<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>
According to the signal characteristics of power quality disturbances, a detection and classification method based on S-transform is proposed. The S-transform module matrix is used to detect and classify power quality disturbance signal. Eight disturbance signals (voltage sag, voltage swell, momentary interruption, voltage spike, voltage notch, harmonic, inter-harmonic and oscillatory transients) which influence power quality have been simulated. The results show that the method can be used to localize the disturbance time and duration precisely and classify them simply.
The Application of S-Transform to Reduce Border Distortion Effect Based on Window Length
