Lee Bohyung, Kim Yeseul, Lee Hyunyong, Kang Changmook
Department of Electrical Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Department of Electrical Engineering, Incheon National University, Incheon 22012, Republic of Korea.
Sensors (Basel). 2025 Jan 24;25(3):693. doi: 10.3390/s25030693.
With the growing penetration of renewable energy sources, ensuring the stability and reliability of Medium-Voltage Direct Current (MVDC) systems has become more critical than ever. A single fault in MVDC systems can cause significant disturbances, necessitating rapid and precise diagnostics to prevent equipment damage and maintain continuous power supply. In this work, we present a Bidirectional Gated Recurrent Unit (Bi-GRU) model that both classifies and locates MVDC faults. By capturing the temporal behavior of voltage signals, the Bi-GRU framework surpasses traditional algorithms such as Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Furthermore, the proposed approach addresses multiple fault scenarios including PTP (Pole-to-Pole), PPTG (Positive Pole-to-Ground), and NPTG (Negative Pole-to-Ground) while preserving real-time diagnostic capabilities. In extensive tests, our model achieves an overall accuracy of 95.54% and an average fault detection time below 1.3 ms, meeting real-world operational requirements. To assess robustness, sensor noise was artificially introduced to emulate realistic conditions. Despite these challenging inputs, our method consistently maintained high diagnostic accuracy, confirming its practicality and reliability. Consequently, the proposed scheme demonstrates a significant contribution toward improving the safety and dependability of MVDC systems, even under noisy conditions.
随着可再生能源渗透率的不断提高，确保中压直流（MVDC）系统的稳定性和可靠性变得比以往任何时候都更加关键。MVDC系统中的单个故障可能会导致严重干扰，因此需要快速而精确的诊断来防止设备损坏并维持持续供电。在这项工作中，我们提出了一种双向门控循环单元（Bi-GRU）模型，该模型既能对MVDC故障进行分类又能定位故障。通过捕捉电压信号的时间行为，Bi-GRU框架超越了传统算法，如卷积神经网络（CNN）和双向长短期记忆（Bi-LSTM）网络。此外，所提出的方法解决了包括极间（PTP）、正极接地（PPTG）和负极接地（NPTG）在内的多种故障场景，同时保持了实时诊断能力。在广泛的测试中，我们的模型实现了95.54%的总体准确率和低于1.3毫秒的平均故障检测时间，满足了实际运行要求。为了评估鲁棒性，人为引入了传感器噪声以模拟实际情况。尽管存在这些具有挑战性的输入，我们的方法始终保持高诊断准确率，证实了其实用性和可靠性。因此，所提出的方案对提高MVDC系统的安全性和可靠性做出了重大贡献，即使在有噪声的条件下也是如此。
