Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors

The feedwater flowrate that is measured by Venturi flow meters in most pressurized water reactors can be overmeasured because of the fouling phenomena that make corrosion products accumulate in the Venturi meters. Therefore, in this paper, support vector machines combined with a sequential probability ratio test are used in order to accurately estimate online the feedwater flowrate, and also to monitor the status of the existing hardware sensors. Also, the data for training the support vector machines are selected by using a subtractive clustering scheme to select informative data from among all acquired data. The proposed inferential sensing and monitoring algorithm is verified by using the acquired real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations, since the root mean squared error and the relative maximum error are so small and the proposed method early detects the degradation of an existing hardware sensor, it can be applied successfully to validate and monitor the existing hardware feedwater flow meters