The electric field distortion due to space charge accumulations plays a significant role in the ageing, degradation and breakdown in failure of HVDC power cables. Currently, limited experimental results of the electric field dominated by space charges are insufficient to diagnose the power cables. This paper proposes an improved long short-term memory network (LSTM) model for predicting the fluctuating maximum electric field (E_max) in cross-linked polyethylene (XLPE) cable insulation. The various E_max data derived from the complex space charge behaviours were measured using the pulsed electroacoustic method. The model uses regularisation and dropout feedback in the LSTM unit, reducing the phenomenon of over-fitting due to the limited data. It enhances the prediction accuracy and ability of long time prediction by improving the prediction of E_max with the non-linear fluctuation. The predicted E_max approaches 190 kV/mm under 150 kV/mm and 60°C after 2 h. The predicted large variation in E_max under 120 kV/mm and 20°C after 4 h ranges from 130 to 160 kV/mm. It indicates high electric stress in the cable insulation during continuous operation. The proposed LSTM model is of great importance to guide the diagnosis of cable degradation in HVDC power cables.

中文翻译：

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基于长短期记忆网络算法的交联聚乙烯绝缘最大电场波动增加的智能模型预测

空间电荷积累引起的电场畸变在高压直流输电电缆的老化、退化和故障击穿中起着重要作用。目前，以空间电荷为主的电场的有限实验结果不足以诊断电力电缆。本文提出了一种改进的长短期记忆网络 (LSTM) 模型，用于预测交联聚乙烯 (XLPE) 电缆绝缘中波动的最大电场 ( E _{max )。}各种E _max来自复杂空间电荷行为的数据是使用脉冲电声方法测量的。该模型在 LSTM 单元中使用了正则化和 dropout 反馈，减少了由于数据有限而导致的过拟合现象。它通过改进非线性波动的E _max预测来提高预测精度和长时间预测能力。在 150 kV/mm 和 60°C 下 2 小时后，预测的E _{max接近 190 kV/mm。}E _max的预测大变化在 120 kV/mm 和 20°C 下 4 小时后，电压范围为 130 至 160 kV/mm。它表明在连续运行期间电缆绝缘层中存在高电应力。所提出的 LSTM 模型对于指导 HVDC 电力电缆中电缆退化的诊断具有重要意义。