The Fault Diagnosis Research On Condenser System Of Power Plant

In power plant, the condenser system plays a very important role on condensation as one of the assistant equipments of power plant system. Therefore, the establishment of condenser fault diagnosis system for power plant has economical and secure practical sense.A fault diagnosis system based on principal component analysis (PCA) with the combination of BP neural network was designed for the condenser. The system could realize the functions such as data collection, data transmission, fault diagnosis, fault analysis.In this paper, the structures, common faults and their symptoms of the condenser system were expatiated in detail, and then a condenser fault diagnosis system was designed. In this system, the main performance parameters of air feeder were collected through EDPF-NT plus system. The data communication had been realized between MATLAB and configuration software of system(DCS) by using OPC technology as a link. So it made on-site data be read to fault diagnosis system in MATLAB by OPC technology. After the principal component analysis(PCA) data pre-processing and feature extraction for the collected raw data, the state of the condenser was diagnosed through BP neural network fault diagnosis model. Finally, the results by analyzing and decision-making were returned to the diagnosis interface to instruct the repairment. The application of advanced fault diagnosis techniques could makes the system has high reliability.The condenser CBM system provides a guarantee for condenser's good operation as well as the security, the regular operation and the economic operation of power plants. It doesn't only reduce the maintenance charge and prolong the maintenance interval, but also extends the life span of condenser which can increase the economic benefits for the power plant.