基于深度学习的集肤效应电伴热系统PI参数优化研究

2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) Pub Date : 2019-06-01 DOI:10.1109/PEDG.2019.8807694

Peng Xiao, Lei Fu, Jiusheng Wang, Guangxue Cui, Liguo Wang

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引用次数: 0

摘要

在石油管道输送中，需要电伴热系统来保证石油的正常流动。加热效率与系统的PI参数直接相关。同时，如果在系统设计时不考虑管道中的压力，很容易因压力过大而威胁到系统的正常运行。针对这些问题，本文提出对系统主体PI参数进行优化，在压力较大时对PI参数进行调节以降低压力，并将其应用于集肤效应电伴热系统。

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Research on optimizing PI parameter of skin effect electric trace heating system based on deep learning

In the pipeline transportation of petroleum, electric heat tracing system is needed to ensure the normal flow of petroleum. The heating efficiency is directly related to the PI parameters of the system.At the same time, if the pressure in the pipeline is not taken into consideration when designing the system , it is easy for the excessive pressure to threaten the normal operation. To solve these problem, this paper proposes to optimize the subject PI parameters of the system, modulate the PI parameters to reduce the pressure when the pressure is high, then apply them to the skin effect electric heat tracing system.

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2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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