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Expert System for Fault Diagnosis of Molten Salt Receiver in the Solar Tower Power Plant

Author: WangJianZuo
Tutor: LiZuo
School: Institute of Electrical Engineering
Course: Theory and New
Keywords: Solar tower power plant Molten salt heat sink Overheat Expert system Neural Networks
CLC: TM615
Type: Master's thesis
Year: 2010
Downloads: 240
Quote: 1
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Abstract


The tower is an important form of concentrating solar thermal power generation , compared to photovoltaic and thermal power generation in the form , it has a large-scale effect , broad commercial prospects . Use molten salt as a heat transfer working fluid , the atmospheric heat high temperature transmission can be realized in the power station , and do not have to worry about the pressure . Molten salt working fluid on the one hand to improve the overall operation of the power station temperature resulting in higher efficiency , on the other hand to achieve the integration of the working fluid of the heat transfer and thermal storage to reduce heat transfer equipment and heat transfer losses , but also can improve the system efficiency. At the same time , the higher the temperature of the heat sink security accordingly put forward higher requirements. Overheating is endothermic most prone to failure , damage to the heat-absorbing structure with molten salt working fluid will affect the long-term operation of the power station , there are a lot of security and economic hazards . This article has designed a set of expert system for the diagnosis of the failure of the heat sink , and numerical simulation method for tower molten salt heat sink overheating fault , easy to extract the parameters of the the endothermic tube and heat transfer working fluid 11 temperature sensitivity analysis to identify the impact of overheating occurs four basic variables that irradiation could flow maxima qmax tube wall thickness δ , molten salt working fluid velocity u and the average fluid temperature tf , and summary of the analysis of the impact of the different variables change temperature trend . With this conclusion, the actual endothermic desirable within the parameters to the four variables selected many different values ??as the initial conditions , the large number of numerical simulation , the temperature response . Then results in variable temperature information is extracted using artificial neural networks to identify the functional relationship between these four variables , with molten salt working fluid , and endothermic tube local maximum temperature . Matrix function as prepared by the expert knowledge into the expert system , the four runtime will be collected in real time value of the variable input expert system , resulting in the forecast the temperature with the maximum allowable temperature in order to determine whether overheating failures .

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CLC: > Industrial Technology > Electrotechnical > Power generation, power plants > Variety of power generation > Solar power
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