Title :
A smart software sensor for feedwater flow measurement monitoring
Author :
Na, Man Gyun ; Lee, Yoon Joon ; Hwang, In Joon
Author_Institution :
Dept. of Nucl. Eng., Chosun Univ., Gwangju
Abstract :
Venturi flow meters are currently used to measure the feedwater flowrate in most pressurized water reactors. The feedwater flowrate can be overmeasured because of the fouling phenomena that make corrosion products accumulate in the Venturi meters. Therefore, in this work, a smart software sensor using a fuzzy model is developed in order to accurately estimate online the feedwater flowrate, and also to monitor the status of the existing hardware sensors. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The proposed smart software sensor 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 smart software sensor early detects the degradation of an existing hardware sensor, it can be applied successfully to validate and monitor the existing hardware feedwater flow meters
Keywords :
fission reactor cooling; fission reactor instrumentation; flow measurement; flowmeters; fuzzy logic; genetic algorithms; intelligent sensors; least squares approximations; nuclear engineering computing; nuclear power stations; PWR; Venturi meters; Yonggwang Nuclear Power Plant; corrosion products; fast algorithm; feedwater flow measurement monitoring; fouling phenomena; fuzzy model; genetic algorithm; hardware sensors; least squares method; optimization; pressurized water reactors; relative maximum error; robust algorithm; root mean squared error; smart software sensor; subtractive clustering method; venturi flow meters; Clustering methods; Corrosion; Current measurement; Fluid flow measurement; Hardware; Inductors; Intelligent sensors; Monitoring; Sensor phenomena and characterization; Software measurement; Feedwater measurement; fuzzy model; measurement monitoring; smart software sensor; subtractive clustering;
Journal_Title :
Nuclear Science, IEEE Transactions on
DOI :
10.1109/TNS.2005.861418
