Predicting and classifying faults in electricity networks is crucial for uninterrupted provision and keeping maintenance costs at a minimum. Thanks to the advancements in the field provided by the smart grid, several data-driven approaches have been proposed in the literature to tackle fault prediction tasks. Implementing these systems brought several improvements, such as optimal energy consumption and quick restoration. Thus, they have become an essential component of the smart grid. However, the robustness and security of these systems against adversarial attacks have not yet been extensively investigated. These attacks can impair the whole grid and cause additional damage to the infrastructure, deceiving fault detection systems and disrupting restoration. In this paper, we present FaultGuard, the first framework for fault type and zone classification resilient to adversarial attacks. To ensure the security of our system, we employ an Anomaly Detection System (ADS) leveraging a novel Generative Adversarial Network training layer to identify attacks. Furthermore, we propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. We comprehensively evaluate the framework's performance against various adversarial attacks using the IEEE13-AdvAttack dataset, which constitutes the state-of-the-art for resilient fault prediction benchmarking. Our model outclasses the state-of-the-art even without considering adversaries, with an accuracy of up to 0.958. Furthermore, our ADS shows attack detection capabilities with an accuracy of up to 1.000. Finally, we demonstrate how our novel training layers drastically increase performances across the whole framework, with a mean increase of 154% in ADS accuracy and 118% in model accuracy.

翻译：电力网络中的故障预测与分类对保障持续供电及最小化维护成本至关重要。借助智能电网领域的进步，已有多种数据驱动方法被提出用于处理故障预测任务。这些系统的部署带来了诸多改善，例如优化能源消耗与快速恢复，因此已成为智能电网的关键组成部分。然而，这些系统在面对对抗性攻击时的鲁棒性与安全性尚未得到充分研究。此类攻击可能欺骗故障检测系统、破坏电力恢复过程，从而损害整个电网并造成基础设施的额外损伤。本文提出了FaultGuard——首个具备对抗攻击弹性的故障类型与区域分类框架。为确保系统安全性，我们采用了一种利用新型生成对抗网络训练层进行攻击识别的异常检测系统。此外，我们提出了一种低复杂度故障预测模型及在线对抗训练技术以增强鲁棒性。通过使用当前弹性故障预测基准测试中最先进的IEEE13-AdvAttack数据集，我们对框架在多种对抗攻击下的性能进行了全面评估。即使在未考虑对抗攻击的情况下，我们的模型仍以高达0.958的准确率超越现有最优方法。同时，我们的异常检测系统展现出高达1.000的攻击检测准确率。最后，我们证明了新型训练层如何显著提升整个框架的性能，其中异常检测系统准确率平均提升154%，模型准确率平均提升118%。