Axial offset control of PWR nuclear reactor core using intelligent techniques

• Published in: Nuclear engineering and design Vol. 227; no. 3; pp. 285 - 300
• Main Authors: Boroushaki, Mehrdad, Ghofrani, Mohammad B., Lucas, Caro, Yazdanpanah, Mohammad J., Sadati, Nasser
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2004; Elsevier
• Subjects: Applied sciences; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Fission nuclear power plants; Installations for energy generation and conversion: thermal and electrical energy; NN; CGD; DYNCO; PT; ANNs; CAOC; PIE; AO; MLP; P; PIL; COSS; RNN; SF; APWR; APE; CODA; BOC; PIR; CRG; RMLP; NARX; OL; Control rod; Decision making; Neural network; Performance; Reactor core; Transients; Pressurized water reactor; Nuclear reactor
• ISSN: 0029-5493
• DOI: 10.1016/j.nucengdes.2003.11.002

Improved load following capability is one of the main technical performances of advanced PWR (APWR). Controlling the nuclear reactor core during load following operation encounters some difficulties. These difficulties mainly arise from nuclear reactor core limitations in local power peaking, while the core is subject to large and sharp variation of local power density during transients. Axial offset (AO) is the parameter usually used to represent of core power peaking, in form of a practical parameter. This paper, proposes a new intelligent approach to AO control of PWR nuclear reactors core during load following operation. This method uses a neural network model of the core to predict the dynamic behavior of the core and a fuzzy critic based on the operator knowledge and experience for the purpose of decision-making during load following operations. Simulation results show that this method can use optimum control rod groups maneuver with variable overlapping and may improve the reactor load following capability.