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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<p>The enhancement of powerful signal processing  tools has broadened the scope research in power quality analysis.The necessity of processing tools to compute the signals accurately without border distortion effect presence has demanded nowadays. Hence, S-Transform has been selected in this paper as a time-frequency analysis tools for power disturbance detection and localization as it capable to extract features and high resolution to deal with border distortion effect. Various window length signal has been analyzed to overcome the border distortion effect in S-Transform.To ascertain validity of the proposed scheme, it is  validated with IEEE 3 bus test system and simulation results show that the proposed technique can minimize the border effect  while detecting transient and voltage sag during fault system. As a result, the longest window length which is four cycle, outperform the least MSE value which indicate the best performance. While, the shortest window length resulting highest MSE value which indicate the worst performance.<em></em></p>

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