Integrating an Analytical Risk Factor into a Neural Network Framework for Self-Protective Inverters

This paper develops a self-protective algorithm for inverters by integrating an analytical risk factor into a neural network framework to detect malicious setpoints. Inverters play an important role in distributed energy generation in the energy infrastructure, where a supervisory control structure is required for energy management and economic dispatch. In a centralized supervisory control structure, the inverters need to be in contact with aggregators, other energy generation units, or the utility operating center. The communication capability makes a grid-interactive inverter a cyber-physical device. However, the connection of inverters to a communication network exposes the inverters to active attackers who can interfere with the control infrastructure and send malicious setpoints to the local controller. Such malicious setpoints can have harmful consequences, such as uncontrolled power oscillations, voltage sags and swells, equipment damage, and blackouts. This paper presents a new method to secure grid-interactive inverters against manipulated setpoints. The results demonstrate that the proposed method can significantly enhance the security of inverters by examining power setpoints using the hybrid model prior to engaging the setpoint to the local controller. The findings of this work are experimentally verified using a small-scale 208-V, 5-kVA inverter connected to a 12-kW NHR 9410 power grid emulator.