A Study On Fault Monitoring And Diagnosis System For Condenser Of Coal-fired Power Plant

Condensing system is an important part of a coal-fried power plant, and is of great importance for the whole plant's safe and economic operating. In this paper, with a great deal of foreign and domestic documents as it basis, conception and calculation formula for coefficient of relative cleanness are put forward, which can guide condenser's operation to some degree; condensing system's performance, especially its transient response under some transient conditions, is discussed; condensing system's common faults are analyzed and the cause and signs of condenser's vacuum falling are emphasized. The judgement of condenser's fault is not easy for it has many kinds of reasons and its signs are fuzzy to some extent. In this paper, fuzzy neural networks, which is fairly advanced, is applied in condenser's fault diagnosis. After introducing fuzzy logic theory and neural networks basis, their complex --fuzzy neural networks is dra~ forth. Fuzzy neural networks, which not only can indicate qualitative knowledge, but also has powerful self-study ability and data process ability, is an advanced fault diagnosis method. Subordinate functions are necessary for condenser's fault diagnosis by fuzzy neural networks. In this papei; according to operating code and experience of a power plant, seventeen subordinate functions, which are enough for condenser's fault diagnosis, are constructed. By these functions, each input can be fuzzified to quantitative input. Fuzzy BP networks? knowledge base structure is attained by training. Having these as its basis, a condenser's practical fault is diagnosed, and the result is satisfied. Finally, a project for on-line monitoring and diagnosis of condenser is put forward.