Analyzes Of The Condenser Vacuum Degree Prediction Based On A660MW Power Plant In China

The operating condition of the unit condenser has a major impact on unit’s security andeconomy. Vacuum degree is the main indicators characteristic of the condenser oncharacterizing the operating, condenser vacuum will not only cause low cycle thermalefficiency decreases, but also leads to corrosion, steam turbine vibration and relatedequipment and piping failure. Therefore, by the actual operation of the power plant, thestaffs hope to grasp the trend of the condenser vacuum, the earlier they detect thedeterioration of the vacuum, the timelier they can deal with the failure.This paper analyzes the operating characteristics of the condenser, this paper analysisof the factors affecting the condenser vacuum from the theoretical and practical, we foundthat the vacuum degree is related to circulating pump failure, vacuum pump failure,condensate pumps and so on. According to the equation,this paper analyzes one event aboutthe variation of the vacuum degree. Based on the history parameter of the vacuum degree ofthe#2condenser in a660MW power plant in China, this paper uses GM (1,1), BP neuralnetwork prediction model and gray-BP neural network prediction model, three kinds ofmodels to predict vacuum degree and verifies it.Through the establishment of the above prediction models,we find that the predictionmodels reflect trends of the condenser vacuum degree, compared among the threemodels, the accuracy of the combined prediction model is higher than the others. So Gray-neural network model is in favor of a more accurate prediction of variaty in condenservacuum, by the combination of the model used in the actual system, the plant operatingworkers can get better guiding.