Abstract
The characteristics of power quality signals are non-stationary, where the behaviour confirms the negative consequence in sensitive equipment. Modern cross-term time-frequency distributions (TFDs) are able to characterize the power quality accurately but suffer from a delay in measurement since the power quality signals, in this case, sag, swell and transient, need to be analyzed in real-time. It is shown that one window shift (OWS) properties of linear time-frequency representation (TFR) results from short-time Fourier transform (STFT) satisfies accuracy, complexity and memory. By optimally selecting the window length of 512, the TFR is able to provide optimal time, and frequency localization, as well as the spectral leakage, can be reduced by the Hanning window. The proposed technique can accurately characterize the power quality signals averagely by 95%, as well as the complexity and memory usage is low. Finally, the paper is concluded by the recommendation of pre-setting for optimum window type and length for real-time power quality measurement.
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Acknowledgements
The research study was carried out successfully with contributions from all authors equally. The main research idea, simulation works, and manuscript preparation was contributed by Muhammad Sufyan Safwan Mohamad Basir. The challenge of power quality from an economic point of view and statistical approaches for STFT error calculation was prepared by Nur-Adibah Raihan Affendy. Mohamad Azizan Mohamad Said and Khairul Huda Yusof contributed to the modelling and simulating using MATLAB. Rizalafande Che Ismail for his support with study administration. The authors’ responsibilities were as follows: design the research and write the manuscript; analyze data and validate; edited and conducted the research manuscript with final content. All authors have read and agreed to the published version of the manuscript. The authors would like to thank Politeknik Mukah Sarawak, Universiti Malaysia Perlis and Management and Science University for providing the support for this research.
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Basir, M.S.S.M., Affendy, NA.R., Said, M.A.M., Ismail, R.C., Yusof, K.H. (2023). Short-Time Fourier Transform with Optimum Window Type and Length: An Application for Sag, Swell and Transient. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_11
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