Data-Driven Based Corrective Actions and Detection of Intrusive Sensors Behavior in Power Electronic Dominated Grids

This paper presents a Bayesian regularization based artificial neural network (BRANN) corrective actions for resilient operation of power electronic dominated grids (PEDG) with compromised sensors of grid-forming (GFM) or grid-following (GFL) inverters. GFM inverters play a significant role in governing the voltage and frequency stability of upcoming PEDG. Thus, spoofing the GFM inverter sensors' readings for their feedback control systems can jeopardize the PEDG resilient operation. This paper presents a framework that corrects the spoofed sensors' readings in the primary controller of the GFM inverter in PEDG. A neural network is trained via Bayesian regularization to realize the proposed corrective actions for sensors readings and consequently enhancing the resiliency of PEDG under sensor attacks. The trained neural network is integrated with GFM inverter's primary control loop to provide real-time corrective action whenever intrusive sensor behavior is detected. Summary of the proposed framework is provided in this paper. The simulated case studies are provided in the paper to validate the theoretical expectations.