Abstract:

With the growth of renewable energy, the application of distributed generation (DG) systems has expanded, offering advantages in energy efficiency and environmental sustainability. However, the decentralized and complex nature of DG systems makes them vulnerable to False Data Injection Attacks (FDIAs), which tamper with real-time measurements, disrupt state estimation, and compromise scheduling decisions. These attacks can lead to instability, operational errors, and even power outages. To enhance system security, this paper proposed DACDiff, a defense method against FDIAs in DG control. Based on an improved conditional diffusion model, DACDiff employed DACformer as a denoising network with a dual-attention mechanism to capture dependencies in time series data. Through upsampling and a multi-scale design, the model preserved data features, generating realistic replacements for compromised data to maintain state estimation and control accuracy. Simulation results on power system datasets show that DACDiff achieves high data generation quality and strong defense capability, effectively restoring DG control systems and enhancing security and stability.

Key words: distributed generation dispatch and control, false data injection attacks, active defence, diffusion model, dual attention net