Neuromorphic Computing in Autonomous Distributed Grid Control

Due to the distributed nature of the electrical grid, intelligent, timely control of critical components such as volt-age regulators, capacitor banks and switches in a highly dynamic environment is extremely challenging. Modern day localized controllers in a distribution system such as remote ter-minal units (RTUs) and programmable logic units (PLCs) are often housed in a substation with multiple connections to several critical controls and are thus good candidates for hosting advanced localized control via deep reinforcement learning (DRL) or evolutionary learning. Additionally, it is feasible to greatly reduce the computational throughput and provide fine-grained control measurement and control using a spiking neural network (SNN) neural processing unit (NPU) architecture. In this paper we explore the use of a hybrid grid control algorithm composed of an optimized genetic algorithm (GA) and SNN controller which acquires optimal distribution system voltage control policies while a system is experiencing a cyber adver-sary's injected control noise. We discuss the computational advantages of the GA-SNN hybrid algorithm in the fine-grained autonomous control of a grid's bus voltages using a commercially available neuromorphic system on chip system (NSoC) with a mean rated power consumption of 1 mW and observed mean prediction time of 4 msecs per inference.