Online Bad Data Detection in Compressed Sensing-Based Distribution System State Estimation

This letter introduces a novel approach for online bad data detection in distribution system state estimation (DSSE) by integrating compressive sensing (CS) with a modified largest normalized residual (LNR)-based detector. To the best of the authors’ knowledge, this is the first work to develop a bad data detection method specifically for CS-based DSSE in unobservable distribution networks. The paper derives a closed-form solution for the compressed sensing problem, which is then used to quantify the error statistics in CS-based DSSE estimates. These statistics enable the design of a modified LNR-based detector using eigen decomposition, significantly improving anomaly detection. Extensive simulations on IEEE 37-bus and 123-bus unbalanced distribution systems demonstrate that the proposed method consistently outperforms the conventional LNR approach and neural network based technique, achieving superior detection rates with low computation effort even with a limited number of measurements. This robust approach effectively detects data anomalies from both random errors and cyber-attacks, making it highly suitable for practical DSSE applications.