An adaptive high-voltage direct current detection algorithm using cognitive wavelet transform |
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| Institution: | 1. School of Information and Communication Engineering, Hunan Institute of Science and Technology, Hunan, China;2. Machine Vision & Artificial Intelligence Research Center, Hunan Institute of Science and Technology, Hunan, China;1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic;1. School of Economics and Management, Chang''an University, Xi''an 710064, China;2. Computer & Information Sciences Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak, Malaysia;3. Institute of IR4.0, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia;4. College of Engineering, Al Ain University, Al Ain, United Arab Emirates;5. Department of Mathematics, College of Science, Tafila Technical University, Tafila, Jordan;1. Ryerson University;2. Arizona State University;3. Illinois Institute of Technology;4. University of Guelph;1. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212100, China;2. School of Economics and Management, Shangqiu Normal University, Shangqiu, 476000, China;3. School of Management, Guangzhou Xinhua University, Dongguan, 523133, China;4. School of Business, Sun Yat-Sen University, Guangzhou, 310003, China |
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| Abstract: | This paper proposes an algorithm that uses wavelet level adaptive decision-making for detecting high-voltage direct current (HVDC) discharge in wavelet transform cognitively. The identification and detection of HVDC discharge is an essential area of investigation, which contributes to ensuring pipeline safety and the optimal operation of an electrical power system. The proposed algorithm overcomes the wavelet packet transform’s disadvantage of needing to determine the level in advance. The decomposition level of wavelet packet transform is controlled by calculating relative wavelet energy change to decide its wavelet level. Our proposal extracts richer features of HVDC discharge by comparing other feature extraction algorithms. To select the best-suited mother wavelet function, we also design a selection method based on quantitative and qualitative approaches. An additional objective of this study is to detect the phenomenon of HVDC discharge using CP time-series data to assess the corrosion of energy pipelines. Moreover, a third primary discovery is that a wavelet-based application framework is designed to detect the HVDC discharge and further protect the energy pipeline. These discoveries can be valuably applied to the protection of power systems. They also provide brighter perspectives on future opportunities to expand on studies-to-date on the detection and classification of time-series data. |
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| Keywords: | High-voltage direct current Wavelet transform Wavelet energy Detection and classification Signal processing |
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