Abstract
High voltage direct current (HVDC) interference is the stray current discharged from HVDC transmission line to the ground during operation, which will cause great damage to the pipeline. Pipeline corrosion protection personnel use pipeline potential as monitoring data to monitor interference from HVDC in real time and determine the health of the pipeline. At present, the corrosion protection industry relies on human experience to judge HVDC interference, but no machine learning methods have been used to detect HVDC interference. In this study, one-dimensional convolutional neural network (1-D CNN) was used to analyze the time-series data of pipeline potential, and a classifier of HVDC interference was constructed to realize the automatic detection of HVDC interference. The experimental results show that the classification accuracy of 1-D CNN in the pipeline spontaneous potential time series data reaches 91.6%, which is better than the general feature extraction method, and can effectively detect HVCD interference.
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Ma, Z., Li, J., Li, J., Xu, X., Wang, Y. (2022). Detection of HVDC Interference on Pipeline Based on Convolution Neural Network. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2021. Lecture Notes in Electrical Engineering, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-16-8052-6_7
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DOI: https://doi.org/10.1007/978-981-16-8052-6_7
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