Wavelet transform is an essential method for preprocessing and analyzing non

This paper presents a statistical algorithm for classification of fault causes on power transmission lines. The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. Fault duration of RMS current signal, voltage dip, and DWT gives concealed data of a fault signature as a contribution to decision tree calculation which is utilized to classify various fault causes. The proposed method was carried out in the MATLAB/SIMULINK programming platform based upon the information made with the fault analysis of the 275 kV sample transmission line considering wide variations in the operating conditions. The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree.

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Neutral to Earth Voltage (NTEV) is one of power quality (PQ) problems in the commercial building that need to be resolved.  The classification of the NTEV problems is a method to identify the source types of disturbance in alleviating the problems.  This paper presents the classification of NTEV source in the commercial building which is known as the harmonic, loose termination, and lightning.  The Euclidean, City block, and Chebyshev variables for K-Nearest Neighbor (K-NN) classifying are being utilized in order to identify the best performance for classifying the NTEV problems.  Then, S-Transform (ST) is applied as a pre-processing signal to extract the desired features of NTEV problem for classifier input.  Furthermore, the performance of K-NN variables is validated by using the confusion matrix and linear regression.  The classification results show that all the K-NN variables capable to identify the NTEV problems. While the K-NN results show that the Euclidean and City block variables are well performed rather than the Chebyshev variable.  However, the Chebyshev variable is still reliable as the confusion matrix shows minor misclassification. Then, the linear regression outperformed the percentage close to a perfect value which is hundred percent.

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