Study On State On-Line Monitoring And Fault Diagnosis Of Transformer

With the rapid growth of installed capacity in the world, the requirement for the reliability of power system becomes more and more urgent. Implement of state on-line monitoring and fault diagnosis of the power equipment is the precondition of predicting maintenance, is the key element of reliable run, and is the important supplement and updated development to the traditional off-line preventive maintenance.In this thesis state on-line monitoring and fault diagnosis of transformer are the research objects. Firstly Least Squares Support Vector Machine (LS-SVM) and Empirical Mode Decomposition (EMD) are deeply studied. After that, they are applied into the fault diagnosis system for transformer, furthermore, study results of both Dissolved Gas Analysis (DGA) and on-line monitoring of Partial Discharge (PD) are given.DGA is one of the mainly methods on insulation monitoring of transformer, but the accuracy is low because it is difficult to distinguish between failures when overheating and PD coexist. By analyzing all types of faults, two methods are raised. One is the DGA of transformer based on multi-classification LS-SVM. Based on correlation analysis and pretreatment, some key gases are selected as the inputs of LS-SVM, furthermore, fault diagnosis is accomplished according to the concentration distribution of typical fault gases in higher dimensional space. The other is the DGA of transformer by neural network based on particle swarm optimization (PSO) with neighborhood operator. Based on correlation analysis and pretreatment, the key gases are selected as the inputs of neural network, furthermore, fault diagnosis is accomplished by the neural network based on PSO with neighborhood operator. By discussing the experiment results, the methods of this paper have very good classification results, and figure out the problem that is difficult to distinguish between failures when overheating and PD coexist, meanwhile, the effectiveness and usefulness is proved.In recent years, on-line monitoring of PD has been a focus at home and abroad, and suppression of interference is the difficulty of fault diagnosis. Two methods of suppressing narrow-band interference are proposed in this paper. One is the improved frequency domain analysis based on data extension using least squares support vector regression. Firstly, the actual PD signals with noise are transformed from time domain to frequency domain, then extending the data near the maximum of interference frequency, ultimately the final signals can be obtained by Inverse Fourier Transform (IFT). This method enlarges the range of interference frequency band and removes effectively the interference residue. The other is a new adaptive algorithm based on EMD. First, the mid-signals can be produced by reducing the amplitude of narrow-band interference in frequency region, which is decomposed with EMD, and intrinsic mode functions (IMF) which contain specific frequency can be obtained, then for every IMF adaptive noise canceller is used to suppress narrow-band interference. A new method of denoising based on EMD and reconstruction of IMF is investigated. The PD signals with noise are decomposed with EMD, and IMFs can be obtained, through thresholding and reconstructing every EMF, then the noise can be depressed. Compared with the conventional wavelet-based denoising algorithms, the new algorithm is simpler, more flexible, and not limited by the selection of wavelet function and optimal decomposition level of wavelet, meanwhile, automatic selection of threshold value and thresholding layers of intrinsic mode functions are realized.