Application of artificial neural networks to nuclear power plant transient diagnosis

详细信息    查看全文

• 作者：T.V. Santosh ; Gopika Vinod ; R.K. Saraf ; A.K. Ghosh ; H.S. Kushwaha
• 关键词：Artificial neural networks ; Resilient-back propagation ; Activation function ; Mean square error ; Operator support system ; Initiating event
• 刊名：Reliability Engineering and System Safety
• 出版年：2007
• 出版时间：October 2007
• 年：2007
• 卷：92
• 期：10
• 页码：1468-1472
• 全文大小：188 K

文摘

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.