Vatsa Aniket, Hati Ananda Shankar, Kumar Prashant, Margala Martin, Chakrabarti Prasun
Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India.
Department of Mechanical, Robotics, and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 gil, Jung-gu, Seoul, 04620, Republic of Korea.
Sci Rep. 2024 Jan 16;14(1):1369. doi: 10.1038/s41598-023-50641-z.
The empirical application of polarization and depolarization current (PDC) measurement of transformers facilitates the extraction of critical insulation-sensitive parameters. This technique, rooted in time-domain dielectric response analysis, forms the bedrock for parameterization and insulation modeling. However, the inherently time-consuming nature of polarization current measurements renders them susceptible to data corruption. This article explores deep-learning-based short-duration techniques for forecasting polarization current to address this limitation. By incorporating spatial shortcuts, the residual long short-term memory (LSTM) network facilitates the seamless propagation of spatial and temporal gradients. Furthermore, the relative forecasting assessment of the proposed residual LSTM model's performance is made against traditional LSTM, attention LSTM, gated recurrent units (GRU), and convolutional neural network (CNN) models. Thus, optimal model selection strategies are evaluated based on their capability to capture extended dependencies and short-term information present in the data. In addition, the Monte Carlo dropout prediction is employed to estimate uncertainty in polarization current forecasts. The findings demonstrate that the proposed residual LSTM network model for polarization current forecasting yields the lowest error metrics and maintains prediction consistency over the testing duration. Thus, the proposed approach significantly reduces PDC measurement time, providing an effective means to develop proactive maintenance strategies for evaluating the insulation condition of transformers.
变压器极化和去极化电流（PDC）测量的实证应用有助于提取关键的绝缘敏感参数。这项技术基于时域介电响应分析，是参数化和绝缘建模的基础。然而，极化电流测量固有的耗时特性使其容易受到数据损坏的影响。本文探讨了基于深度学习的短持续时间技术来预测极化电流，以解决这一局限性。通过纳入空间捷径，残差长短期记忆（LSTM）网络促进了空间和时间梯度的无缝传播。此外，将所提出的残差LSTM模型的性能与传统LSTM、注意力LSTM、门控循环单元（GRU）和卷积神经网络（CNN）模型进行了相对预测评估。因此，基于它们捕捉数据中存在的扩展依赖性和短期信息的能力来评估最优模型选择策略。此外，采用蒙特卡洛随机失活预测来估计极化电流预测中的不确定性。研究结果表明，所提出的用于极化电流预测的残差LSTM网络模型产生的误差指标最低，并在测试期间保持预测一致性。因此，所提出的方法显著减少了PDC测量时间，为制定评估变压器绝缘状况的主动维护策略提供了一种有效手段。
