The tendency toward electrification of marine vessels has led the evolution of the all electric ship (AES). The harsh operating environment of the AES makes the shipboard power system (SPS) vulnerable, so a powerful monitoring system for fault detection and localization (FDL) is essential for safe navigation. We propose a machine learning based FDL method for monitoring the system condition with the problem of imbalanced training dataset. The generative adversarial network (GAN) comprising of deep convolutional neural networks was employed to synthesize numerous valid samples. Feature extraction and selection technologies were applied to time-series signals to reduce features for monitor training. Finally, the random forest (RF) model was trained using the augmented training dataset, combining real data with generated ones by GAN, to verify the capability of the GAN-RF based FDL method. Both real training and testing data were collected from the SPS model established in PSCAD/EMTDC. The results demonstrated that the monitor could distinguish different conditions in real-time with the help of hardware implementation on the FPGA and a 99% classification accuracy was achieved with excellent anti-noise capability.

中文翻译：

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基于深度学习的全电船MVDC电力系统基于硬件的实时故障检测和定位

船舶电气化的趋势引领了全电动船（AES）的发展。AES苛刻的操作环境使船上电力系统（SPS）易受攻击，因此，用于故障检测和定位（FDL）的强大监视系统对于安全导航至关重要。我们提出了一种基于机器学习的FDL方法，用于监视系统状态与训练数据集不平衡的问题。由深度卷积神经网络组成的生成对抗网络（GAN）用于合成大量有效样本。特征提取和选择技术已应用于时间序列信号，以减少用于监控训练的特征。最后，使用增强型训练数据集对随机森林（RF）模型进行了训练，将真实数据与GAN生成的数据相结合，验证基于GAN-RF的FDL方法的功能。实际的培训和测试数据都是从PSCAD / EMTDC中建立的SPS模型中收集的。结果表明，该监控器可以借助FPGA上的硬件实现实时区分不同条件，并且具有出色的抗噪声能力，可实现99％的分类精度。