A Novel Gating Adversarial Imputation Method for High-Fidelity Restoration of Missing Electrical Disturbance Data

The ongoing evolution of cyber-physical power systems renders them susceptible to frequent and multifaceted electrical disturbances. Critically, missingness resulting from cascading cyber-physical failures severely impedes the ability to accurately monitor and diagnose these electrical disturbances. To address this serious challenge, this paper proposes a novel gating adversarial imputation (GAI) framework specially tailored for the high-fidelity restoration of missing electrical disturbance data. The proposed GAI efficiently introduces the latest gating mechanism into a stability-improved adversarial imputation process, enabling robust feature representation while maintaining high imputation accuracy. To validate its efficacy, a synthetic dataset encompassing 15 distinct disturbance types is constructed based on precise mathematical equations and standard missingness. A comprehensive experimental evaluation demonstrates that the proposed GAI consistently outperforms five representative imputation benchmarks across all tested missing percentages. Moreover, GAI effectively preserves the original critical characteristics during data recovery, thereby enhancing accurate system monitoring and operational security.