core flow, and pressure noise sources are quantitatively evaluated, and the void noise source is shown to be the most dominant.

Application of autoregressive modeling techniques in BWR noise analysis

Abstract Experience on development and application of an autoregressive (AR) modeling technique in BWR noise analysis is described. Based on the improvement of the AR model using an associate matrix, which specifies signal transfer paths in measured signals, BWR noise in reactor core and control systems is analyzed. In the reactor core, void and recirculation flow noise sources, which are most dominant in BWR plants, are quantitatively evaluated by the improved AR model. Also, ordinary and partial coherence functions between radially located neutron detector signals are estimated to clarify spatially distributed noise sources. In the control system, the open-loop and closed-loop responses are identified from steady-state noise data. Furthermore, a failure detection and isolation method for the control system is developed on the basis of the improved AR model.

Operating experience of a BWR plant diagnosis system

Abstract The development of a diagnosis system is one of the important aspects in application of noise analysis technique. A diagnosis system for a BWR plant has been developed in Japan, and is now in the feasibility study. The diagnosis objects of the system are reactor core, main control systems (recirculation flow control system, feedwater control system and pressure control system) and jet pumps. The system includes an on-line algorithm based on statistical dicision theory for surveying noise data and diagnosing plant operating status. The system was installed on a BWR plant and the test operation has been performed. It is confirmed that this system performance is satisfactory, and that this system will be of great use for surveillance of core and control system without artificial disturbances.

Method of monitoring reactor system by state space trajectory pattern

Abstract A method of monitoring system states by a state space trajectory pattern is proposed. This method is applied to a one-dimensional system, a two-dimensional system and reactor systems with temperature or void feedback using computer simulation. It is shown that the resultant deterministic and stochastic trajectory patterns correspond closely to each other. This method permits direct treatment of raw data and also nonlinear system and detection of abnormal condition indicated in either two- or three-dimensional space.

Development of Advanced Core Noise Monitoring System for a Boiling Water Reactor

For an efficient nuclear power plant operation, advanced core design is very important. However, advances in design may result in approaching unexplored areas where unforeseen events are increasingly common. The purpose of this paper is to describe a BWR core noise monitoring system for addressing core anomaly problems in future advanced core operation. To monitor in-core status from a limited number of signals, we introduce various new algorithms to compensate for a lack of information. We demonstrate that up-to-date signal processing technologies, such as independent component analysis, nonlinear principal component regression, factor analysis and neural network, are effective tools for core monitoring. Also, the combination of the first principle or design model and the empirical fitting model is applied to estimate unobserved in-core state variables. Several concrete examples, such as stability monitoring, reactivity coefficient estimation, core flow estimation and in-core signal validation, are shown based on long-term monitoring experiences in real BWR plants. Through these examples, we demonstrate that the core noise monitoring system is a general platform for providing a variety of monitoring tools to meet the requirements of an advanced core operation strategy.