In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and industry FDI attacks can affect the internal state estimation processcritical for smart grid monitoring and controlthus being able to bypass conventional Bad Data Detection BDD methods Hence prompt detection and precise localization of FDI attacks is becomming of paramount importance to ensure smart grids security and safety Several papers recently started to study and analyze this topic from different perspectives and address existing challenges Datadriven techniques and mathematical modelings are the major ingredients of the proposed approaches The primary objective of this work is to provide a systematic review and insights into FDI attacks joint detection and localization approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localization aspects For this purpose we select and inspect more than forty major research contributions while conducting a detailed analysis of their methodology and objectives in relation to the FDI attacks detection and localization We provide our key findings of the identified papers according to different criteria such as employed FDI attacks localization techniques utilized evaluation scenarios investigated FDI attack types application scenarios adopted methodologies and the use of additional data Finally we discuss open issues and future research directions

翻译：近年来，针对智能电网的网络攻击日益频繁。在可对智能电网发起的众多恶意活动中，虚假数据注入（FDI）攻击已引起学术界和工业界的极大关注。FDI攻击能够影响智能电网监测与控制所依赖的关键内部状态估计过程，从而绕过常规的不良数据检测（BDD）方法。因此，及时检测并精确定位FDI攻击对于确保智能电网的安全与可靠性变得至关重要。近期，众多论文从不同角度研究并分析这一课题，并应对现有挑战。数据驱动技术与数学建模是所提方法的主要组成部分。考虑到其他综述主要集中于检测方面而未详细涵盖定位方面，本工作的主要目标是系统性地综述并深入探讨FDI攻击的联合检测与定位方法。为此，我们筛选并审查了四十余项主要研究成果，详细分析了它们与FDI攻击检测和定位相关的方法与目标。我们根据不同标准（如所采用的FDI攻击定位技术、使用的评估场景、研究的FDI攻击类型、应用场景、采用的方法论以及附加数据的使用）对所识别的论文提供了关键发现。最后，我们讨论了开放性问题与未来研究方向。