In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks
翻译:在智能电网中,故障检测任务可能对经济和关键任务产生高影响。在近年来,许多智能电网应用,如缺陷检测和负荷预测,已经采用了数据驱动方法。本研究的目的是探讨与智能电网场景中机器学习(ML)应用的安全性相关的挑战。实际上,这些数据驱动算法的健壮性和安全性尚未在所有电力网络应用中得到广泛研究。我们首先证明了在智能电网中使用的深度神经网络方法容易受到对抗扰动的影响。然后,我们阐明了故障定位和类型分类研究如何阐明目前智能电网中ML算法的各种对抗攻击弱点。