With the recent advancement in smart grid technology, real-time monitoring of grid is utmost essential. State estimation-based solutions provide a critical tool in monitoring and control of smart grids. Recently there has been an increased focus on false data injection attacks which can circumvent the traditional statistical bad data detection algorithm. Most of the research methodologies focus on the presence of FDIA in measurement set, whereas their exact locations remain unknown. To cater this issue, this paper proposes a deep learning architecture for detection of the exact locations of data intrusions in real-time. This deep learning model in association with traditional bad data detection algorithms is capable of detecting both structured as well as unstructured false data injection attacks. The deep learning architecture is not dependent on statistical assumptions of the measurements, it emphasizes on the inconsistency and co-occurrence dependency of potential attacks in measurement set, thus acting as a multilabel classifier. Such kind of architecture remains model free without any prior statistical assumptions. Extensive research work on IEEE test-bench shows that this scheme is capable of identifying the locations for intrusion under varying noise scenarios. Such kind of an approach shows potential results also in detection of presence of falsified data.

随着智能电网技术的最新发展，对电网的实时监控至关重要。基于状态估计的解决方案提供了监视和控制智能电网的关键工具。最近，人们越来越关注错误数据注入攻击，这种攻击可以绕开传统的统计错误数据检测算法。大多数研究方法都集中在测量集中存在FDIA，而它们的确切位置仍然未知。为了解决这个问题，本文提出了一种深度学习架构，用于实时检测数据入侵的确切位置。结合传统的不良数据检测算法的这种深度学习模型能够检测结构化和非结构化的虚假数据注入攻击。深度学习架构不依赖于度量的统计假设，它强调度量集中潜在攻击的不一致性和共现依赖性，因此充当多标签分类器。这种架构无需任何先前的统计假设即可保持模型自由。在IEEE测试平台上的大量研究表明，该方案能够确定在各种噪声情况下的入侵位置。这种方法在检测伪造数据时也显示出潜在的结果。在IEEE测试平台上的大量研究表明，该方案能够确定在各种噪声情况下的入侵位置。这种方法在检测伪造数据时也显示出潜在的结果。在IEEE测试平台上的大量研究表明，该方案能够确定在各种噪声情况下的入侵位置。这种方法在检测伪造数据时也显示出潜在的结果。