A Reliable Phasor Measurement Framework Using Artificial Intelligence to Prevent False Data Manipulation

Abstract

This work introduces a novel intelligent security approach to improve the reliability of smart grid operations by protecting measurement data produced by the Phasor Measuring Unit. The increasing demand for power distribution requires continuous and accurate monitoring, which is dependent on reliable measurement data synchronized with strict timing constraints. To address the risks related to inaccurate measurements, unauthorized modification, and malicious disruption, this study develops a self-adaptive security analysis process using an artificial intelligence-based learning strategy. The system evaluates multiple operational factors and converts them into dynamic multivariate states to support accurate decision making. The proposed method integrates a learning mechanism based on nearest neighbor classification to detect abnormal measurement behavior and prevent false data events before operational failure occurs. An experimental evaluation in four scenarios demonstrates that the approach improves security and reliability metrics compared to existing solutions. The results confirm that the proposed method provides an effective pathway for reliable measurement data and resilient smart grid operation.