  The emergence of novel the dummy data injection attack (DDIA) poses a severe
threat to the secure and stable operation of power systems. These attacks are
particularly perilous due to the minimal Euclidean spatial separation between
the injected malicious data and legitimate data, rendering their precise
detection challenging using conventional distance-based methods. Furthermore,
existing research predominantly focuses on various machine learning techniques,
often analyzing the temporal data sequences post-attack or relying solely on
Euclidean spatial characteristics. Unfortunately, this approach tends to
overlook the inherent topological correlations within the non-Euclidean spatial
attributes of power grid data, consequently leading to diminished accuracy in
attack localization. To address this issue, this study takes a comprehensive
approach. Initially, it examines the underlying principles of these new DDIAs
on power systems. Here, an intricate mathematical model of the DDIA is
designed, accounting for incomplete topological knowledge and alternating
current (AC) state estimation from an attacker's perspective. Subsequently, by
integrating a priori knowledge of grid topology and considering the temporal
correlations within measurement data and the topology-dependent attributes of
the power grid, this study introduces temporal and spatial attention matrices.
These matrices adaptively capture the spatio-temporal correlations within the
attacks. Leveraging gated stacked causal convolution and graph wavelet sparse
convolution, the study jointly extracts spatio-temporal DDIA features. Finally,
the research proposes a DDIA localization method based on spatio-temporal graph
neural networks. The accuracy and effectiveness of the DDIA model are
rigorously demonstrated through comprehensive analytical cases.
