The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adop...The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosisinto three levels, which are data fusion level, feature level and decision level. The feature leveluses three parallel neural networks whose structures are the same. The purpose of using neuralnetworks is mainly to get basic probability assignment ( BPA) of D-S evidence theory, and the neuralnetworks in feature level are used for local diagnosis. D-S evidence theory is adopted to integratethe local diagnosis results in decision level. The reactor coolant system is the study object andwe choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example.The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirementof complicated system, and verify that the fusion fault diagnosis system can realize the faultdiagnosis of NPP on line timely.展开更多
A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the se...A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the security of reactor, therefor, the study of pressurizer’s pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a presurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.展开更多
文摘The work condition of nuclear power plant (NPP) is very bad, which makes ithas faults easily. In order to diagnose (he faults real time, the fusion diagnosis system is built.The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosisinto three levels, which are data fusion level, feature level and decision level. The feature leveluses three parallel neural networks whose structures are the same. The purpose of using neuralnetworks is mainly to get basic probability assignment ( BPA) of D-S evidence theory, and the neuralnetworks in feature level are used for local diagnosis. D-S evidence theory is adopted to integratethe local diagnosis results in decision level. The reactor coolant system is the study object andwe choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example.The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirementof complicated system, and verify that the fusion fault diagnosis system can realize the faultdiagnosis of NPP on line timely.
文摘A pressurizer is one of important equipment in a pressurized water reactor plant. It is used to maintain the pressure of primary coolant within allowed range because the sharp change of coolant pressure affects the security of reactor, therefor, the study of pressurizer’s pressure control methods is very important. In this paper, an adaptive fuzzy controller is presented for pressure control of a presurizer in a nuclear power plant. The controller can on-line tune fuzzy control rules and parameters by self-learning in the actual control process, which possesses the way of thinking like human to make a decision. The simulation results for a pressurized water reactor plant show that the adaptive fuzzy controller has optimum and intelligent characteristics, which prove the controller is effective.
