基于LSTM的海底电力电缆磁场强度预测模型研究

Jianping Wu, Xi Yang, Huan Wang, Jianping Chen, Quanzhong Zhao, B. Xiao
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引用次数: 1

摘要

海底电力电缆敷设后,电缆与海面的距离(H)、两条平行敷设的海底电力电缆之间的距离(d)等参数是恒定的。由于海底电缆不能随机放置,因此研究不同H和d下海底电力电缆的磁场强度分布比较困难。为了解决这一问题,首先研究了不同H组和d组对海底电缆磁场强度分布的影响,并将不同H组和d组产生的海底电缆磁场强度作为训练样本和测试样本。然后利用训练样本构建基于长短期记忆(LSTM)神经网络的海底电力电缆磁场预测模型,以测试样本作为输入对海底电力电缆磁场进行预测。最后,仿真结果表明LSTM预测模型对海底电缆磁场数据具有较好的预测能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Prediction Model of Magnetic Field Intensity of Submarine Power Cable Based on LSTM
After the submarine power cables are laid, the parameters such as distance between cables and surface of the sea (H), distance between two submarine power cables placed in parallel (d) are constant. It is difficult to study the magnetic field intensity distribution of submarine power cable under different H and d, because the submarine cable can't be placed randomly. In order to solve this problem, firstly the influence of different groups of H and d on the distribution of submarine cable magnetic field intensity is studied, and the magnetic field intensity of submarine cable generated by different groups of H and d are used as training samples and testing samples. Then the training samples are used to construct the submarine power cable magnetic field prediction model based on Long Short Term Memory (LSTM) neural network, and the submarine power cable magnetic field is predicted with the testing samples as the input. Finally, the simulation results show that there is good prediction ability of LSTM prediction model for submarine cable magnetic field data.
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