(A) study on the applications of expert systems and neural netwooks for the development of operator support systems in nuclear power plants = 원자력발전소 운전원 지원시스템 구축을 위한 전문가시스템과 신경회로망 적용에 관한 연구

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In order to assist operators in effectively maintaining plant safety and to enhance plant availability, the need to develop operator support systems is growing to increase. The application of both expert system and neural network technologies to the operator support has the potential to increase the performance of these systems. A prototype integrated operator support system, called NSSS-DS, has been developed for nultiple alarm processing, plant trip diagnosis, and the failure diagnosis of three main systems (a rod control system, reactor coolant pumps (RCPs) and a pressurizer) in the primary side of the Kori-2 nuclear power plant. This system diagnoses system malfunction quickly and offers appropriate guidance to operators. The system uses rule-based deduction with certainty factor operation. Diagnosis is performed using an establish-refine inference strantegy. This strategy is to match a set of symptoms with a specific malfunction hypothesis in a predetermined structure of possible hypotheses. The diagnostic symptoms include alarms, indication lamps, parameter values and valve lineup that can be acquired at a main control room. The overall plant-wide diagnosis is performed at the main control part which can process multiple alarms and diagnose possible failure modes and failed systems in the plant. The method of alarm processing is the object-oriented approach in which each alarm can be represented as an active data element, an object. The alarm processing is performed using alarm processing meta rules and alarm processing frames. Also, the diagnosis of a plant trip can be performed at the main control part. The specific diagnosis of the three main systems can be performed followed by the diagnostic results of the main control part. The system also provides follow-up treatments to the operators. The application to these systems is described from the point of view of diagnostic strategies. For the applications of the neural network technology, two feasibility...
Chang, Soon-Heungresearcher장순흥researcher
한국과학기술원 : 원자력공학과,
Issue Date
60648/325007 / 000875430

학위논문(박사) - 한국과학기술원 : 원자력공학과, 1993.2, [ xxi, 248 p. ]

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