Research Of Fault Detection And Diagnosis To Power Electronics Based On Neural Network

With the development of power electronics' complexity, function and automation, it's more vital for fault detection and diagnosis in time and for steady power electronics operation. This paper studys the importance of fault diagnosis, why fault detection and diagnosis is carryied on,and the diffculty which consists in the traditional fault detection and diagnosis and so on in the prologue. Basic knowledge of neural network, especially the BP neural network, is the emphasis of the paper, and the ways of fault diagnosis also are discussed. At last, the author proposes the methods of neural network(NN) hardware implementation by using DSP(Digital Signal Processing).The fault diagnosis based on neural network consists of two parts, which one is the collection of sample data of fault and the other is fault diagnosis. Before the collection of sample data of fault and checking data, the paper establishs the Matlab model of fault diagnosis firstly. All kinds of fault are made artificially and collected by the Matlab model. After normalization, the sample data of fault is servered as the data of training neural network. At the same time, the checking data is used to examine whether the trained neural network have the function of fault detection and diagnosis. There are shortcoming of the slow constringency rate and disadvantage that error easily gets into partial minimal value in the process of diagnosis by BP algorithm in BP network. So it adopts the following solutions. Firstly, the learning method of "batch dealing" is used. During the training neural network, this method accelerates the speed of constringency while neglects the affection of studying sample orders. Secondly, the improving BP arithmetic is adopted, which is so called conjugated gradient method, which the inertia is iterative in the training process. The aim to accelect the speed of convergence and improve realtime diagnosis accuracy is achieved.For the research object, the data of the model's electric currents and voltages are collected to train neural network and diagnosed. The result proves that the fault detection and diagnosis based on neural network can be used in reality.