A Novel Self-Evolving Intelligent Multiagent Framework for Power System Control and Protection

In this paper, we propose a multiagent methodology (MAM) for power system monitoring and protection. The uniqueness of the proposed architecture is the ability of MAM to evolve in the wake of an attack of malicious intent by mutation, thus always monitoring power system buses remotely. The architecture interacts with the mutated agents using a voting methodology, thus alleviating the effect of agent malfunction in the presence of an attack. In addition, this proposed architecture enables inter-agent communication, thus developing a collaborative framework and increasing the degree of fault tolerance. This method is tested on a two-area five-machine eight-bus power network for abnormal condition detection and generator bus isolation, as well as for restoring power to the area that is affected by the bus isolation. Simulation studies indicate that the proposed methodology is not only capable of detecting the power grid attack but also capable of continuously monitoring and isolating the generator bus even in the presence of sustained attack as the agents are regenerated.