Online Defense of Data Spoofing for Renewable Energy-Based Active Distribution Networks

作者：Bo Wang, Guangda Xu, Zhaohua Wang, Guowu Li, Jian Zhang · 发表于：IEEE Sensors Journal · 年份：2025 · DOI：10.1109/jsen.2025.3600654 · 研究领域：Smart Grid Security and Resilience、Power Line Communications and Noise、Smart Grid Energy Management

The diversity of facilities and limitations of communication protocols in active distribution networks (ADNs) make measurements vulnerable to secretive cyber-physical spoofing (CPS) attacks, such as the false data injection attack, impacting applications such as load control, power dispatch, and market planning. To address this critical issue, a novel detection framework is proposed for cyber-physical spoofing defense, aiming to authenticate data and mitigate the adverse effects of attacks. First, a dynamic local outlier factor is proposed to filter the abnormal measurements and improve the data quality. Then, an enhanced S-transform is developed to extract the unique fingerprints by mapping the measurements in both the time and frequency domains. Next, a lightweight and highly efficient model is implemented to realize real-time CPS detection after feature extraction. Utilizing the real-world measurements of smart meter from ADNs, multiple comparative experimental results reveal that the proposed detection framework the proposed detection framework achieves a detection accuracy of 96.33%, surpassing the recent advanced method by 18.26% in accuracy. Moreover, the average detection time is 55.62 ms, achieving superior real-time CPS defense.