Conception of Voltage Interruption Signal using Continuous Wavelet, Discrete Wavelet, and Wavelet Packet Analysis

• Published in: E3S web of conferences 2024, Vol.564, p.7001
• Main Authors: Priyadarshini, M. S., Bajaj, Mohit, Avikal, Shwetank, Vishnuram, Pradeep
• Format: Article
• Language: English
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202456407001

This paper focuses on conception of voltage interruption signal in different domains. According to IEEE Standard 1159, Interruption is categorised under short-duration voltage variations in the description of classification of ‘Power quality Disturbances’. Power quality disturbances affect the quality of electric power supplied by utilities to power consumers. An interruption represents loss of voltage for a duration of time from 0.5 cycles to a duration less than 1 minute. This variation in supply voltage will affect the connected load. This acts as a significant challenge in terms of stability and reliability of power supply. Signal processing techniques of Fourier transform (FT), Short-Time Fourier transform (STFT), continuous wavelet transform (CWT), discrete wavelet transform (DWT) and Wavelet Packet Transform (WPT) are applied to the voltage signals in MATLAB. This approach results in analysis and obtaining characteristics of this disturbance of interruption. This work focuses on extracting information from voltage signal resulting in detection of power quality issues in different perspectives. Continuous wavelet transform provides a time- frequency representation of signals. Discrete wavelet transform is based on multi-resolution analysis resulting in breaking down a signal into different frequency bands. For finer resolution, wavelet packet transform is used. These different transforms result in better understanding of the disturbance present in the signal. MATLAB platform is used to implement FT, STFT, CWT, DWT, WPT on interruption disturbance. Based on the same approach, conception of different signals time domain signals having practical relevance and physical significance can be analyzed to extract information in different perspectives.