Fault Diagnosis Of Thermal Boiler Based On Neural Network

With the rapid development of power industry,the application of large-scale power station's boilers is becoming more and more extensive and the structure of boiler system is more complicated.Besides,the operating parameters are becoming more.Therefore,there is an urgent need for the technology on fault diagnosis.In numerous fault diagnosis technologies,the fault diagnosis technology based on neural network is widely used in the fault diagnosis of power plant system because of its advantages such as strong learning ability,good fault tolerance,fast and convenient and able to deal with complex nonlinear relationship.In the neural network fault diagnosis method,the fault feature recognition and classification is one of the key steps to affect the safe,reliable and efficient function of the fault diagnosis system.So it is very important to study feature recognition and classification accuracy of the fault.When the leakage of the superheater in the power plant occurs,the fault characteristic rule is difficult to be summarized and the characteristics of knowledge extraction are difficult.Besides,the characteristic parameter is changed quickly.Based on this situation,in order to overcome the shortcomings of traditional single boiler fault diagnosis method and manual fault diagnosis,wavelet neural network fault diagnosis model is designed.In order to solve the problem of the training parameters of the network model,the particle swarm optimization algorithm is proposed to optimize the parameters.The comparative study of MATLAB simulation shows that the fault diagnosis model trained by this algorithm is superior to other algorithms in terms of accuracy and training time.In addition,in view of various failure modes coexist case and the fault features such as high-dimensional features,Probabilistic Neural Network Fault Diagnosis Model is designed,and the particle swarm optimization algorithm is used to improve the network to form an adaptive probability neural network fault diagnosis model.MATLAB simulation verifies the effectiveness of the improved algorithm.In the end,the configuration monitoring system of thermal power plant fault diagnosis system is completed by using Kingview software,and the data communication between MATLAB and Kingview is established by using OPC technology.The design of fault feature recognition algorithm in MATLAB environment is realized.